Citation
Developing Educational Materials in Bioprocessing Using an Ontology Database Management System

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Title:
Developing Educational Materials in Bioprocessing Using an Ontology Database Management System
Creator:
BADAL, ROHIT ( Author, Primary )
Copyright Date:
2008

Subjects

Subjects / Keywords:
Bottles ( jstor )
Databases ( jstor )
Domain ontologies ( jstor )
Educational research ( jstor )
Instructional materials ( jstor )
Java ( jstor )
Learning ( jstor )
Modeling ( jstor )
Simulations ( jstor )
Websites ( jstor )

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Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Rohit Badal. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
7/12/2007
Resource Identifier:
660161436 ( OCLC )

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Full Text












DEVELOPING EDUCATIONAL MATERIALS IN BIOPROCESSING USING AN
ONTOLOGY DATABASE MANAGEMENT SYSTEM
















By

ROHIT BADAL


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2007
































Copyright 2007

by

Rohit Badal


































To my mother












ACKNOWLEDGMENTS

I would like to sincerely thank my advisor, Dr. Howard Beck, who has helped me

by replying to many late-night and weekend e-mails and has provided ample time to me

for guidance. He taught me ontology, obj ect database, Java, and other topics relevant to

my research. I also want to thank him because I got an opportunity to know him in

person. I feel lucky and proud to be his student, because Dr. Beck is not only a good

researcher but also a good human being. I would also like to thank the director (Dr.

William Sheehan), researchers (Dr. Dave Mayzyck, Dr. Art Teixeira, and Dr. Dave

Chynowyth land graduate students (Patrick, Beau, and others) of ES CSTC for their

valuable help.

Secondly, I would like to thank my committee members who have given

appropriate guidance and their valuable time. Dr. Fedro Zazueta introduced the concepts

of learning object, and I am very thankful to him. Dr. Joachim Hammer has guided me to

see the role of ontology and database in my research; so, I really appreciate his

comments. Dr. Art Teixeira has been a great help, because he guided me in developing

additional educational simulations for showing various aspects of process. Dr. Roger

Nordstedt has given valuable advice by seeing the usability of my work from an end-user

perspective.

I would like to thank my friends (Shantanu Mishra [Golu], Jairaj Payyapalli [Paya]

,Soonho Kim, Yunchul Chris, Bruno, Shiva, and Frank Barone) at UF. Also, I enjoyed

my time at Transcendental Meditation Center; so, I would like to thank Dr. Alcine Potts

and Patricia. Krishna Lunch has been a great place to meet friends and have food, so










many thanks to them. Also, I would like to thank Dr. Jiannong Xin, Danielle, and Dr.

Petraq Papajorgji for tea and valuable advice.

Many thanks go out to my parents, Saroj Badal and R.S Badal; my brother, Rahul

Badal; my sister, Rachna Badal; our pet motu and other members for their support and

love. Lastly, I would like to thank the source of all happiness and good will.






















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S ............ ...... ._._ .............._ iv...


LI ST OF FIGURE S .............. .................... ix


LI ST OF ACRONYMS .............. .................... xi


AB S TRAC T ......_ ................. ..........._..._ xiii..


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Statement of Problem ................ ............ ........ ......... ........ .........1

Duplication of Efforts ............... ... .. .......... ... .......... .............
Unstructured content of the educational material ................. ............... ....4
Lack of separation between presentation and content ................. .................4
Lack of Knowledge Reuse between Research and Educational Materials ............5
Appropriate Format for Presenting Educational Material .................. ...............5
Specific Obj ectives .....__. ................ .......__. .........6
Approach .........._.... .. .... ........ .....___ .. ..... ... ........
Other Related Proj ects for Managing Research Information ................. ................. 9
E-Science ................... .... ........ .... .. ...... ............
Austrian Research Information System Proj ect ................. ......__. ............10
Dissertation Layout ................. ...............11........... ....

2 CONTENT MANAGEMENT APPROACH FOR DEVELOPING
EDUCATIONAL MATERIAL ................ ...............13.................


Introducti on ................. ...............13.................
Domains Studied................ ...............14
Solid W aste Treatment ................. ...............14................
Wastewater Treatment ................. ..... .... ...... .... .......... ..... ..........1
Rational for Structuring and Reusing Information of ES CSTC .............. .... ........._..16
Ontology .............. ...............17....
Literature Review .............. .. ...............17...

Computer-Based Instruction................ .............1
Content Management Systems .............. ...............19....












Learning Objects .................... ............... .................1
Shareable Content Obj ect Reference Model (SCORM) ................. ................ .21
Efforts in Managing and Reusing Content Using Ontologies ................... ..........21
Generating Presentations from Content............... ...............24
Content Management Approach ...._ .................. ...... ......... ......... 2
Components of a Content Management System .............. ....................2
Ontology .............. ... .. ......... ........... .............2
Methodology of developing an ontology .............. ..... ............... 2
Tools for creating an ontology .............. ...............29....
Database System ................. ...............31.................
Presentation Generator .............. ...............32....
Java server page technique ................. ...............32................
Java applet technology .............. ...............34....
Generated Educational Materials .............. ...............34....


3 EDUCATIONAL SIMULATION: AN APPROACH FOR PRESENTING
DYNAMIC INFORMATION OF A PROCES S ................ .......... ................3 7


Introducti on ................. ...............37.................
Virtual Lab ............... .... ....... .......... .. ........ .... .. ...... ............3
Literature Review on Virtual Labs and Educational Simulations .............. ................38
Methodology of Creating Educational Simulations .............. ....................3
Ontology Development .............. ...............39....
Development of Java Cl asses ................. ........................ ..............40
Development of Educational Simulation ......___ ..... ... .__ ..........__.....4
R e sults................ ........ .......... .. ...__ .. ...........4
Simulation for Solid Waste Treatment ....__ ......_____ ...... ......_........42

Bioprocess lab (BMP lab) .............. ...............42....
SEBAC simulation .............. ...............43...
Simulation for Wastewater Treatment............... ...............4
Evaluation of Simulation .............. ... ...__ .....__ ............4
Evaluation of Solid Waste Treatment Simulation ......____ ...........__..........47
Evaluation of Wastewater Treatment Simulation ................_ ........... ..........50
Conclusions............... ..............5


4 AN ONTOLOGY-BASED APPROACH TO MATHEMATICAL MODELING ....53


Introducti on ....._ ._................. ...............53.......
Literature Review .............. ......... .............5
Problems in Developing Simulations ................ ... ............. ........5
Possible Solution for Communicating Knowledge of a Model ................... ........55
Applications of Ontologies in Simulation .............. ...............56....
M odel base .............. ...............56....

System structure ................ ... .......... ..........5
Representing Equations and Symbols in a Model ................... ...............5
Reasoning .............. .......... ..... ..............6
Generating and Integrating Documentation and Training Resources.........................62












How to Build an Ontology-Based Simulation: Bioprocessing Example ................... .62
Collection of Relevant Documents ................. ...............63................
Define Model in Terms of Elements ................ ................ ................63
Identifying Classes, Individuals and Properties .............. .....................6
Define Equations ...................... ... ....... ..........6
Enter the Initial Values of State Variables .............. ...... ...............69
Generating Program Code for Implementing the Simulation ................... ...........69
Execution of Simulation .......................__ ...............70......
Conclusions............... ..............7


5 CONCLUSIONS, CONTRIBUTIONS, AND FUTURE DIRECTIONS .................72

Conclusions............... .... ..................7
Documenting Research Information...................... ..................7
Methodology for Generating Educational Material by Reusing Information .....72
Presenting Dynamic Information of a Lab Exercise as Educational Simulation 73
Representing Knowledge of a Mathematical Model by Ontology ................... ...73
Contributions .............. ...............74....
Future Directions ............... .. ..... ...............7
Ontology-Based Instruction Design .............. ...............74....
Ontology Reasoning ................. .. ........... ........... .............7
Development of Tools for Developing Online Lesson................. ...............7


APPENDIX


A EVALUATION FORM OF BMP SIMULATION. ......____ ........__ ..............76


B EVALUATION FORM OF MAPR SIMULATION ..........._.... .........._............79


REFERENCES .............. ...............82....


BIOGRAPHICAL SKETCH .............. ...............90....



















LIST OF FIGURES


Figure pg

2-1 Components of a content management system used for developing educational
m material s .............. ...............27....

2-2 Schematic of Web Taxonomy showing a portion of the Biochemical Methane
Potential (BM P) ontology .............. ...............30....

2-3 Schematic of Obj ect Editor showing a list of equipment and reagents used in the
BMP lab and the relationship between them ................. ............... ......... ...31

2-4 Web site generated by the content management approach ................. ................. .3 5

3-1 Interface for the BMP laboratory for determining biodegradability of a sample in
m ovie m ode ................. ...............41.......... .....

3-2 Interface for the BMP laboratory for determining biodegradability of a sample in
interactive mode .............. ...............42....

3-3 Interface of the Sequential Batch Anaerobic Composting (SEBAC) process for
treating solid waste in movie mode. .............. ...............44....

3-4 Interface of the SEBAC process for treating solid waste in interactive mode.........44

3-5 Interface of the SEBAC process with three reactors for treating solid waste in
movie mode (ES CSTC Education and Outreach Website, 2006) .........................45

3-6 Interface of the SEBAC process with a single reactor for showing the process of
clogging (ES CSTC Education and Outreach Website, 2006) .............. ..............46

3-7 Interface of the Magnetic Agitated Photocatalytic Recator (MAPR) laboratory
for treating a sample of wastewater in movie mode ..........._.... ..._. ............47

3-8 Interface of MAPR laboratory for treating a sample of wastewater in interactive
m odel ................. ...............48..._..._ ......

3-9 Overall subj ective experience of the students by two teaching methodologies for
the BMP lab evaluation ........._...... ...............49..__._. .....

4-1 Representation of equation as a tree structure ...._._._._ .... ... ..... ........_.......60










4-2 Conceptual model of the SEBAC system .............. ...............64....

4-3 SimulationEditor diagram for SEBAC process showing elements of SEBAC
simulation and showing various transformations that occur during the process......65

4-4 Interface of EquationEditor to input the concepts in a particular element of the
simulation ................ ...............66.................

4-5 Ontology for different forms of nitrogen ................ ................. ..............67

4-6 Interface of the EquationEditor for entering equation ................. ........_ .......68

4-7 Interface for presenting results of SEBAC simulation using animation ..................70









LIST OF ACRONYMS


ADL

AICC

ASP

AURIS-MM

BMP

CERIF

CMS

DARPA

ES CSTC

HTML

IBM

IDE

IEEE

JSP

LCMS

LMS

LO

LOs

MAPR

MATLAB

M-OBLIGE

OWL


Advanced Distributed Learning

Aviation Industry Computer Based Training Committee

Active Server Pages

Austrian Research Information System Multimedia Extended

Biochemical Methane Potential

Common European Research Information Format

Content Management Systems

Defense Advanced Research Proj ects Agency

Environmental Systems Commercial Space Technology Center

HyperText Markup Language

International Business Machines

Integrated Development Environment

Institute of Electrical and Electronics Engineers

Java Server Pages

Learning Content Management Systems

Learning Management Systems

Learning Obj ect

Learning Obj ects

Magnetic Agitated Photocatalytic Reactor

Matrix Laboratory

Multitutor Ontology-Based Learning Environment

Web Ontology Language









RDF Resource Description Framework

RMI Remote Method Invocation

SCORM Shareable Content Obj ect Reference Model

SEBAC Sequential Batch Anaerobic Composting

TRP Technology Reinvestment Proj ect

UV ultraviolet

XML Extensible Markup Language

XML FO XML Formatting Obj ects

XSL Extensible Stylesheet Language

XSLT XSL Transformations









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

DEVELOPING EDUCATIONAL MATERIALS IN BIOPROCESSING USING AN
ONTOLOGY DATABASE MANAGEMENT SYSTEM

By


Rohit Badal

May 2007

Chair: Howard Beck
Major Department: Agricultural and Biological Engineering

An ontology database management system was utilized for developing an

educational outreach program at UF/ES CSTC ( The University of Florida' s

Environmental Systems Commercial Space Technology Center) with the objective of

disseminating research information generated at ES CSTC. The purpose of educational

outreach of a research center is to educate the targeted audience about various aspects of

research conducted at the center. Information technology can facilitate educational

outreach by supporting and enhancing various functionalities for success of the

educational outreach program.

A database approach to managing and developing educational and training

materials websitess, simulations) is presented that utilizes ontologies and obj ect database

treatment systems to better manage educational resources and enhance learning of waste

treatment processes. Examples in the area of solid waste treatment and wastewater

treatment are presented. An ontology is used to define and organize the concepts in the

domain, in this case concepts involving the biology, chemistry, and physics of waste

treatment. A database, rather than files, is used to store and distribute concept objects.









Web-based data visualization tools are used by instructors to develop and manage course

content. Obj ects can be proj ected to a number of different presentation formats,

including Web sites and printed materials. Evaluation of a 2-D simulation of a

bioprocessing experiment showed that Web-based simulation can offer many of the

experiences of hands-on laboratory exercises. The immediate advantage of this approach

is that educational programs can be more easily produced at lower cost compared with

conventional tools currently available.















CHAPTER 1
INTTRODUCTION

The educational outreach of a research center is an important aspect of

disseminating information generated by research center proj ects and helping different

audiences understand a research proj ect. The purpose of educational outreach of a

research center is to educate the targeted audience about various aspects of the research

conducted at the center. Information technology can facilitate educational outreach by

supporting and enhancing functionalities for the success of the educational outreach

program. The educational outreach program involves five important tasks:

* Identifying educational goals and obj ectives
* Generating and managing educational content that meets goals and obj ectives
* Creating educational and training material from the content
* Disseminating educational materials to different targeted audiences in a suitable
format
* Performing assessment to test the effectiveness of educational outreach program

Statement of Problem

The Environmental Systems Commercial Space Technology Center (ES CSTC) is a

commercial research center of NASA located at the University of Florida. This study

reports on the research performed to develop a methodology for creating an educational

outreach program at ES CSTC with the obj ective of disseminating ES CSTC research

information. The audience to be reached included industries interested in adopting ES

CSTC technologies as well as other researchers working in the area of waste recovery

and instructors teaching waste management courses. The methodology was developed by

applying new techniques in database management and obj ect oriented technology to









create a repository of educational resources needed to disseminate research results and to

provide an alternative approach for developing educational materials (Badal et al.,

2004a). Various challenges are involved in the development of educational materials,

and these challenges are described in this section. These problems include the following:

* Duplication of efforts
* Lack of knowledge reuse between research and educational materials
* Appropriate format for presenting educational material

Duplication of Efforts

A research center generates a variety of information in various forms such as

websites, research papers, reports, simulations, and animations. For example, NASA

maintains a website for high school students where the students can find information

about a space mission. Conventional tools such as PowerPoint (PowerPoint Website,

2006), Adobe Acrobat (Adobe Website, 2006 ), Macromedia Flash (Adobe Website,

2006 ), and HTML development tools (Dream Weaver Website, 2006) are presently used

for developing educational resources.

Substantial effort and coordination are typically required for creating educational and

training materials. Several methodologies have been developed for creating educational

and training materials, and most of them are based on the Analysis, Design,

Development, Implementation, and Evaluation (ADDIE) model (McGriff, 2000). The

ADDIE model involves five steps:

* Analysis: The gap between desired learning outcome and the existing knowledge
and skills of an audience is determined.
* Design: The specific learning objectives, content, assessment tools, and exercises
are documented.
* Development: The learning materials are created.
* Implementation: The learning materials are distributed to a specific audience.
* Evaluation: The learning materials are evaluated by a specific audience.









Conventionally, a subj ect matter expert provides content and coordinates with an

instructional designer who designs lessons based on the content provided. Content refers

to the subj ect or topics covered in an educational program (Online Dictionary Website,

2006). The content is related to the message or knowledge that the user gets from the

educational resource. The information technology professional provides information

technology tools and support to the subject matter expert and instructional designer. If

instructional designer and information technology personnel are not available, the

instructors develop their own educational materials. In any case, most of the steps for

developing educational materials, as explained in the ADDIE model, must be performed

from the start because the instructors have difficulty in reusing existing course materials

(Araujo, 2004). Additionally, these steps are focused on developing a specific set of

educational materials rather than representing the course content in a generic form, like a

network of concepts, that would allow the reuse of knowledge in developing a variety of

educational materials. It is important to reuse the knowledge because it can decrease the

development cost and time while increasing the quality and accuracy of educational

materials (Fisher, 2002). The lack of knowledge reuse increases the volume of

educational materials, creating a problem for managing these materials which in turn

increases the cost related to storage and maintenance of knowledge. Reusable knowledge

can be used in developing educational materials in different contexts and for different

audiences (Arauj o, 2004). For example, the MAPR website (MAPR website, 2005) was

created for teaching the concept of "photo catalysis application of titanium dioxide for

treating wastewater". This website contains many important wastewater treatment

concepts which are presented in a specific order so a reader can develop an awareness of









the concepts. The concepts illustrated in the website can also be used in other

educational programs, but the reusability of the MAPR website is limited because of the

following reasons:

* Unstructured content of the website/educational material
* Lack of separation between presentation and content

Unstructured content of the educational material

The unstructured content is defined as "information whose intended meaning is

only loosely implied by its form and therefore requires interpretation in order to

approximate and extract its intended meaning" (Ferrucci, 2004), that means, the

organization and semantics of information are not defined explicitly. Examples of

unstructured content include Microsoft Word documents and PowerPoint presentations.

The unstructured information of the website (educational material) creates a challenge in

reusing a specific concept in other educational materials. Suppose, for example, that a

wastewater treatment company is creating a training material for their waste management

process, and that they want to teach the concept of photocatalysis (as explained in the

MAPR website), but they do not want to teach the concepts irrelevant to their process.

The unstructured information of the MAPR website makes it a challenge for the

wastewater company to search for the relevant concepts in the MAPR website and decide

if the concepts can be used in the company's educational and training material. Of

course, the content can always be manually extracted and reused, but this can be a time

consuming and tedious task, especially in large educational programs.

Lack of separation between presentation and content

The tight coupling of content and presentation creates a challenge of updating and

managing educational resources (Roure, 2003). Presentation refers to the rendering of









educational resource in a specific format like print (W3C Website, 2006). Separation of

content from presentation allows a developer to update the content while maintaining the

consistency of presentation. Similarly, the developer can change the presentation of an

educational program while maintaining the consistency of the content. This improves

maintainability and facilitates the customization of educational material.

Lack of Knowledge Reuse between Research and Educational Materials

The information used for educational or training purposes can also be used for

research purposes or vice-versa. For example, a researcher can describe a waste

management system in a project report using some concepts. These concepts can also be

used by an instructor to explain the waste management system. Research and learning

processes are interdependent, and they contribute to knowledge (Lyon, 2002). The

integration of research and educational knowledge will increase transparency in research,

improve the accessibility of research results, and enhance the development of the

educational materials with up-to-date information (Lyon, 2004). However, research

knowledge in the traditional form of reports, simulations, or mathematical models is not

effectively reused for developing educational and training material and vice-versa.

Appropriate Format for Presenting Educational Material

The presentation of educational material in a particular format is highly crucial.

Cognitive information processing and information theory has found that certain formats

for presenting information are more familiar to the users than others. The familiarity of a

format affects learning because it influences human processing capabilities (Lloyd and

Jankowski, 1999). The human visual system has the highest information processing

capability (Rohrer, 2000). Cognitive psychologists have described human processing as

conscious and pre-conscious. Processing graphic information is pre-conscious, which









frees up more conscious processing ability and allows more learning to happen.

However, excessive or confusing graphics can hinder learning.

Cognitive psychologists have found that multimedia can affect the students

learning (Mayer and Moreno, 2002). Mayer has described five principles that can be

used for teaching scientific concepts to students using multimedia. These principles are:

*Multiple representation principle: It is better to use multiple modes of presentation
(like words and pictures) than a single mode (only words or pictures).

*Contiguity principle: The corresponding words, pictures, and other multimedia
information should be presented contiguously rather than separately.

* Split-attention principle: Multimedia should be explained by auditory narration
instead of a text explanation.

*Individual differences principle: The multiple representation principle, contiguity
principle and split attention principle are more important for learners with low level
of prior knowledge than learners with high level of prior knowledge.

*Coherence principle: The multimedia explanation should not use extraneous words
and pictures.

This study involves the development of educational and training materials for

engineering processes used at ES CSTC for treating wastewater and solid waste.

Engineering processes are dynamic in nature, and it is beneficial to present these

processes in a suitable graphical format for effective understanding.

Specific Objectives

* Identify available technologies for facilitating the documentation of ES CSTC
research information, which can allow for processing and storage of ES CSTC
research information in an appropriate format so it can be shared, accessed, and
maintained easily.

* Develop a methodology for generating a variety of educational materials websitess,
animations, and reports) while avoiding duplication of effort.

* Present dynamic (simulations, process) and static (equipment details) information
in a suitable format to a variety of audiences (high school students, researchers or
management professional in the industry).










*Investigate a better method of representing knowledge of a mathematical model to
allow the use of knowledge for various purposes including the development of
educational materials.

Approach

This study has investigated an approach of developing an educational outreach

program utilizing information technology tools with an obj ective of reusing and

presenting the information explicitly, that is, representing information in a structured

format such as an ontology. An ontology, an approach to knowledge use/reuse and

knowledge sharing (Beck, 2003a), allows the information to be represented as a network

of concepts. For example, the details of a lab exercise can be represented as a network of

concepts like equipment (bottles, pipes, valves), chemicals, and samples used in the

experiment rather than a Microsoft Word document. The ontology can be used for

assisting in communication between people, attaining interoperability among computer

systems, and improving the quality of engineering software systems.

A content management system (CMS) is used for developing educational materials.

A CMS is a database management system used for storing content which includes not

only media such as text, images, animations, sounds, and videos, but also concepts in the

form of individual words and phrases, rules, and even mathematical equations (Beck,

2003a). The CMS stores content as ontology. Compared to conventional ways that focus

on developing educational material in a specific format (PowerPoint, Flash, Microsoft

Word etc.), this approach allows for a better method to organize resources, assist in

search and retrieval, and generally promote greater reusability and sharing of content.

The CMS also has the ability of automatically generating presentations from a database

through a process in which the elements of database obj ects are mapped to a particular

presentation format such as HTML, print, Flash, Java Applet and others. The CMS was









used to generate educational simulations rendered as Java Applets, and Web pages

rendered by using Java Server page (JSP) technology.

Educational simulations were used for describing engineering processes. These

simulations are run in a virtual environment allowing students to operate or manipulate

the equipment as well as the simulation process itself. Instructors can show a lab in the

form of an animation for explaining various concepts. The students can also change the

process parameter to study the behavior of the system. One of the objectives of this

proj ect is to present information in a suitable format. The interactivity of a simulation

increases student' s learning efficiency (Mclean and Riddick, 2004). Another advantage

of using simulation is that the student can access and operate the process anytime and

anywhere. Educational simulations have been used for explaining processes (Navarro

and Hoek, 2005), so various simulations were created for explaining waste treatment

concepts used in three proj ects in the area of solid waste and wastewater at ES CSTC

(Badal et al., 2006).

The static information (for example, geometrical orientation of equipment) was

stored in the database and rendered as a webpage. The information rendered as a

webpage has links to other relevant information based on the data modeling or the

structure of the ontology. The structure of the ontology of projects at ES CSTC will help

users to browse project specific knowledge and access relevant information. Any change

in the data model or ontology will automatically update the webpage. The static

information in the form of reports can also be generated using Extensible Stylesheet

Technology (XSL).









Other Related Projects for Managing Research Information

Several efforts are taking place for enhancing the use of information technology in

managing research information. These are described in this section.

E-Science

One of the efforts is taking place in the United Kingdom, where E-Science Institute

is trying to support and enhance the scientific process using information technology

(Roure, 2003). The aim of the E-Science initiative is to allow sharing of resources

among individuals and institutions in a flexible, secure, and coordinated manner. E-

Science refers to the activities performed by a scientific community in a distributed

environment using the Internet. These activities require access to computing resources

for data collection, data analysis, simulation, data visualization, and other relevant

information (procedure, standard) used by researchers in conducting experiments.

The E-Science proj ect has three layers: data/computation layer, information layer,

and knowledge layer. The computation layer deals with the task of collecting data

(experimental and simulation) and allocating resources for collecting data This layer

involves distributed computing systems. The information layer deals with the task of

representing, storing, accessing, sharing, and maintaining information. The knowledge

layer deals with the process of acquiring, using, retrieving, publishing, and maintaining

knowledge. This study shares common goals with the E-Science proj ect with respect to

information and knowledge layer. However there is a significant difference in the scale

of this study the presented work and the E-Science project. This study is conducted at

the level of a single research center while the E-Science proj ect is conducted at the level

of a country (Britain) with the budget of 250 million pounds and has sponsored 100

proj ects. The content used in E-Science is manually annotated using Extensible Markup










Language (XML) or Resource Description Framework (RDF) while the content used in

this study is self annotated because it is stored as an ontology in an ontology database

management system. The large scale of the E-Science project poses a challenge for

structuring the content as ontology. On the other hand, the relatively unstructured nature

of the E-Science proj ect content results in reduced ability to understand and reuse the

content.

Austrian Research Information System Project

The Austrian Research Information System Multimedia Extended (AURIS-MM)

proj ect involves the development of a semantic web application for accessing research

information in Austria (AURIS-MM Website, 2002). The present Web technology is

designed for humans to read the content while semantic web, an extension of the current

Web, is envisioned to bring structure to its content so the content can be processed

automatically by various programs to perform useful tasks (Lee et al., 2001).

Researchers need a variety of information, so there should be a mechanism by which they

can get the relevant information for doing a particular task. The proposed solution of the

AURIS-MM project is the creation of RDF ontologies. This study and the AURIS-MM

proj ect share a common objective of managing research information so it can be shared

and readily searchable and available among researchers. However, the difference is that

the AURIS-MM proj ect has used the Common European Research Information Format

(CERIF-2000) metadata (CERIF-2000 Website, 2002) for describing the research

information, while this study has developed an ontology ofES CSTC research

information for describing the ES CSTC research projects. The ontology of ES CSTC

research information was able to capture the knowledge of research proj ects so the

proj ects can be shared and searched from the level of vocabulary used by researchers.









The initial development of an ontology was a time consuming activity. However, the

ontologies can be reused which can decrease the development time in the future. On the

other hand, the time required to enter the metadata information for AURIS-MM proj ect is

relatively less but the information can be searched only from the level of metadata

terminology used in CERIF-2000 and not from the level of natural vocabularies used by

researchers.

Dissertation Layout


The literature review for this study is further explored in chapters 2, 3, and 4.

Chapter 2 describes the ontology as a technology for documenting ES CSTC research

information (obj ective 1), followed by the methodology for generating a variety of

educational material (obj ective 2).

Chapter 3 describes the approach for representing dynamic information using

educational simulations (objective 3). Chapter 3 illustrates the methodology of

developing educational simulations followed by a description of the simulations that were

created. Evaluation studies are presented comparing explaining waste management

process by simulation and by conventional methods (class room lecture and lab

experiments) .

Chapter 4 describes an ontology-based approach for representing mathematical

models and simulations that explicitly exposes knowledge contained in models at a

higher level (obj ective 4). The knowledge can be further used for constructing

conceptual models, simulations of similar systems, and educational and training

materials. Chapter 4 also addresses several problems with conventional methodology

used to develop simulations.







12


Chapter 5 summarizes contributions and conclusions, and identifies future

directions.















CHAPTER 2
CONTENT MANAGEMENT APPROACH FOR DEVELOPING EDUCATIONAL
MATERIAL

Introduction


The technology for authoring and delivering instructional materials continues to

evolve. At the current time, conventional tools such as PowerPoint, Adobe Acrobat,

Macromedia Flash, and HTML development tools are widely used to develop computer-

based educational resources in higher education. However, new approaches are evolving

that are based on databases, content management systems, and learning objects (LOs). A

significant difference between conventional tools and these new approaches is the latter's

focus on better representing the content (Dicheva and Aroyo, 2002) what we know and

what we teach and separating content from presentation how we teach and how

particular concepts are presented. By better defining and representing content, instructors

and course authors will achieve greater freedom and flexibility in creating and delivering

effective educational materials. These educational materials should be more easily

shared, and duplication of effort in developing learning materials can be reduced. In

addition, instructional experiences should be tailored to the needs of individual students,

not only providing the appropriate level and sophistication of information, but also

presenting it in a way that meets the individual student' s preferred learning style.









Domains Studied

Educational materials (educational simulation, websites) were created for the

following knowledge domains (two research areas of ES CSTC):

Solid Waste Treatment

The solid waste treatment area had two proj ects. The first proj ect was a bioprocess

laboratory called the Biochemical Methane Potential (BMP) lab. The objective of the

BMP lab exercise was to determine biodegradability of biological waste material (Course

Website for Bio. Eng. Lab, 2004). It involved three major steps:

* Medium preparation: This step involved mixing, heating, and cooling different
chemicals to prepare a medium. The medium and inoculum (sludge with bacteria)
were added to the sample of solid waste.

* Incubation: The sample, inoculum, and medium were mixed and were stored in a
bottle, which was placed in an incubator.

* Sample testing: The biodegradation of the sample was measured at different times
using gas chromatography machine. The biodegradability was measured after one,
three, five, fifteen, and thirty days.

An operational laboratory system of the BMP lab exercise had nine bottles, one

reactor, two gas cylinders, one incubator, and one gas chromatography machine. The

biodegradability determination using the physical lab took thirty days to complete. The

task of collecting data was divided among groups of students. Expensive chemicals and

equipment were used in the lab. The BMP lab was taught in two courses offered in the

Department of Agricultural and Biological Engineering, University of Florida. Dr. John

Owens taught the BMP lab in an undergraduate level course called Biological

Engineering Laboratory (ABE 3062) and Dr. David Chynoweth taught the BMP lab in a

course called Applied Microbial Biotechnology/Advanced Applied Microbial

Biotechnology (ABE 4666/ABE 6663). Because of the commercial application, this lab









is also consulted by waste management professionals at the national and international

level .

The second proj ect was titled "Anaerobic Composting for Recovery of Energy,

Nutrients, and Compost from Solid Waste during Extended Space Missions". It involved

the treatment of solid waste by a process called Sequential Batch Anaerobic Composting

(SEBAC). The fundamentals of the SEBAC process were the same as that of the BMP

process. The only difference was in the scale of operation; which means, the BMP

proj ect was a laboratory scale of the SEBAC proj ect. The biodegradability test needed

by the SEBAC was done in the BMP project. The SEBAC process used five reactors and

circulates liquid slurry, or leachate, between reactors in a specific sequence. The leachate

was circulated internally, to a reactor containing activated feed, and between the reactors

containing mature (old) feed and new feed. It took twenty-one days to treat a single

batch of solid waste (Chynowyth, 2002).

Wastewater Treatment

The wastewater treatment area had one proj ect titled "Effectiveness of a

Photocatalytic Reactor System for Water Recovery and Air Revitalization in Long-

Duration Human Space Flight". This proj ect involved the treatment of wastewater by the

Magnetic Agitated Photocatalytic Reactor (MAPR) process. The wastewater was treated

using magnetically agitated particles coated with titanium dioxide catalysts in the

presence of ultraviolet radiation. The experiments were conducted at different magnetic

strengths and with different particle sizes of the catalyst for the purpose of studying the

efficiency of the MAPR process (Mayzyck, 2002). The MAPR proj ect involved three

maj or steps










* Sample preparation: The wastewater sample and nano pure water were added to a
mixing bottle and mixed for several minutes.

* Sample treatment: The mixture of wastewater sample and nano pure water was sent
to the MAPR reactor. The UV light was turned on followed by the generation of a
magnetic field by a frequency generator. The frequency generator was operated at
a frequency of 20 hz, 80 hz or 120 hz. The sample was treated in the MAPR
reactor for a few minutes in the presence of UV light and magnetic field.

* Sample analysis: The sample was collected and sent to the spectrophotometer for
analysis and the collecting of kinetic data.

Rational for Structuring and Reusing Information of ES CSTC

The ES CSTC proj ects involved laboratory exercises in solid waste and wastewater

treatment. Typically, the instructions of a lab exercise are available as a paper or

electronic document that contains the relevant lab information. For example, the

instructions for the BMP lab exercise were available as a Microsoft Word document

containing the information on equipment (reactors, bottles), raw materials, catalyst, and

methodology (Course Website for Bio.Eng. Lab 2004). These lab instructions were not

structured, which means, the relationships between different concepts (equipment, steps,

and raw materials) were not defined explicitly. Several other lab exercises and other

educational materials (like lecture notes and presentations) in solid waste treatment also

use many concepts used in the BMP lab exercise, but the information cannot be reused

effectively because of the unstructured format of the information. Additionally, there is

no formal agreement in the way these concepts are defined, which creates communication

problems at the level of human and computer. There is a need to organize, process, and

retrieve the knowledge stored in the educational materials (lab exercise) so that the

content of the educational material can be easily reused and applied to build better

educational experiences.









Ontology

Ontologies are a promising technology for knowledge reuse and knowledge sharing

(Zheng et al., 2003). An ontology is a collection of concepts and relationships among

these concepts in a specific domain (Noy et al., 2000). For example, an ontology of the

BMP lab exercise contains the knowledge of anaerobic digestion and the concepts used in

a typical wet lab such as bottles and chemicals. The ontology of the BMP lab exercise

gives a well-defined meaning to the concepts used in the BMP lab exercise which will

allow these concepts to be used in other applications (reports, presentations, and

simulations on BMP). An ontology will allow educators at different institutions to share

their educational materials, improve the understanding of domain knowledge, and

increase the usage of knowledge within an organization (O'Hara and Shadbolt, 2004).

Ontologies can be used for assisting in communication between people, attaining

interoperability among computer systems, and improving the quality of engineering

software systems. Ontologies are a core component of the emerging Semantic Web

movement that attempts to go beyond conventional HTML file formats and other

proprietary file formats to better represent content on the Web (Lee et al., 2001). A

number of developments utilizing ontologies have been proposed to support a variety of

instructional and authoring activities. These developments are summarized in the section

"Efforts in Managing and Reusing Content Using Ontologies".

Literature Review


Computer-Based Instruction

Several relevant recent efforts involving techniques for developing computer-based

instruction are presented here. The Defense Advanced Research Proj ects Agency's










(DARPA) Technology Reinvestment Project (TRP) invited proposals for developing

authoring tools which could help in lowering the cost of producing computer-based

instructional materials (Spohrer et al., 1998). Many industries (publishing and

technology) and academia participated in DARPA's TRP project. Apple and IBM

proposed ScriptX, an object-oriented and cross-platform standard, for developing CD-

ROM content utilizing an authoring technology called SK8. The SK8 technology was

focused on providing authoring tools specific to the tasks, which would enable authors to

do their j ob in cost efficient and effective ways. One of the important lessons learned

from this proj ect was that intellectual property protection barriers, social conventions,

and business model restrictions can prevent people from using authoring tools.

The advent of the Internet had a significant impact on the process of delivering

educational content. The Internet was seen as a better medium for delivering educational

material than a CD-ROM (Spohrer et al., 1998). The focus shifted from developing

specific authoring tools to collaborating within an authoring community using the

advantages of the Intemet. The Intemet enabled the easy distribution and maintenance of

educational materials in an economical and efficient manner. The Internet also enabled

learners to access the course materials from remote locations like the home or office.

Presently, educational materials are developed using multiple multimedia

development technologies such as Macromedia Flash, Shockwave, or Microsoft

PowerPoint. For example, Flash animations are created to explain the various concepts

of chemistry (Neo/Sci Website, 2006). Authoring educational materials using computer-

based tools has many advantages. Computer-based authoring tools can lower the cost of

producing educational materials, engage learners by developing interactive and









immersive learning materials, and help educators in customizing and reusing content.

However, the management of educational materials becomes challenging as the content

of educational material increases in size and complexity. A concern arises about the

reusability of the content from technical and legal perspective. Additionally, it is

becoming difficult to locate and retrieve relevant educational materials.

Content Management Systems

Content Management Systems (CMS) are being developed for managing the content of

educational materials (Learning Circuits Website, 2001) by providing a capability for

authoring, collecting, storing, and delivering educational materials. Learning

Management Systems (LMS) are used for managing various administrative aspects, such

as course registration, of delivering a course. Learning Content Management Systems

(LCMS) combine the functionality of LMS and CMS. "A content management system is

a distributed software system which treats information in a granular way, enabling the

access, versioning, and dynamic assembly of pieces of information, and named content,

such as diagrams, tables, images, or pieces of text" (Canfora, 2002). Boiko (2002)

defined CMS by the following key processes:

* Collecting: Creating or acquiring content items and transforming the content into
standard formats

* Managing: Storing and maintaining the content and their metadata in a repository

* Publishing: Retrieving and extracting the content for producing information in a
specific format

Learning Objects

Presently, many educational materials are created without considering pedagogical

aspects. Learning Objects (LOs) are a paradigm that emphasizes presenting the domain

knowledge within the context of instructional strategies and assessments (Khan, 2003).









A Learning Obj ect (LO) consists of the following components (Sepulveda-Bustos, et al.,

2006)

* Goals and learning obj ectives

* Knowledge domain: It consists of the knowledge of course content, which can be
presented as text, image, animations, or movies.

* Instructional information: It presents the information relevant for presenting the
content in a particular sequence and adjusting the sequence and pace of the
delivering content based on learner' s ability.

* Searchable metadata: It includes the information about the content, which can be
used by learners or instructors for searching for a specific LO. It includes
information like name of the author, title of LO, or keywords.

* Assessment: It determines the attainment of learning obj ectives by the students,
which can be achieved by using assessment resources (exams, quizzes).

Other important aspects in generating LOs include the graphic design (the way it is

presented) and the medium of delivery.

A basic problem faced by the learning community is how to produce and deliver

quality content for online learning experiences. International Business Machines (IBM)

developed an approach for producing LOs to provide individualized learning experience

for learner' s specific needs (Farrell, 2004). The content of LOs was produced from the

reference books and presentations in a semi-automatic fashion. The learners were able to

search the LOs on the basis of media type, intended use, level of difficulty, or keywords.

Several efforts have been going on in standardizing the way LOs are created,

managed, and used. Four organizations are developing standards relevant to LO

technology: Aviation Industry Computer Based Training Committee (AICC), Institute of

Electrical and Electronics Engineers (IEEE) Advanced Distributed Leamning (ADL), and

Instructional Management Systems (IMS), Global Learning Consortium (WBTIC

Website, 2005).











Shareable Content Object Reference Model (SCORM)

Shareable Content Obj ect Reference Model (SCORM) is a standard developed by
ADL for LO (ADL Technical Team, 2004). The development of SCORM had a

significant impact on the e-learning industry and on the development of LO. Most of the
vendors are developing standards based on the SCORM. The SCORM standard requires
LOs to have the following features:

* Reusability: The LO should be capable of being assembled and restructured in a
variety of different courses. For example, a LO on "overview of anaerobic
digestion process" developed in an organization such as an agricultural engineering
department should be able to be usable in the training modules of other
organizations like USDA.

* Interoperability; The users should be able to combine LOs from the various sources
for designing their own courses.

* Durability: The advancement in the technology should not make a LO obsolete.

* Accessibility: The content developed using LOs should be accessible at anytime
from a variety of locations.

Efforts in Managing and Reusing Content Using Ontologies

Several relevant recent efforts in managing and reusing the content (also LOs) are

presented here. Most of these efforts have been proposed rather than implemented. Most

of the researchers (Angelova et al., 2004; Sridharan et al., 2004; Tan and Goh, 2005;

Nicola et al., 2004) have proposed ontologies for annotating learning resources while the

presented approach has described a system for storing the learning content in an

ontology.

A number of developments utilizing ontologies have been proposed to support a

variety of instructional and authoring activities, including hypertext navigation,

collaborative learning and training, courseware authoring, user interaction, and

information retrieval (Aroyo and Dicheva, 2002). For example, an approach has been









proposed for integrating authoring tools with the knowledge of instructional theories and

principles by developing a series of ontologies with the obj ective of delivering an

appropriate instruction method based on instructional theory (Mizoguchi and Bourdeau,

2000). An ontological approach to courseware authoring has been proposed by

separating domain knowledge and application related knowledge (Aroyo and Dicheva,

2002). Ontologies have been developed for describing the multimedia content used in

educational material. For example, Stanford has developed an ontology for MPEG-7, a

standard for describing multimedia content.

There have been several suggestions for making LOs reusable using ontology. One

of the suggestions was to create an ontology of the LO metadata which can help users in

searching and using LOs (Gasevic et al., 2005). The DocSouth proj ect used domain

specific metadata for describing the content of a LO (Pattuelli, 2006). Tan and Goh

(2004) proposed the association of domain ontologies with the learning resource for

classification, navigation, and searching ofleaming resources. Multitutor Ontology-

Based Leamning Environment (M-OBLIGE) proposed a system where ontologies were

used as the metadata of web-based educational materials i.e., educational material will

point to various ontologies for semantic markup.

The Larflast proj ect structured the learning content by developing a domain

ontology in finance and by using the domain ontology for annotating LOs (Angelova et

al., 2004 ). The annotations of LOs were entered manually and were used for linking the

LOs with the concepts of the ontology. The ontology of the Larfast project contains 300

concepts. The two types of LOs were described in the Larfast proj ect:

* Static exercises: Used to determine the knowledge of a domain
* Reading materials: Collected from the Internet and related to relevant concepts











The Larflast proj ect emphasized the usage of explicit domain knowledge in

describing LOs. For the purpose of authoring course outlines, Yang et al.(2005)

proposed an ontology based course editor. Sridharan et al. (2004) proposed an

application for managing and searching relevant documents by developing an ontology in

RDF .

Nicola et al. (2004) described the use of ontologies in gathering and organizing

teaching materials for the construction of a course. The ontology of course content was

developed and referenced to the learning resources. For validating the approach

suggested by Nicola et al., a course on ontologicall modeling" is under development and

an ontology of 168 concepts has been developed. Iowa State University developed the

domain ontology from a controlled vocabulary in the medical domain (colonoscopy and

endoscopy) and used it for annotating a video database (Bao et al., 2004).

Sepulveda-Bustos, et al. (2006) proposed a methodology for developing LO by

applying the approaches of software engineering, proj ect management, and instructional

design. The work of Sepulveda-Bustos, et al. applied the principles of Blooms taxonomy

in establishing the learning obj ectives. The components required to built a LO was

represented by an ontology of the components(obj ective, assessment, metadata, learning

assets, etc.) of LO. The ontology was used for identifying and collecting the identified

resources. The LOs were rendered as a webpage using Macromedia Dreamweaver, and

they were evaluated in an undergraduate course in fluid mechanics. On the contrary, this

study utilized ontology for storing the knowledge of resources. This study structured the

content of educational materials websitee and educational material) as the domain









ontology and the educational materials were generated automatically as explained in

"Presentation Generator" section.

Generating Presentations from Content

The content of educational material can be presented in a variety of formats like

animation, website, reports etc. The development of an educational material in a specific

format involves three maj or steps: collection of information, organization of information,

and presentation of information in a specific format (Alberink et al., 2004). There are

several techniques for generating presentations. One fairly common approach is to use

"server page" technology such as Microsoft' s Active Server Pages (ASP) (ASP Website,

2004) or Sun Microsystems's Java Server Pages (JSP) (Sun Website, 2004). Server page

technology (JSP and ASP) is restricted to the creation of web pages, but has the

advantage of drawing content from a database to populate web pages.

Style sheets offer another technique for creating presentations. A Style sheet

describes the rules for presenting documents in different presentation style formats on

different media like webpage or print (W3C Website, 2006). Separating content and

presentation can be achieved by storing the content in a database and generating the

presentation by using style sheets (Clark, 1999). The style of a presentation can be

specified independently of the actual content, so that the same content can be presented in

different styles. For example, multiple websites with different presentation styles (fonts,

colors, layout) can be generated from the same content so the content can be presented to

a specific audience in a suitable format (CSS Website, 2005). The rationale for using

multiple styles is the preference of a specific style by the intended audience. For

example, different colors are prominent in different cultures so the background color of

the website can be changed based on culture of the audience. Similarly, older audiences










prefer bigger fonts so the font can be changed based on the age of the intended audience.

Among other things, this frees instructors (course authors) from having to be experts in

graphic design, and they can focus instead on their subject expertise. Instructors can

choose from pre-existing styles that were created by graphic design experts.

One of the most well known methods of utilizing independent styles to generate

presentations is Extensible Stylesheet Language (XSL) technology (Clark, 1999). In this

approach, the style of presentation is described in a XSL Transformations (XSLT) file.

Basically, an XSLT provides instructions for how one XML file can be converted to

another by telling how a tag in the source file should be converted to a tag in the

destination file. In practice the source XML file contains the content to be presented and

the destination XML file can be HTML for website generation, XML Formatting Obj ects

(XML FO) for printing, or other formats. As XSL technology can be somewhat tedious

to develop, other techniques have been created to convert database obj ects to

presentations where basic elements of style are described in a flexible format (also as

database obj ects) and are used by a program that generates multiple formats (HTML,

Applet) from database objects. The style objects that specify details such as fonts and

colors guide the program.

Content Management Approach

The approach used in this study applied a CMS for creating and managing

educational materials in the area of waste treatment These systems have the ability to

generate presentations from a database through a process in which the elements of

database obj ects concepts stored in database were mapped to a particular presentation

format such as HTML, animations and other formats as explained in "Presentation

Generator" section). Such a facility can provide a valuable component in an information









technology approach to handling educational resources in agricultural and biological

engineering. It can promote the sharing and reusing of educational materials within a

department and between different departments locally and regionally. The presented

approach will change the focus from developing a specific educational material to

representing the knowledge of educational material and using generic software

applications for generating educational materials.

Components of a Content Management System

This section describes the components of the content management approach used in

this study for developing educational materials. Web-based tools were used for entering

the details of the lab processes as an ontology in the database (Badal et al., 2004b).

Presentations that can take the form of educational simulations, web pages and other

formats were then generated from the database using software tools.

Figure 2-1 shows the components of the content management system used for

developing educational materials. Central to the approach is an ontology for building

formal descriptions of concepts and showing how these concepts are interrelated. The

ontology was stored in an obj ect database that provided a physical storage mechanism for

large numbers of concepts or obj ects; the bioprocess lab example contains several

hundred. Graph-based and web-based authoring tools (described in section "Tools for

Creating an Ontology") were used by instructors to create and manage course content.

These tools were integrated with an obj ect database for storing the ontology structured

information. Several different techniques (JSP, Java Applet) were used to automatically

generate presentations from this content. Details of the major components of the system

are described here.













Ontology I D~atabase I bettr






Presentation Java Applet,
Generator Flash, HTML


Generated Educational
Simulation,
MaterialsWeae


Figure 2-1. Components of a content management system used for developing
educational materials -

Ontology

Each concept in the lab exercises is formally defined by a concept in the ontology.

An ontology of the BMP lab exercise contains concepts such as bottle, stock solution

(chemical), degradation, and other concepts specific to the lab. The BMP lab exercise

uses many bottles so the ontology specifies the concept of bottle and stores various

bottles (such as a bottle for storing samples) as a bottle concept. A concept contains

taxonomic relationships (a "bottle" is a member of the class "equipment"), properties (a

bottle has as a particular volume), and association with other concepts (a bottle can

contain a chemical, a bottle can be physically connected to a valve). A concept or obj ect

can also have behavior (a bottle can fill or empty over time).









Methodology of developing an ontology

The ontology was developed using the Web Taxonomy authoring tool (described in

section "Tools for Creating an Ontology"). The following elements were used in the

ontology:

* Class: A class is used for describing general concepts like bottle or procedure. For
example, the BMP lab exercise used many bottles & so a bottle class was used to
describe the bottle concept.

* Individual: An individual is used for describing instances or specific occurrences of
a concept class. For example, the BMP lab exercise used seven bottles so seven
individuals of the bottle class were created.

* Property: A class can have several properties for defining its attributes. For
example, radius and height were defined as a property of the bottle class for
capturing geometrical information.

* Relationships: A class can have relationships with other classes. The relationship
can be either predefined (subClass, superClass, hasParts, partOf) or user-defined
(hasName or comesOutOf). The hierarchical relationships were modeled by
subClass and superClass relationships. A bottle is a specific kind of equipment so
there exists a relationship called "subClass" between the bottle class and the
equipment class.

Ontology was developed with WebTaxonomy authoring tool. The following steps were
used for developing the ontology:

Collection of relevant documents: The relevant documents such as research
papers, PowerPoint presentations, and published reports of ES CSTC projects
were collected.

Analysis of documents: The information from the relevant documents was
analyzed and the concepts were extracted from them manually.

Development of class hierarchy: The collected concepts were enumerated and
classes were generated from the concepts. The classes were further organized
into a class hierarchy by organizing classes from more general (like equipment) to
the more specific (like pump). The classes were entered into the ontology using
the Web Taxonomy editor. Figure 2-2 shows the class hierarchy for the BMP lab
exercise created as a part of this project. Each class was specified by its
definition, properties, and important relationships like superClass and subClass.

* Creation of individuals: The individuals were created by specifying the class to
which the individual belongs and by entering the values of properties.









An individual of bottle class called "stock solution bottle 1" was created by

following steps:

* Open the Web Taxonomy Editor (Figure 2-2).

* Specify the name of the class "bottle" to which the individual belongs-"stock
solution bottle 1."

* Create an individual using the Web Taxonomy editor

* Enter the definition of the individual and the values of properties for the individual
(radius = 50, height = 10).

Tools for creating an ontology

Web Taxonomy (Beck and Lin, 2000) and Obj ectEditor (Beck, 2003b) were used

for creating ontologies. The availability of the ontology construction tools on the Web

not only makes the tools more accessible and easier to distribute, it also allows users to

collaborate over the Internet to develop educational resources. Web Taxonomy (Figure

2-2) is a tool for adding and editing the concepts in the ontology. Figure 2-2 shows a

portion of the ontology developed for the BMP proj ect, and it displays the different

equipment items such as bottle, flask, gauge, etc. used in the BMP laboratory procedure.

Each piece of equipment used in the experiment was described by an individual in the

ontology. For example, the BMP lab used seven stock solution bottles so there are seven

individuals of stock solution bottle in the ontology.

Obj ectEditor (Figure 2-3) is an alternative graphic interface for partitioning the

concepts that belong to a specific proj ect like the BMP project. Figure 2-3 shows a

portion of an ontology developed for the BMP proj ect using Obj ectEditor. In particular,

this diagram (Figure 2-3) shows equipment obj ects and how they are physically

connected. For example, it shows that the individual "ss pipe 1" is related to the


















Applet
4~ ~ .n,.,-. eqimetI


individual "stock-solution bottle 1" by a relationship called "out of bottle" because "ss

pipe 1" comes out of "stock solution bottle 1"


I~t~ Fraru raur~ ~:lrl~r~ ?mmlr*tiu


Concept )1 [n] thernaterials needeclfor purpose :


Figure 2-2. Schematic of Web Taxonomy showing a portion of the BMP ontology

The ontology captured not only the physical obj ects and their structural and

dynamic relationships needed for developing interactive animations (educational

simulations), but it also acts as a dictionary for all the terms used in the ES CSTC

proj ects described in the section "Domains Studied". The ontology provides a better way

for students to browse concepts to learn their meaning and interrelationships. This

dictionary provides machine-interpretable definitions, which means, the computer can

analyze the meaning of terms, and provide reasoning facilities that can determine how










terms are related. A multilingual feature is also supported so that terms in different

languages can be used to refer to the same obj ect.


Projects Windows Help


Figure 2-3. Schematic of Obj ect Editor showing a list of equipment and reagents used in
the BMP lab and the relationship between them

Database System

The web-based tools for constructing the ontology were built on top of Obj ectStore

(ObjectStore Website, 2006), a commercial object database management system. The

obj ect database was used for storing the ontology because the obj ect database provided a

more convenient and natural way to organize data structured as an ontology rather than

through tables, as is done in a relational database. The integration of the web-based tools

with a database facilitated the development of educational materials by storing the









ontology in the database and using it for generating educational materials in different

formats .

The online tools allow the instructor to develop educational materials from any

remote location and store them in a common server-side database. The concepts can be

added or edited using Web Taxonomy, Obj ectEditor, or other tools provided as part of

the authoring environment (including equation editors, text, table, and vector graphic

editors).

Presentation Generator

The presentation generator consisted of several computer programs written in

various languages (Java, Java Server Pages (JSP)) for rendering educational material in

multiple formats. Two applications were developed using JSP and Java applet

technology. The JSP application was developed for rendering the research information of

ES CSTC proj ects as a website while the Java applet application was developed for

displaying the dynamic information ofES CSTC projects as educational simulations.

The next sections describe these applications.

Java server page technique

The website for the proj ects at ES CSTC was generated using JSP technology.

Figure 2-4 displays the interface of the website created for the BMP lab exercise, which

shows the details of a chemical (stock solution) used in the BMP lab. A JSP is very

much like a conventional HTML page and contains HTML tags for defining the

appearance of a webpage, but it also contains additional tags embedded in the HTML that

refer to database obj ects. In general, wherever a reference to a database obj ect appears,

the contents of that obj ect are displayed at that point in the JSP. So, in Figure 2-4 the

logos, titles, and frames were all created using static HTML tags, but the body of the text









was created dynamically from database objects referenced in the JSP. The JSP must be

created manually, but then the content is inserted automatically. The following steps

were used for creating the website from the content:

* Ontology development: The details of the ES CSTC proj ects (described in section
"Domains Studied") were stored in the database by developing an ontology using
the Web Taxonomy authoring tool. The process of developing an ontology is
described in section "Methodology of Creating an Ontology".

* Design of website layout: The general layout of the website (logo, title, frames)
was created in HTML using Microsoft FrontPage. Some of the links (e.g. "About
the Center") on the left-hand side of the webpage were manually hyperlinked to an
external website (www.ees.ufl.edu), while some of the links (e.g. "Proj ect") were
hyperlinked to the webpage generated from the content stored in obj ect database.

* Development of JSP application: A JSP application was developed for rendering a
specific concept in the ontology as an individual webpage. The JSP application
contained the HTML tags developed during step 2 and additional tags for
communicating with a Java class. The JSP application communicated with a Java
class called "BMP Bean", and the "BMP Bean" class was used for communicating
with the ontology database using Java Remote Method Invocation (RMI) protocol.
Borland JBuilder integrated development environment (Borland Website, 2006)
was used for developing the Java Bean class and for implementing the RMI
protocol .

The presented approach illustrated an approach of dynamically generating a

website from the ontology. The general layout of the website (header, side) was designed

using Microsoft FrontPage. The content for the main body of the website was structured

as an ontology, and the main body for the website was generated by the logic embedded

in a Java class, as described in previous paragraph. The content for the main body of the

website can be updated by modifying the ontology while the presentation of the website' s

main body can be changed by modifying the Java class.

It is easy to provide dynamic content using JSP (Sun Website, 2006). The JSP

technology uses the functionality of Java language and is widely supporetd by the

software vendors (Webber, 2000). The JSP technology uses reusable components, rather









than using only scripting in a page, which speeds up the development of an application

(Sun Website, 2006). The JSP technology uses Java classes for generating the content of

a webpage and HTML tags for controlling the layout of the webpage. In this way, the

JSP seprates the content and layout of a webpage. Java IDE tools can be used for

debugging Java classes while the commonly used webpage design tool can be used for

debugging the html part of the JSP website.

The functioning of JSP involves the generation of a Java class from the JSP and

the Java class is then parsed to create a serylet class (Webber, 2000). Another

disadvantage of the JSP technology is that the content and the logic is not well separated.

The JSP technology allows the embedding of logic in a webpage, which defeats the

purpose of separating the logic and the content (Spielman, 2001). This can create the

problem of maintaining and updating the website. The JSP technology also allows the

insertion of inline Java code in a JSP page, which makes it difficult to separate the tasks.

This also creates the problems in understanding the JSP page.

Java applet technology

The Java applet described in chapter 3 was used for presenting dynamic

information of the ES CSTC proj ects. The presentation of dynamic information required

interactive features provided by the Java applet technology. In contrast, the JSP

technology is used for generating HTML, XML or other types of documents. This study

used JSP for generating HTML. However, Java Applets can be inserted in a JSP page for

providing interactivity.

Generated Educational Materials

The educational materials were generated from the same database in two formats:

as a website containing text and graphics and as an educational simulations. The website
































,JII(~P~~


QA gant


STOCK ~ SOLl~iTION 2





('meogosirwl ~ r Inomain





r HI I .I~III II I i~lll Iji- I- ,I ll j FllI H I III I 0 11



II.IJII1 1 I .IlUI


was used to display the ontology of the ES CSTC proj ects in solid waste treatment and

wastewater treatment. Students can browse the different waste treatment concepts and

use the website as a waste management dictionary. The educational simulation

(described in chapter 3) is a Java applet that presents the dynamic information as a 2-D

animation. The simulations were evaluated in two courses taught in University of

Florida.


Address /~http fforb.at.ufl .edulE515TCIlESCSTC. jsp~name=stock%/20solu tion%202 v Go Links .Search -
-,. I v 11..1.1. I* e.... QPPo-upsBlocked(0) ijom all $Messenger Idr1 11:r]


600gle-


Environmental Systems Commercial Soace Technoloav Center


tiouir riseee.re, r




Lillru


SiSite4r Tenilop-


Figure 2-4. Website generated by the content management approach

This study showed that the content management approach (i.e., using a database to

store research information) can be used for documenting research information. The

information was first structured as an ontology (structured information) and stored inside









an object database (an ontology management system). This approach allowed the

documentation of research at a very fine level (i.e., documenting research at the level of

concepts used in various research projects) instead of storing the educational materials at

only a course level in the form of documents, presentations or other formats that fail to

explicitly represent content. There can be an overlap of concepts used in various

proj ects, and the overlap of concepts can be used for identifying similarities in various

proj ects. For example, both SEBAC and BMP proj ect uses the concept of anaerobic

digestion, so the overlap of concepts in ontology can infer the similarity in BMP and

SEBAC project.

The JSP technology was used to generate a website from the ontology. Automatic

presentation techniques can greatly reduce the effort required to create educational

materials; however, it is not always desirable to fully automate the process, as often the

instructor does want to have full control over the presentation. Chapter 3 describes the

automatic generation of educational simulation (rendered as a Java applet) for displaying

dynamic information of the lab processes used in the ES CSTC projects. Information

about the lab processes was stored in the ontology and the dynamic information was

displayed in an interactive format animations using Java applet technology.















CHAPTER 3
EDUCATIONAL SIMULATION: AN APPROACH FOR PRESENTING DYNAMIC
INFORMATION OF A PROCESS

Introduction

It is critical to present educational materials in a format that best matches the

student's individual needs. Since engineering processes are dynamic in nature, it is

beneficial to present the processes in the form of an educational simulation. An

educational simulation is a presentation of a dynamic process (like the steps of a

laboratory experiment or operating a machine) as an interactive and intuitive animation

which can help a student in understanding a specific process. The interactivity of the

educational simulation increases a student's learning efficiency (Mclean and Riddick,

2004). Another advantage of using simulation is that the student can access it anytime

and anywhere, in contrast to an in-lab experience requiring special equipment.

Virtual Lab

Educational simulations are also known as virtual labs, where students can

experiment with the equipment and the process itself. Instructors can show the lab in the

form of an animation for explaining the different concepts in the lab. The learners can

also change the process parameters to study how they impact the behavior of the system.

Since one of the obj ectives of this proj ect is to present information in a format most

suitable to students, virtual labs have been created for explaining the concepts of waste

treatment processes described in Chapter 2.









Literature Review on Virtual Labs and Educational Simulations

Virtual laboratories have been developed in various domains like physics,

engineering, power electronics, and medicine (Hashemi, 2005). The IrYdium project

developed educational materials in the domain of chemistry (Yaron, 2003). Their goal

was to create a simulation-based learning environment where high school and college

students can learn the concepts of chemistry through interesting real-world applications.

Remote database and network technologies were being used to facilitate the delivery of

the software over the Web. Similarly, a multimedia-based course in environmental

engineering and process design was developed at University of Maine (Katz et al., 1997).

The video clips and spreadsheet technologies were used for explaining the processes of

natural systems as well as data collection processes. A virtual laboratory in the area of

material science and engineering was developed in the Department of Mechanical

Engineering at Texas Tech University using Flash and other multimedia technology

(Hashemi, 2005). The University of Florida used the same approach (Flash technology)

in the domain of medicine to teach an anesthesia machine operation (Lampotang, 2004).

The University of California, Davis developed seventeen virtual experiments in

food processing for academic purposes (Singh and Erdogdu, 2005). Each virtual

experiment includes simulations, which were implemented with Flash technology. These

simulations were developed for enhancing the understanding of engineering concepts

used in food processing operations.

Rice University is using Java technology for teaching various statistical concepts

(Lane, 2003). The Iowa Bioprocess training center offers training in bioprocessing by

virtual reality and classroom training (Brigham, 2003). Because of the cost and skills

requirements, there is a great need for training bioprocessing (waste management) skills










by simulations or virtual laboratory. Many other examples of teaching a concept by

utilizing a virtual laboratory are also available on the Web.

The task of creating a virtual laboratory is challenging because it requires a

multidisciplinary effort; in addition the task of managing the content of virtual laboratory

becomes more challenging as the content increases in volume and complexity. Most of

the virtual laboratories are implemented using a conventional programming language

(JAVA, C, ActionScript) and software tools with little effort in explicitly representing

content. This study investigated an approach of using an ontology for structuring and

storing the content for facilitating the development of virtual laboratories and other

educational resources.

Methodology of Creating Educational Simulations

The content management approach described in chapter 2 was used as a

methodology for creating educational simulations. These simulations were developed for

running the experiments related to the waste treatment processes, as described in chapter

1, on the Web. The following steps were used for creating educational simulations:

Ontology Development

The details of an experiment were stored in the database by developing an

ontology using the ontology development tools described in chapter 2. The ontology was

developed for a specific domain like the BMP lab exercise. The details of the lab

exercise like information about various equipment (bottles, pipes, valves), chemicals

(stock solutions, inoculums), and samples used in the experiment were represented as

different individuals in ontology. The information of the lab exercise was structured

using the concepts of obj ect-oriented design and ontology principles. For example,

"paper" is a kind of a sample and it has a property called "rate constant" with a value of









"2 seconds" which was used for calculating the rate of degradation for biodegradable

sample. Therefore, a concept called "sample" was entered in the ontology and "paper"

was described as a specific type (or subclass) of sample. Each paper concept has a

property called "rate constant" used for storing the value of rate constant.

Development of Java Classes

Java classes were developed for rendering the details of specific concepts (like

equipment) used in the lab exercise and also for implementing the behavior of specific

concepts (like bottle) in the simulation. The details of the concepts were stored in the

ontology. For example, a bottle is a concept that has width and height. The details of the

bottle and its association with different concepts were stored in the ontology, but Java

classes were implemented for rendering the bottle concept and required behavior like

filling and emptying the bottle. A Java class was implemented for every physical

individual in the simulation, and within each Java class, methods implement the behavior

of each individual.

Development of Educational Simulation

The simulation was rendered as a Java applet. Individuals specific to a lab

exercise were loaded into a module using Obj ect Editor (described in chapter 2). The

applet loaded the details of each individual (equipment) in the module from the ontology

and executed corresponding Java classes for rendering the details and behavior of each

equipment. The simulation was implemented in two modes: movie mode and interactive

mode. Movie mode (Figure 3-1) was implemented by writing a script which was used for

starting and stopping the animation of different equipment. The movie mode ran the











simulation sequentially, similar to the instructions or standard operating procedure for a

process, so a student can get an overview of the process.


Biodegradabilitys Determination Simulation

delonized water ,i,
copper column heater
_BMP therrnorneter button


Ammonium ~ ~ ~ ~ ~ ~ ~ B Popa H lm eter button 1 C



macro nutrient
N2-COL
Iron chloride


Reducing agent





.-....... d3collect data

GC Machine Incubator
Demonized water Is added
sample is added to the serum bottle



Figure 3-1. Interface for the BMP laboratory for determining biodegradability of a
sample in movie mode

Interactive mode (Figure 3-2) allowed students to experiment with the lab


experiment in an interactive fashion. In the interactive mode, the learners started and

stopped the animation of different equipment by clicking on the valves and buttons

(Badal et al., 2004c). The instructions for running the simulation in interactive mode are

given on the ES CSTC education and outreach website (ES CSTC Education and

Outreach Website, 2006).











Biodeg radab~ilityc Determi nation Si mulation
deionized wrater


Figure 3-2. Interface for the BMP laboratory for determining biodegradability of a
sample in interactive mode

Results


Several educational simulations were developed for explaining the different

aspects of waste treatment processes. The details of these processes are described in the

"Domain Studied" section of chapter 2. These simulations can be accessed from the ES

CSTC education and outreach website (ES CSTC Education and Outreach Website,

2006)

Simulation for Solid Waste Treatment

Bioprocess lab (BMP lab)

Figure 3-1 and Figure 3-2 show the interface for the BMP laboratory exercise for

determining biodegradability of a sample in movie mode and interactive mode. Figure 3-

1 shows that the BMP lab exercise contains nine bottles, one reactor, two gas cylinders,


aC MICllhin InCulbaler










one gas chromatography equipment, and one incubator. The BMP process involved

mixing, heating, and cooling of chemicals to prepare a medium. The sample and medium

were mixed and placed in an incubator. The biodegradability was measured after one,

three, Hyve, fifteen, and thirty days by the gas chromatography equipment.

SEBAC simulation

Figure 3-3 shows the interface of the SEBAC process for treating solid waste in the

movie mode. It shows Hyve reactors that were used for various purposes (filling sample,

empty sample, and storing new, activated (matured), and old sample) during the SEBAC

process. The sample was treated in the reactors by circulating a new sample with an old

sample and by circulating the activated sample with itself. The movie mode (Figure 3-3 )

shows three buttons which can be clicked for showing the different circulations in the

SEBAC process. The "single reactor" button shows the circulation in the reactor

containing the activated sample. The "two reactor circulation" button shows the

circulation between the reactor containing the old sample and the new sample. The

"three reactor circulation" button shows the circulation between the reactor containing

the old sample and the new sample and the circulation in the reactor containing the

activated sample. Figure 3-4 shows the interface for the SEBAC process for treating

solid waste in interactive mode. The user can click on different valves for activating the

flow in the pipe and filling the reactor.

The interface of the SEBAC process (Figure 3-3, Figure 3-4) has many pipes,

reactors, and valves which can be hard to comprehend, so an additional simulation was

developed for showing the circulations between the three reactors in the SEBAC process

(Figure 3-5). The simulation shows three reactors with new, activated, and old feeds.
















filC


.


JIlliusalu M


Simulation Result
YTrriecdays) PercentCorwarsono


44




SEBAC Simulation








011 II


Lun].I.J .I rIJ.-


Figure 3-3. Interface of the SEBAC process for treating solid waste in movie mode.


SEBAC Simulation


Sltrilatlort GAustu
Loading sitlus
Flow. Description


Figure 3-4. Interface of the SEBAC process for treating solid waste in interactive mode


Siriulalia0 Rdsult
Tifie(Clays) Percen1 Correrlain










SEBAC Simulation with three reactors






















Figure 3-5. Interface of the SEBAC process with three reactors for treating solid waste in
movie mode (ES CSTC Education and Outreach Website, 2006)

It does not show the process of filling and emptying the sample in order to simplify

the presentation. Figure 3-6 shows the phenomenon of clogging in the SEBAC process.

The clogging simulation was developed in an interactive mode. The student can click the

two-way valve for circulating the flow of liquid slurry (leachate) in an up-flow direction

or down-flow direction. The simulation illustrates the movement of solid sample

particles in the reactor and the flow of leachate in the reactor and the pipe. The pressure

of the reactor is shown by the pressure gauge. The pressure in the reactor increases due

to the accumulation of solid particles (that is, clogging) at the inlet and outlet of the

reactor. The problem of clogging was solved by reversing the flow of leachate. The

reversible flow of leachate was achieved by using a two-way valve. The flow should be

reversed automatically or manually after a fixed time to avoid clogging. In Figure 3-6,










up-flow is obtained by clicking the upper half of the valve and the down-flow is obtained

by clicking the lower half of the valve.



I
S|0.04132231404958678
pressure gauge













Figure 3-6. Interface of the SEBAC process with a single reactor for showing the process
of clogging (ES CSTC Education and Outreach Website, 2006)

Simulation for Wastewater Treatment

Figure 3-7 shows the interface of the MAPR process for treating a sample of

wastewater in movie mode. The user can watch the MAPR process by clicking the "start

movie" button. The interface shows two bottles for storing a wastewater sample and

nano pure water. These bottles were connected to the third bottle (mixing bottle) by

pipes which have valves for moving the wastewater and nano pure water to the mixing

bottle. The color of the valve changes to green when the valve is opened, and the color

changes to red when the valve is closed. The wastewater sample and nano pure water

were mixed in the mixing bottle. The diluted wastewater sample was treated in the

MAPR reactor in the presence of ultraviolet (UV) light and a magnetic field. The UV

light lamps were used for producing UV light. A frequency generator was used for

generating electrical signals at three different frequencies. These electrical signals were











transmitted to a solenoid for producing a magnetic field. The treated wastewater sample

was sent to the spectrophotometer for the analysis of wastewater.


MAPR simulation





wastewater concentrate nano pure water









Spectrophotometer
get data
1-~ l .- In ~
Simulation Status _ll H: : :
frequncy generator


Medium frequency current Is used to generate magnetic field for agitating magnetic particle


Figure 3-7. Interface of the MAPR laboratory for treating a sample of wastewater in
movie mode

The user can click on valves and buttons for running the MAPR process in an

interactive fashion. Figure 3-8 shows the interface of MAPR laboratory for treating a


sample of wastewater in interactive mode.

Evaluation of Simulation

Evaluation of Solid Waste Treatment Simulation

An evaluation of the BMP lab exercise was done with the obj ective of collecting

feedback from students and to compare the methodology of teaching the BMP lab

exercise by simulation with the conventional lab instructions and hands-on methods. An

evaluation form was designed to measure the understanding of technical concepts by the

students as well as their perspective about the teaching methodology.







48




MAPR simulatiion




waeIBW910f COflCBII1IalB flafi piltG V.*Tit











frequencygenerato



idedium f[requeacy cur.Int is used irgener le m ig;~rah rild in jj; ibb. pri gult p ai=.JE*


Figure 3-8. Interface of MAPR laboratory for treating a sample of wastewater in
interactive model

The understanding of technical concepts was measured by designing a set of ten

questions related to the BMP lab exercise with the help of the instructor, Dr. John Owens.

The perspective of the students was measured by designing a set of eight questions

related to the experience of the students with the teaching methodology. The subjective

evaluation measured the following aspects:

* Encouraging students to learn by a particular teaching methodology
* Developing confidence in the students about concepts used in the lab
* Enabling students to work through course materials at their own pace
* Developing students' creativity and skills
* Enabling students to apply the concepts learned in the lab to real world situations
* Teaching students to work together







49


The students were also asked if they found the teaching methodology interactive

and if they had a good learning experience during the evaluation. These questions were in

the format of multiple choice.


The learning experince is good

90.00%
80.00%
70.00%-
60s.00%

ii40.00%-

20.00%
10.00%-
0.00%




B m after performing lab
satisfaction after seeing simulation


Figure 3-9. Overall subjective experience of the students by two teaching methodologies
for the BMP lab evaluation


Ten students of ABE 3062 (Course Website for Bio. Eng. Lab, 2004) were asked to

read the lab instructions and perform the lab manually. After performing the lab, the

students were asked to fill out the evaluation form (Appendix A) within one week. Seven

students of ABE 4666/ABE 6663 were shown the simulation as a group and were asked

to complete the evaluation form in the classroom. Before evaluating the simulation, the

students of ABE 4666/ABE 6663 were given a brief tour (45 days before seeing the

simulation) of the BMP lab as a part of the course.

Results of the evaluation were that the average score (technical concepts) for the

class after seeing the simulation was 57.14, and the average score for the class after










reading the lab handout and performing the lab was 62.22. The statistical analysis

showed that there was no significant difference in the scores (t = -.5, df = 14, p = .25).

The subj ective evaluation (Figure 3.9) showed that students found both teaching

methods (hands on lab and learning by simulation) useful to nearly the same extent. The

results showed no significant difference in the various aspects of subj ective evaluation

except that the students found it easier to work at their own pace with the conventional

method than by simulation. Of course, in this evaluation the students were not yet given

the chance to use the simulation individually; rather, the instructor showed the simulation

to the entire group.

Evaluation of Wastewater Treatment Simulation

The class of ENV 45 14 (Water and Wastewater Treatment) was divided into two

groups. Each group had seventeen students. The first group was asked to run the

simulation on their laptop and read the online lesson. The online lesson was designed

manually using Microsoft FrontPage for giving the background information of MAPR

process. Some of the concepts in the online lesson were linked to the MAPR ontology.

The students were asked to complete the evaluation form (Appendix B) after running the

simulation and reading the online lesson. Presently, the students are not given the hands

on experience using the MAPR process because the lab exercise has not been designed.

The second half of the class attended the class room lecture of MAPR and was asked to

fill the evaluation form in the classroom.

Results of the evaluation showed that the average score (technical concepts) of the

class after performing the simulation was 82 and the average score for the class after

attending the class room lecture of MAPR was 73. The statistical analysis showed that










there was no significant difference in the score (t =1.59, df = 16, alpha = .05) for

technical concepts.

The results showed the significant difference in the following aspects of subj ective

evaluation:

* The confidence in the concepts of the MAPR lab after performing simulation was
greater compared to learning the concepts in the classroom lecture (t = 2.51, alpha
= 0.05).

* The simulations enabled students to work through course materials at their own
pace (t = 4.4, alpha = 0.05).

* Students found the learning experience with simulation more interactive (t = 2.79,
alpha = 0.05).

* Students did not like the learning experience with simulation (t = -2.66, alpha =
0.05).

However, the results also showed that there was no significant difference in the

following aspects of subj ective evaluation:

* Encouraging students to learn by a particular teaching methodology
* Developing students' creativity and skills
* Enabling students to apply the concepts learned in the lab to real world situations


Conclusions

Based on the results of evaluation, it can be concluded that the simulation can help

the instructor in teaching a lab exercise. The effectiveness of simulations also depends

on the approach of integrating simulations in the instruction, that is, the simulations can

be either shown to the students as a demonstration or students can run the simulations on

their own computer. The evaluation results concluded that the simulations enabled

students to work through course materials at their own pace when the students ran

simulations on their computer whereas the students were not able to follow the concepts

when the simulations were only demonstrated in the classroom. In addition, the










confidence of a student in the lab concepts increased when the student ran the simulation

on his/her computer.

The use of simulation helps in teaching the lab where it is practically infeasible to

teach the lab as a hands-on approach because of the high cost of the equipment and

chemicals involved. Simulations can also serve as a replacement experience for

universities and colleges that do not have a waste treatment laboratory. The computer-

based simulation can also be used to augment the real laboratory experience.

Furthermore, the techniques presented in this study can reduce the cost of creating

computer-based simulation.

The evaluations were not designed by consulting the statistical analysis

professionals. However, instructor of BMP lab (Dr. John Owen) and instructor of the

MAPR lecture (Dr. Dave Mayzyck) were consulted for developing evaluation forms, and

they are consistent with the type of evaluations (tests and quizzes) used in the courses

taught by these instructors.















CHAPTER 4
AN ONTOLOGY-BASED APPROACH TO MATHEMATICAL MODELING

Introduction

An ontology-based approach to mathematical modeling, in which a model is

represented using ontology concepts, can help address several problems with current

methodology used to develop simulations. The general goal is to better communicate

knowledge about models, model elements, and data sources among different modelers

and between different computers. This can be achieved through the ontology's ability to

explicitly represent and thus define concepts used in models.

Various researchers create simulations within a particular domain to address a

specific problem. For example, various simulations have been written in the domain of

solid waste management for determining anaerobic biodegradability of a solid waste

(Batstone, 2002). There is an overlap of the concepts and interactions used in these

simulations. Frequently, different modelers use different symbols for the same concept.

The use of different programming languages makes communication even more difficult

(Reitsma and Albrecht, 2005).

Literature Review

Problems in Developing Simulations

Typically, a model is implemented in a particular programming language like

FORTRAN, C++, or Java so it can be run to understand the behavior of the system.

However, the meaning of the model is lost when it is represented using program code

(Furmento et al., 2001). Researchers must understand the programming language in









order to understand the model. Semantic issues (like meaning of symbols or concepts

used in the model) should be addressed so the knowledge in a simulation can be made

explicit (Lacy and Gerber, 2004). While such models are documented using papers and

manuals, this documentation is physically separate from the model implementation itself.

It is difficult to maintain both the model and the documentation, and often the

documentation is not an accurate description of the model implementation. All the

details of program code are difficult to describe in written documentation, so that

ultimately it is necessary to read the computer code in order to truly understand how the

model works. These issues need to be addressed so the knowledge in a simulation can be

made explicit (Lacy and Gerber, 2004; Cuske et al., 2005).

Typically, many different yet similar models are available for a particular domain

like solid waste management (Batstone, 2002). The challenge lies in knowing precisely

how two models are similar or different and selecting the one most suitable for a

particular task (Yang and Marquardt, 2004). When a particular model is encoded in a

conventional programming language, it is very difficult to do comparisons between

models.

The construction of a model starts by problem definition followed by the

development of a conceptual model, mathematical model and the implementation of

mathematical model. Initially, the problem is defined as a text or other suitable format.

Once the problem is made explicit then the task of conceptualizing the problem takes

place. There are different ways to conceptualize a problem (Fishwick, 1995). The

conceptual model defines the structure of the problem and characterizes a system using

physiochemical concepts (Yang and Marquardt, 2004), that is, it represents a system as a









network of concepts. For example, a conceptual model of a solid waste treatment process

consists of various concepts like bacteria, fatty acids, ions, and interrelations such as

conversion of acids into ions (Lai, 2001). These concepts and interrelations are further

represented as mathematical symbols and equations within a mathematical model. A

mathematical model is further implemented as a simulation. Different tools and

vocabulary are used in development of each layer (conceptual, mathematical, simulation

code) of the model (Zerr, 2005). As the model incorporates new functionalities, the

modifications are not made in all layers of the model. For example, the simulation is

often modified to incorporate the new system functionality by modifying the code, but

the conceptual model is not updated (Zerr, 2005).

Possible Solution for Communicating Knowledge of a Model

One of the possible solutions for enhancing the communication of knowledge about

models at both the researcher/developer and machine level is the use of ontologies. An

ontology is an explicit specification of a conceptualization (Noy et al., 2000). An

ontology contains a set of concepts within a particular domain and shows how the

concepts are interrelated. One of the uses of ontologies is management of knowledge.

Simulations are used for studying a particular system like a waste management system.

They contain knowledge about a specific process in a particular domain. A simulation in

the area of solid waste management contains concepts like bacteria, solid waste, and

acetic acids.

Utilizing ontologies for managing model and simulation knowledge facilitates

representing this knowledge in an explicit manner. An ontology provides the model

semantics, which allows a computer to interpret concepts in an automated manner (Lacy

and Gerber, 2004). The construction of ontologies encourages the development of









conceptually sound models, more effectively communicates these models, enhances

interoperability between different models, and increases the reusability and sharing of

model components (Reitsma and Albrecht, 2005). It also provides assistance in

computation by structuring data (Altman et al., 1999).

Web Ontology Language, OWL (OWL, 2005), can be used for describing the

ontology of a solid waste management process called Sequential Batch Anaerobic

Composting (SEBAC). The Owl:Class is used for describing generic concepts like

bacteria while the specific instances like propionate bacteria are modeled as the

Owl: Individual. The Owl:Property is used to define a property of a concept. Two types

of properties have been used to model the relationships. The Owl:Obj ectProperty models

relationships between individuals while Owl :DataTypeProperty models relationship

between individuals and data values. Each property has a domain and range. For

example, the concept bacteria has a property called "acts on" which is used to describe

the interaction of bacteria with fatty acids. The "acts on" property is defined with

bacteria class as domain and fatty acid class as range..

Applications of Ontologies in Simulation

The notion of combining ontologies with simulation has received much attention in

recent years (Fishwick and Miller, 2004; Lacy and Gerber, 2004; Miller et al., 2004;

Raubal and Kuhn, 2004). This section explores several different ways in which

ontologies can be applied to simulation, and in particular how ontologies can solve some

problems in current methods of building simulations for agriculture and natural resources.

Model base

Many biochemical and physiochemical processes in waste management are

fundamental and well studied. For example, anaerobic digestion process has been studied









and used for treating wastewater and solid waste. Many different anaerobic digestion

models have been built over the years, but their uses by engineers, waste management

operators, and process technology providers has been very limited (Batstone, 2002). The

International Water Institute has established an anaerobic digestion modeling task group

for developing a generalized anaerobic digestion model for achieving extended usage of

anaerobic process knowledge generated by research activities and operational experience.

The development of such a generalized model has many advantages. It will increase the

application of models for plant design, operation, and optimization. The common

vocabulary in the form of a general model will also facilitate future model development

and transfer of technology from research to industry.

Similarly, there are many crop models, but there is no comprehensive management

system for managing all these models. Research is being done to develop a suite of crop

models for a variety of crops and integrate these crop models with weeds and insects

models (Agriculture Research Service, 2005). Many other crops can be modeled by

assembling modules from available models and changing few parameters and rate

equations. However, having so many different yet similar models causes problems in

managing models and in sharing model components among developers. There is

unnecessary redundancy resulting from poor communication among developers. For

example, there may be as many as two dozen irrigation models that all basically operate

on the principles of water balance. They may use similar ways of calculating processes

such as evapotranspiration, or they may use different equations to achieve the same

results. Unfortunately, the traditional methods for creating these models make it very

difficult to compare the models to see how they are similar or different.










An ontology can be used to build a database of models, that is, a "model base", that

can help to classify different but similar models and that can be searched to locate models

and model components suitable for a particular application. Each specific model can be

represented by an instance in the ontology and abstract model structure and behaviors

represented as classes. Similar models can be grouped together into a class, and

neighboring related classes grouped together to form subclasses. At the top of the

resulting taxonomy would be generic modeling approaches. If an ontology is also used to

represent the internal structure of a model, then model internals can be compared in an

automated fashion to determine which parts of the model are similar and which are

different.

The vast collection of models and model components resulting from this analysis

would create a large but organized taxonomy. This taxonomy could be searched using

query processors based on ontology reasoners (as explained in section "Reasoning") to

locate models (and model components) of interest. It can also be used to compare and

contrast two models and explicitly identify how they are different or similar.

System structure

System structure can take many forms including a geometric structure, a chemical

structure, or a physiological structure. The use of object-oriented design for analysis of

system structure is well known and is one of the first applications of obj ect-oriented

programming dating back to the 1960s. The biological and physical systems in

agriculture and natural resources are analyzed in this fashion by decomposing a complex

system into simpler interconnected parts and subparts. Modular, object-oriented designs

are widely used (Beck et al., 2003; Kiker, 2001). Of course, traditional obj ect-oriented

design uses programming languages such as Java or C++ as a representation language.










Using an ontology is the next step in this approach (Fishwick and Miller, 2004). There

are several advantages to elevating the obj ect comprising the system to the status of

ontology objects. For one, the model description and behavior is forced to be done in an

entirely declarative fashion (representation based on concepts and relationships).

Ontologies do not utilize methods or program code to represent the behavior of model.

By using ontology obj ects, model components can be classified and interrelated based on

their meaning. System structure is made explicit in a way that can be exploited by

ontology reasoners in order to compare and contrast model structures.

Representing Equations and Symbols in a Model

Model behavior can be described entirely using mathematical equations (Cuske

et al., 2005). Equations are composed of symbols, and each of these symbols can be

represented as a concept in the ontology. This enables the symbol's meaning to be more

exposed and accessible to analysis and manipulation than if the symbols were encoded as

a computer program. Whereas equations describe the quantitative behavior of variable,

the variables are also symbols, and the things the symbols represent can be made explicit.

Furthermore, the basic mathematical operators can also be treated as symbols and

described in the same fashion.

Equations can be stored in the ontology by representing them as tree structures.

For example the formula:

NH4' = Nt NH3 (Equation 4-1)

can be expressed using the tree structure in Figure 4-1. The tree is rooted on the equal

symbol, and equal has a left side and right side which are the first two branches in the

tree. Operators, such as minus, are nodes in the tree with subtrees for each of the

operator arguments. Each node in the tree, including operators and variables, become









concepts in the ontology. Each concept includes associations to related concepts, for

example "minus" contains associations to the concepts being subtracted.
















Nt NHs



Figure 4-1. Representation of equation 4-1 as a tree structure

The advantage of better defining symbols appearing in equations is improved

interoperability of concepts and associated symbols appearing in different models. In

addition, with the inclusion of basic operators, the ontology can classify groups of

equations and organize them taxonomically from generic forms to specific applications.

This will lead to discovery of similarities in forms of equations used in different models,

and will help to communicate among different modelers (Altman et al., 1999).

While an ontology is a valuable tool for representing the meaning of the symbols

appearing in equations, it has no facilities for solving equations or even performing

simple arithmetic operations needed to do simulation. Although it is possible that an

ontology language such as OWL could be extended to support analytical equation

solving, this area has not been explored and goes beyond the scope of ontology reasoners.

Instead, whereas the ontology acts as an excellent library for equations and their symbols,









external facilities are needed to solve the equations. An external code generator can take

equation structures that are stored in the ontology and produces XML, or program code in

C++ or Java (or other languages) that can implement the simulation.

Reasoning

The power of ontologies lies not only in their ability to provide declarative

representations of concepts and their relationships, but also the ability to automatically

reason about those concepts. Basic reasoning facilities include ontology validation,

automatically determining subsumption relationships (determining if class A is a subclass

of class B), and classification (automatically determining the location of a new class

within the class taxonomy). Extended facilities included automatic clustering

(conceptual clustering) of concepts, and analogical reasoning or similarity-based queries

and case-based reasoning. These facilities can be applied to simulation in order to

automatically classify models, model components, and the equations and symbols used in

the models. Query facilities based on reasoning would help to locate simulation elements

within a large collection. Clustering techniques can compare the structure of two models

and tell how they are similar or different.

For example, the knowledge in an ontology of solid waste management domain can

be used for automatically generating equations based on physio-chemical equilibrium

laws. A particular law can be applied based on the specific property of an individual

symbol. In the SEBAC simulation fatty acids dissociate into fatty acid ions based on a

physio-chemical equilibrium law, and that law is represented by an equation. The

reasoner can automatically instantiate an equation corresponding to the law when it finds

that an individual of the fatty acid class has a property called "in equilibrium with" and









the range of the property is fatty acid ion. It would use the particular properties of the

individuals involved to parameterize the equation

Generating and Integrating Documentation and Training Resources

If the ontology is part of a complete database management system, the ontology

can store and organize any content, including multimedia content in the form of rich text,

images, 2D/3D animations, and video. In the context of simulation, this creates a

complete environment for all information associated with the simulation. In particular,

all research materials (experimental procedures, raw data, statistical analysis, technical

reports, journal articles) and educational resources (training-based simulations, scenario

training, case studies) can be integrated.

How to Build an Ontology-Based Simulation: Bioprocessing Example

Sequential Batch Anaerobic Composting (SEBAC) is an anaerobic digestion

process that decomposes organic matter into methane and carbon dioxide by a series of

reactions in the presence of several microorganisms. The details of the SEBAC are

explained in the section "Domain Studied" of chapter 2. A mathematical model was

developed to understand the SEBAC system and to study the response under various feed

conditions (Annop et al., 2003). The model consists of a set of differential equations,

which have been constructed based on mass balance and physio-chemical equilibrium

relationships. This study did not implement the tools (SimulationEditor and

EquationEditor) but used these tools for developing ontology-based simulation for the

SEBAC process. The steps in building the SEBAC model based on ontology are as

follows:









Collection of Relevant Documents

The first step in building an ontology-based simulation was to collect all relevant

documents such as technical papers of the system and any existing related models. In the

case of the SEBAC simulation, an existing model had already been implemented using

Matrix Laboratory (MATLAB) (Lai, 2001),. Available documents included a graduate

thesis describing the variables and equations used in the model (Lai, 2001), a research

publication describing the implementation of the mathematical model (Annop, 2003), and

source code of the MATLAB implementation.

It would have been useful to have access to a conceptual model for understanding

the conceptual schema of the system. A simple conceptual model of the SEBAC process

was sketched for understanding the SEBAC domain. Figure 4-2 shows the conceptual

model with nine concepts (Owl:Class) and three types of interactions or relationships

(Owl:Obj ectProperty). These concepts have individuals which can be mapped to the

variables used in the simulation. There were six individuals of bacteria and six

individuals of fatty acids in the SEBAC system which could be mapped to the state

variables of the model.

Define Model in Terms of Elements

The next step was to define the model in term of elements. Elements were used to

modularize the model into logical units. Related classes, individuals, properties and

equations were entered in a particular element. The description of the model in terms of

elements was helpful in understanding the structure of the model. Typically, a modeler

designs a particular model by creating a graph containing elements and links indicating

the information flow between elements. SimulationEditor (Figure 4-3) was used for

building the model structure in the form of an element graph. SimulationEditor also










contained facilities for automatically generating and running simulations and generating

reports.

The SEBAC simulation involved a biological process. The simulation was

described in terms of elements which captured the important processes like bioconversion

of fatty acids and substrate and dissociation of fatty acids. Figure 4-3 shows the elements

of the SEBAC simulation and gives an overview of the SEBAC process including various

transformations that occur during the process.


Figure 4-2. Conceptual model of the SEBAC system

Identifying Classes, Individuals and Properties

After defining the general elements of the model, specific concepts in the model

were identified. For the SEBAC system, the concepts were identified from the list of

variables used in the model (Lai, 2001). From these, the following classes with the























































Figure 4-3. SimulationEditor diagram for SEBAC process showing elements of SEBAC
simulation and showing various transformations that occur during the process

Some of these classes had several individuals. For example, there were three

individuals of fatty acid ion, and each fatty acid ion had a specific value of equilibrium

constant for dissociation and conversion factor. Relevant classes, individuals, and


corresponding properties (constants, parameters, yield coefficients and variables) were

created:

* Reactor: liquid volume, gas head space, reactor temperature
* Fatty acid ion: equilibrium constant for dissociation, conversion factor
* Fatty acid
* Bacteria: biomass death rate, half velocity constant, maximum growth constant
* Methane
* Carbon dioxide
* Soluble substrate and insoluble substrate


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66



properties were entered in a particular element. Figure 4-4 shows how an individual


called "Ammonium ion" was entered into the ontology database. The other classes,


individuals and properties were entered into the database in a similar fashion.







I~utos Symbols Units

Symbols Symbol


qulbimconstant for dissociation
-Jtogen yield
I oal Nitrogen
Ioal nitrogen formation rate
lirgncontent


~..1 : 1- I'***** 'I*~ i.r v SymbollD0


I Eglsh1 Sansh//German
The SEBAC process creates ammornum ion (NH4 ) equal to the-
difference of total nitrogen (14) and amm onia produced(NH3 )


Enable Index Symbol I- select SymbollD v

Source Type: Equation : RI I- Condition# v
O constant |0 o
Value Type : ~ IScalar O Matrix : Dlmenslan I as Iconstant V


New
Delete


Show all Equations/Symbols for:
SSEBAC Nitrogen content
O SEBAC


Save





Figure 4-4. Interface of EquationEditor to input the concepts in a particular element of
the simulation


In conventional modeling languages, the meaning of the symbols and the


relationships between the symbols are not defined explicitly. For example, the SEBAC


model had symbols for various forms of nitrogen such as ammonia, nitrate, and


ammonium ion, but the simulation written in MATLAB does not explicitly specify the


relationship between these forms of nitrogen or the meaning of each form. The meaning










of the symbols and relationships can be defined explicitly using an ontology. Figure 4-5

shows a portion of the ontology for different forms of nitrogen.




e i... ed .........idor....-
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Nitrous Oxide gas Nitrogen Dioxide


Hydoge io P in eqluilibrium withflmoi Ntrg

Ammonium ion v
<^} Hydrogen

consiss of onsists of




Total dissolved nitrogen




Figure 4-5. Ontology for different forms of nitrogen

In the SEBAC model, total dissolved nitrogen was found in the form of ammonia

which in turn could be found in two forms: ammonium ion (NH4 ) and dissolved

ammonia gas (NH3). In Figure 4-5 there is a relationship called "consists of' with a

domain of total dissolved nitrogen and a range of forms of ammonia (NH4 N3).

Ammonium ion concentration was calculated by the difference of total dissolved nitrogen

and ammonia. NH4' and NH3 were in equilibrium, and their concentration is given by

the equation:

NH4' t* NH3 + H' (equation 4-2)











Figure 4-5 displays a property called "in equilibrium with" having NH4+ aS a

domain and NH3 as a range. This property modeled a reversible conversion between the

two forms of ammonia (NH4+ and NH3). Ammonia was defined as a specific kind of gas,

so it was also a subclass of the class gas.

Define Equations

The equations describing dynamic behavior were entered in the system after

entering the classes, individuals, and properties of symbols which were used in the


equation. Figure 4-6 shows the interface of EquationEditor for entering an equation that

represents the relationship between total dissolved nitrogen, ammonia, and ammonium

10n concentration.


Equations Syrrbols //Units





mmnaequation
Nirgncontenrt rate equation NH4_plus = Nt NH3
Nirgnformation rate equation
Nirgnrate equation


New
Delete


Show all Equations/Syrrbols tor:
SSEBAC Nitrogen content
O SEBAC


Figure 4-6. Interface of the EquationEditor for entering equation

An equation models the dynamic relationship between concepts (classes) and

represents a statement of a specific law. The Michaelis-Menten equation (Heidel and

Maloney, 2000) models a relationship between acid and bacteria. In the SEBAC system,

Acetic acid is an individual of the acid class and acetolistic methane bacteria is an


Eili O










individual of the bacteria class. The acetolistic methane bacteria acts on acetic acid, and

this relationship can be modeled by Michaelis-Menten equation. These relationships can

be explicitly shown in ontology as properties. It is also possible to store the specific laws

in the ontology so that the equations can be automatically generated based on the specific

relationships between individuals by using an ontology reasoner.

Enter the Initial Values of State Variables

Initial values of state variables were entered manually using an input form which

was generated automatically based on the logic that each differential equation has a state

variable and that an initial value was required for each state variable. The SEBAC

simulation has twenty one state variables, so the input form has twenty one text Hields.

The value of constants (like the universal gas constant) and other parameters used in the

simulation were stored in the ontology as properties of individuals representing these

constants.

Generating Program Code for Implementing the Simulation

Program code for running the simulation was automatically generated by

processing the descriptions of model structure and behavior (equations) stored in the

ontology. Currently, the system generates Java code, but other languages can also be

supported. The code generation involved retrieving equations and symbols belonging to

each element in the ontology database and making a reference list of symbols having the

hierarchical structure of operators in each equation. A Java class was generated for each

element of the simulation (mainly to partition the code into logical modules). The

symbols for variables belonging to an element were generated as member variables in the

Java class while the equations were generated as Java methods. Each method returned a

value for a particular variable based on an equation defined for that variable. For









example, a Java method was generated corresponding to the ammonia balance equation,

as shown in Figure 4-6, that returns the value of NH4

Execution of Simulation

After generating the Java code, the code was compiled and the simulation was

executed. The simulation results were presented in the form of charts and tables. In

order to enhance interpretation, the results of the simulation could also be presented as an

animation. The dynamics of the SEBAC simulation were shown in term of reactors that

change colors based on pH and other chemical properties of the system (Figure 4-7). The

ontology facilitated creating these animated interfaces by storing graphic obj ects as

described in chapter 3 that could be used to render an animation along with the

associated model concepts.


SEBAC Simulation
ti115 I fillH fillH 0115l


Figure 4-7. Interface for presenting results of SEBAC simulation using animation









Conclusions

This chapter explored several ways in which ontologies can be applied to

mathematical modeling. As an example, an ontology based simulation was developed in

the bioprocessing domain. The development process involved seven steps including

collection of relevant documents; defining the model in terms of elements; identifying

classes, individuals, and properties; encoding equations; entering initial values of state,

constant, and other parameters; generating code; and executing the simulation.

The development of an ontology for simulation models explicitly exposes

knowledge contained in models at a higher level. This knowledge can be further used for

constructing conceptual models, simulations of similar systems, and educational and

training materials. The construction of an ontology will allow better communication of

knowledge about models, model elements, and data sources among different modelers; It

will enhance interoperability between different models, increase the reusability and

promote sharing of model components.















CHAPTER 5
CONCLUSIONS, CONTRIBUTIONS, AND FUTURE DIRECTIONS

Conclusions

Documenting Research Information

The presented work showed that the content management approach (i.e., using a

database to store research information) can be used for documenting research

information. The information was first structured as an ontology (structured information)

and stored inside an obj ect database (an ontology management system). This approach

allowed the documentation of research at a very fine level (i.e., documenting research at

the level of concepts used in various research proj ects) instead of storing the educational

materials at only a course level in the form of documents, presentations or other formats

that fail to explicitly represent content. There can be an overlap of concepts used in

various proj ects, and the overlap of concepts can be used for identifying similarities in

various proj ects. For example, both SEBAC proj ect and BMP proj ect used the concept of

anaerobic digestion, so the overlap of concepts in ontology can infer the similarity in

BMP proj ect and SEBAC proj ect.

Methodology for Generating Educational Material by Reusing Information

The structured information was used for creating a variety of educational materials

such as websites, animation and reports. A JSP application was developed for creating

Web pages from an ontology and a Java animation was developed for explaining the

dynamic processes used in the research proj ects. The concepts used in the dynamic

process were stored in the database, and these concepts can also be accessed as a website.









It was shown that the ability to reuse information in a variety of formats has the following

advantages:

* Duplication of efforts is minimized: The content management approach decreases
the cost and time for producing educational materials.

* Enforcement of information integrity: Using an ontology as a single source of
information enforces information integrity within an organization like ES CSTC.
The information can be updated and verified at a central location (ontology
database) instead of checking the accuracy of information in various formats.

* Separation of presentation and content: The presented approach allows the
separation of content from presentation which allows updates or modifications in
presentation without changing content and vice versa.

Presenting Dynamic Information of a Lab Exercise as Educational Simulation

Based on the results of the evaluation of simulations, it can be concluded that the

simulation can be used to effectively teach a lab exercise. The effectiveness of

simulations also depends on the approach of integrating simulations in the instruction,

that is, the simulations can be either shown to the students as a demonstration or students

can run the simulations on their own machine. The confidence of a student in the lab

concepts increases when the student ran the simulation on his/her computer. The use of

simulation helps in teaching the lab where it is practically infeasible to teach the lab as a

hands-on approach because of the high cost of the equipment and chemicals involved.

Simulations can also serve as a replacement experience for universities and colleges that

do not have a waste management laboratory. The computer-based simulation can also be

used to augment the real laboratory experience.

Representing Knowledge of a Mathematical Model by Ontology

The development of an ontology for mathematical models explicitly exposes

knowledge contained in models at a higher level. This knowledge can be further used for

constructing conceptual models, simulations of similar systems, and educational and










training materials. The construction of an ontology will allow better communication of

knowledge about models, model elements, and data sources among different modelers;

enhance interoperability between different models; and increase the reusability and

sharing of model components.

Contributions


* This proj ect illustrated a content management approach combining an ontology and
database for structuring and storing content. It facilitated the development of
simulations and other educational resources. This approach was used for
developing educational materials in the domain of waste management technologies
at ES CSTC.

* The dynamic information of a proj ect (process, lab exercise) was presented as a
simulation. The interactivity of the medium was beneficial in showing the concepts
effectively. The simulations were evaluated in classroom settings at the University
of Florida.

* A library of Java classes was developed during the generation of simulations.
These Java classes can be reused to create similar simulations.

* An ontology for three proj ects was created for generating educational material in
the domain of solid waste and wastewater. The ontology can be used in generating
reusable learning material.

* The process of generating an ontology for a simulation or mathematical model
revealed a new approach of representing models in terms of concepts and
relationships between concepts.

Future Directions

Ontology-Based Instruction Design

This study focused on representing the content of educational material as an

ontology. During the development of the website, it was realized that the instructional

design could be a next step for representing the concepts of ontology in a learning

context. An ontology of instructional design can be integrated with an ontology of

research projects for developing courses (Sepulveda-Bustos et al., 2006). Furthermore,









future work can also be focused on developing a SCORM compliant course using domain

ontologies that were created during this proj ect.

Ontology Reasoning

The presented proj ect did not focus on ontology reasoning, which is an important

function of ontologies. The ontology reasoners could be used for selecting appropriate

teaching method based on the ontology of instructional design and validating

instructional material. Future work can also involve application of reasoners in

modeling. One of the application of reasoners is generation of equations based on

relationships between different concepts in a model.

Development of Tools for Developing Online Lesson

The generic tools used in the presented project were appropriate for developing

ontologies but were not specifically designed for producing online lessons. Future work

should also be focused on developing custom authoring tools that can more rapidly

address instructional design issues because they support features tailored specifically to

development of educational materials.
















APPENDIX A
EVALUATION FORM OF BMP SIMULATION

Name :
Student #

Section 1. evaluation: Please do the evaluation after reading the lab handouts or
performing virtual simulation.

Part a: Technical evaluation (multiple choice)
1. Objective of the experiment?
1. To estimate biochemical methane potential of a substance and estimate the rate
of anaerobic degradation
2. To measure biochemical methane potential
3. To determine Aerobic digestibility
4. To measure Aerobic digestibility

2. How many stock solutions are used?
1. 6
2. 7
3. 8
4. 5

3. Inoculum is added at --- degree C.
1. 29
2. 30
3. 32
4. 35

4. What is difference between aspirator bottle and serum bottle?
1. Aspirator bottle is used for making medium and serum bottle is used for storing
stock solution.
2. Serum bottle is used for making medium and Aspirator bottle is used for storing
sample
3. Aspirator bottle is used for making medium and serum bottle is used for
containing the bioassay
4. Aspirator bottle is used for storing stock solution and serum bottle is used for
storing sample

5. Purging is done with which gas?
1. N2
2. H2










3. 02
4. N2 CO2

6. Purging is done for removing which gas?
1 N2 CO2
2. H12
3. 02
4. N2

7. What is in the inoculum digester?
1 Anaerobically digested dog food and mixed cultured bacteria
2 Aerobically digested dog food, mixed cultured bacteria and water
3 Aerobically digested dog food, and water
4 Anaerobically digested dog food, mixed cultured bacteria and water

8. What volume of inoculum is added to the media?
1. 20% byvolume
2 10 ml
3 40 % by volume
4 50 ml

9. What does heated copper column do?
1 Absorbs CO2 from the gas stream
2. Absorbs 02 from the gas stream
3. Absorbs N2 from the gas stream
4. Absorbs N2-CO2 from the gas stream


10. What are the functions of Na2S and Reazurin
1. Na2S is redox indicator and Reazurin is a reducing agent
2. Na2S is reducing agent and Reazurin is a reducing agent
3. Na2S is reducing agent and Reazurin is a redox indicator
4. Na2S is redox indicator and Reazurin is a redox indicator


Part b: Subjective evaluation
Please rate each of the following in connection to your experience of BMP lab from 1 to
5 where: 1 is the lowest priority, and 5 is the highest priority.
12345

1. I am encouraged to learn
2. I am confident with the concepts used in BMP lab.
3. Enabling student to work through course materials at their own pace
4. Developing student's creativity and skills
5. Applying what you are learning to "real world" situations
6. Teaching students to work together










To what extent do you agree or disagree with each of the following statements regarding
the BMP lab:
(select only one response per question)
Strongly Agree (SA), Agree (A), Disagree (D),,Strongly Disagree,(SD), Not
Applicable(N)

7. The learning experience was interactive
8. I had a good learning experience















APPENDIX B
EVALUATION FORM OF MAPR SIMULATION

Name:
Student I.D.:

Virtual experiment evaluation: Please do the evaluation after reading online lesson and
performing simulation (animation)

Part a. Objective Evaluation

Evaluation of Online Lesson
1. The semiconductor used in photocatalysis is...
1. Barium ferrite
2. Titanium dioxide
3. Silica
4. Activated carbon
Ans:

2. What is the primary oxidant in photocatalysis?
1. Hypochlorous acid
2. Hydrochloric acid
3. Hydroxyl radical
4. Hydroxyl ion
Ans:

3. What are the organic molecules converted to when oxidation is complete?
1. UV light
2. Carbon dioxide, water, and mineral acids
3. A polymer
4. Sulfur dioxide and nitrous oxide
Ans:

4. Which type of electron is excited to form an electron/hole pair?
1. A conduction band electron
2. A valence band electron
3. A hot electron
4. All of the above
Ans:










5. The purpose of magnetic agitation is to
1. Activate the catalyst
2. Begin the breakdown of the contaminant
3. Maximize photocatalysis
4. Keep the system stable
Ans:




Evaluation of Simulation
1. The alternating current magnetic field is generated by
1. Solenoid
2. UV light
3. Wastewater
4. all of the above
Ans:

2. Maximum performance of the MAPR is at what solenoid frequency
1. 20 Hz
2. 80 Hz
3. 120 Hz
4. Performance is same between 20 and 80 Hz
Ans:

3. Which of the following is *not* used in MAPR experiment
1. Heater
2. UV light
3. Frequency generator
4. Spectrophotometer
Ans:

4. What is one of the methods used for determining the extent of photocatalysis?
1. Counting by hand
2. Spectrophotometer
3. MAPR
4. Inductively coupled plasma
Ans:


5. Which of the following is true about MAPR process ?
1. Only magnetic agitation is required for effectively treating wastewater
2. There is no effective treatment of wastewater without UV light
3. There is no effective treatment of wastewater without magnetic agitation
4. Effective wastewater treatment requires UV light and magnetic agitation
Ans:












Part b: Subjective evaluation
Please rate each of the following in connection to your experience of MAPR lab
from 1 to 5 where: 1 is the lowest priority, and 5 is the highest priority.
12345

1. I am encouraged to learn ...
2. I am confident with the concepts used in MAPR lab....
3. Enabling student to work through course materials at their own pace....
4. Developing student's creativity and skills.....
5. Applying what you are learning to "real world" situations.....
6. Teaching students to work together....

To what extent do you agree or disagree with each of the following statements regarding
the MAPR lab:
(select only one response per question)
Strongly Agree (SA), Agree (A), Disagree (D),,Strongly Disagree,(SD), Not
Applicable(N)

7. The learning experience was interactive....
8. I had a good learning experience.....











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Full Text

PAGE 1

DEVELOPING EDUCATIONAL MATERIALS IN BIOPROCESSING USING AN ONTOLOGY DATABASE MANAGEMENT SYSTEM By ROHIT BADAL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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Copyright 2007 by Rohit Badal

PAGE 3

To my mother

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ACKNOWLEDGMENTS I would like to sincerely th ank my advisor, Dr. Howard Beck, who has helped me by replying to many late-night and weekend emails and has provided ample time to me for guidance. He taught me ontology, object database, Java, and other topics relevant to my research. I also want to thank him because I got an opportunity to know him in person. I feel lucky and proud to be his student, because Dr. Beck is not only a good researcher but also a good human being. I would also like to thank the director (Dr. William Sheehan), researchers (Dr. Dave Mayzyck, Dr. Art Teixeira, and Dr. Dave Chynowyth )and graduate students (Patrick, B eau, and others) of ES CSTC for their valuable help. Secondly, I would like to thank my committee members who have given appropriate guidance and their valuable time. Dr. Fedro Zazueta introduced the concepts of learning object, and I am very thankful to him. Dr. Joachim Hammer has guided me to see the role of ontology and database in my research; so, I really appreciate his comments. Dr. Art Teixeira has been a great help, because he guided me in developing additional educational simulations for showing various aspects of process. Dr. Roger Nordstedt has given valuable advice by seeing th e usability of my work from an end-user perspective. I would like to thank my friends (Shantanu Mishra [Golu], Jairaj Payyapalli [Paya] Soonho Kim, Yunchul Chris, Bruno, Shiva, a nd Frank Barone) at UF. Also, I enjoyed my time at Transcendental Meditation Center; so, I would like to thank Dr. Alcine Potts and Patricia. Krishna Lunch has been a grea t place to meet friends and have food, so iv

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many thanks to them. Also, I would like to thank Dr. Jiannong Xin, Danielle, and Dr. Petraq Papajorgji for t ea and valuable advice. Many thanks go out to my parents, Saroj Badal and R.S Badal; my brother, Rahul Badal; my sister, Rachna Badal; our pet mo tu and other members for their support and love. Lastly, I would like to thank th e source of all happiness and good will. v

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF FIGURES ...........................................................................................................ix LIST OF ACRONYMS .....................................................................................................xi ABSTRACT .....................................................................................................................xiii CHAPTER 1 INTRODUCTION........................................................................................................1 Statement of Problem ...................................................................................................1 Duplication of Efforts ............................................................................................2 Unstructured content of the educational material ...........................................4 Lack of separation between presentation and content....................................4 Lack of Knowledge Reuse between Re search and Educational Materials ............5 Appropriate Format for Pres enting Educational Material .....................................5 Specific Objectives .......................................................................................................6 Approach .......................................................................................................................7 Other Related Projects for Ma naging Research Information .......................................9 E-Science ...............................................................................................................9 Austrian Research Information System Project ..................................................10 Dissertation Layout .....................................................................................................11 2 CONTENT MANAGEMENT AP PROACH FOR DEVELOPING EDUCATIONAL MATERIAL..................................................................................13 Introduction .................................................................................................................13 Domains Studied .........................................................................................................14 Solid Waste Treatment ........................................................................................14 Wastewater Treatment .........................................................................................15 Rational for Structuring and Re using Information of ES CSTC ................................16 Ontology .....................................................................................................................17 Literature Review .......................................................................................................17 Computer-Based Instruction ................................................................................17 Content Management Systems ............................................................................19 vi

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Learning Objects .................................................................................................19 Shareable Content Object Reference Model (SCORM) ......................................21 Efforts in Managing and Reus ing Content Using Ontologies .............................21 Generating Presentations from Content ...............................................................24 Content Management Approach .................................................................................25 Components of a Content Management System .................................................26 Ontology ..............................................................................................................27 Methodology of developing an ontology.....................................................28 Tools for creating an ontology .....................................................................29 Database System ..................................................................................................31 Presentation Generator ........................................................................................32 Java server page technique ...........................................................................32 Java applet technology .................................................................................34 Generated Educational Materials ........................................................................34 3 EDUCATIONAL SIMULATION: AN APPROACH FOR PRESENTING DYNAMIC INFORMATION OF A PROCESS........................................................37 Introduction .................................................................................................................37 Virtual Lab ..................................................................................................................37 Literature Review on Virtual La bs and Educational Simulations ..............................38 Methodology of Creating Educational Simulations ...................................................39 Ontology Development .......................................................................................39 Development of Java Classes ..............................................................................40 Development of Educational Simulation .............................................................40 Results .........................................................................................................................42 Simulation for Solid Waste Treatment ................................................................42 Bioprocess lab (BMP lab) ............................................................................42 SEBAC simulation .......................................................................................43 Simulation for Wastewater Treatment .................................................................46 Evaluation of Simulation............................................................................................47 Evaluation of Solid Waste Treatment Simulation ...............................................47 Evaluation of Wastewater Treatment Simulation ................................................50 Conclusions .................................................................................................................51 4 AN ONTOLOGY-BASED APPROACH TO MATHEMATICAL MODELING....53 Introduction .................................................................................................................53 Literature Review .......................................................................................................53 Problems in Developing Simulations ..................................................................53 Possible Solution for Communicating Knowledge of a Model ...........................55 Applications of Ontologies in Simulation ...........................................................56 Model base ...................................................................................................56 System structure ...........................................................................................58 Representing Equations and Symbols in a Model ......................................................59 Reasoning ...................................................................................................................61 Generating and Integrating Docume ntation and Training Resources .........................62 vii

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How to Build an Ontology-Based Simulation: Bioprocessing Example ....................62 Collection of Relevant Documents ......................................................................63 Define Model in Terms of Elements ...................................................................63 Identifying Classes, In dividuals and Properties ..................................................64 Define Equations .................................................................................................68 Enter the Initial Values of State Variables ..........................................................69 Generating Program Code for Implementing the Simulation ..............................69 Execution of Simulation ......................................................................................70 Conclusions .................................................................................................................71 5 CONCLUSIONS, CONTRIBUTIONS, AND FUTURE DIRECTIONS..................72 Conclusions .................................................................................................................72 Documenting Research Information ....................................................................72 Methodology for Generating Educationa l Material by Reusing Information .....72 Presenting Dynamic Information of a La b Exercise as Educational Simulation 73 Representing Knowledge of a Mathematical Model by Ontology ......................73 Contributions ..............................................................................................................74 Future Directions ........................................................................................................74 Ontology-Based Instruction Design ....................................................................74 Ontology Reasoning ............................................................................................75 Development of Tools for Developing Online Lesson ........................................75 APPENDIX A EVALUATION FORM OF BMP SIMULATION.....................................................76 B EVALUATION FORM OF MAPR SIMULATION .................................................79 REFERENCES ..................................................................................................................82 BIOGRAPHICAL SKETCH .............................................................................................90 viii

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LIST OF FIGURES Figure page 2-1 Components of a content management system used for developing educational materials ...................................................................................................................27 2-2 Schematic of Web Taxonomy showi ng a portion of the Biochemical Methane Potential (BMP) ontology ........................................................................................30 2-3 Schematic of Object Editor showing a lis t of equipment and r eagents used in the BMP lab and the relationship between them ............................................................31 2-4 Website generated by the content management approach ........................................35 3-1 Interface for the BMP laboratory for dete rmining biodegradability of a sample in movie mode ..............................................................................................................41 3-2 Interface for the BMP laboratory for dete rmining biodegradability of a sample in interactive mode .......................................................................................................42 3-3 Interface of the Sequential Batch Anaerobic Composting (SEBAC) process for treating solid waste in movie mode. .........................................................................44 3-4 Interface of the SEBAC process for treating solid waste in interactive mode .........44 3-5 Interface of the SEBAC pr ocess with three reactors for treating solid waste in movie mode (ES CSTC Educa tion and Outreach Website, 2006) ...........................45 3-6 Interface of the SEBAC pr ocess with a single reactor for showing the process of clogging (ES CSTC Education and Outreach Website, 2006) .................................46 3-7 Interface of the Magnetic Agitated P hotocatalytic Recator (MAPR) laboratory for treating a sample of wastewater in movie mode .................................................47 3-8 Interface of MAPR laboratory for treating a sample of wastewater in interactive model ........................................................................................................................48 3-9 Overall subjective experience of the students by two teaching methodologies for the BMP lab evaluation ............................................................................................49 4-1 Representation of equation as a tree structure ..........................................................60 ix

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4-2 Conceptual model of the SEBAC system ................................................................64 4-3 SimulationEditor diagram for SEBAC process showing elements of SEBAC simulation and showing various transfor mations that occur during the process ......65 4-4 Interface of EquationEdito r to input the concepts in a particular element of the simulation .................................................................................................................66 4-5 Ontology for different forms of nitrogen .................................................................67 4-6 Interface of the EquationEditor for entering equation..............................................68 4-7 Interface for presenting results of SEBAC simulation using animation ..................70 x

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LIST OF ACRONYMS ADL Advanced Distributed Learning AICC Aviation Industry Computer Based Training Committee ASP Active Server Pages AURIS-MM Austrian Research In formation System Multimedia Extended BMP Biochemical Methane Potential CERIF Common European Research Information Format CMS Content Management Systems DARPA Defense Advanced Research Projects Agency ES CSTC Environmental Syst ems Commercial Space Technology Center HTML HyperText Markup Language IBM International Business Machines IDE Integrated Development Environment IEEE Institute of Electri cal and Electronics Engineers JSP Java Server Pages LCMS Learning Content Management Systems LMS Learning Management Systems LO Learning Object LOs Learning Objects MAPR Magnetic Agitated Photocatalytic Reactor MATLAB Matrix Laboratory M-OBLIGE Multitutor Onto logy-Based Learning Environment OWL Web Ontology Language xi

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RDF Resource Description Framework RMI Remote Method Invocation SCORM Shareable Content Object Reference Model SEBAC Sequential Batch Anaerobic Composting TRP Technology Reinvestment Project UV ultraviolet XML Extensible Markup Language XML FO XML Formatting Objects XSL Extensible Stylesheet Language XSLT XSL Transformations xii

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Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DEVELOPING EDUCATIONAL MATERIALS IN BIOPROCESSING USING AN ONTOLOGY DATABASE MANAGEMENT SYSTEM By Rohit Badal May 2007 Chair: Howard Beck Major Department: Agricultural and Biological Engineering An ontology database management system was utilized for developing an educational outreach program at UF/ES CSTC ( The University of Floridas Environmental Systems Commercial Space Technology Center) with the objective of disseminating research information generated at ES CSTC. The purpose of educational outreach of a research center is to educate the targeted a udience about various aspects of research conducted at the center. Inform ation technology can facilitate educational outreach by supporting and enhancing vari ous functionalities for success of the educational outreach program. A database approach to managing a nd developing educational and training materials (websites, simulations) is presented that utilizes ontologies and object database treatment systems to better manage educatio nal resources and enha nce learning of waste treatment processes. Examples in the ar ea of solid waste treatment and wastewater treatment are presented. An ontology is used to define and organize the concepts in the domain, in this case concepts involving th e biology, chemistry, and physics of waste treatment. A database, rather than files, is used to store and dist ribute concept objects. xiii

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Web-based data visualization tools are used by instructors to devel op and manage course content. Objects can be pr ojected to a number of different presentation formats, including Web sites and printed materials. Evaluation of a 2-D simulation of a bioprocessing experiment showed that We b-based simulation can offer many of the experiences of hands-on laborator y exercises. The immediate advantage of this approach is that educational programs can be more eas ily produced at lower cost compared with conventional tools currently available. xiv

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CHAPTER 1 INTRODUCTION The educational outreach of a research center is an important aspect of disseminating information generated by resear ch center projects and helping different audiences understand a research project. The purpose of educational outreach of a research center is to educate the targeted audience about va rious aspects of the research conducted at the center. Information technol ogy can facilitate edu cational outreach by supporting and enhancing functionalities for the success of the educational outreach program. The educational outreach progr am involves five important tasks: Identifying educational goals and objectives Generating and managing educational conten t that meets goals and objectives Creating educational and traini ng material from the content Disseminating educational materials to different targeted audiences in a suitable format Performing assessment to test the effec tiveness of educational outreach program Statement of Problem The Environmental Systems Commercial Space Technology Center (ES CSTC) is a commercial research center of NASA located at the University of Florida. This study reports on the research performed to deve lop a methodology for creating an educational outreach program at ES CSTC with the obj ective of disseminating ES CSTC research information. The audience to be reached incl uded industries intere sted in adopting ES CSTC technologies as well as other research ers working in the area of waste recovery and instructors teaching waste management courses. The methodology was developed by applying new techniques in database mana gement and object oriented technology to 1

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2 create a repository of educational resources ne eded to disseminate research results and to provide an alternative approach for developing educationa l materials (Badal et al., 2004a). Various challenges are involved in the development of educational materials, and these challenges are descri bed in this section. These problems include the following: Duplication of efforts Lack of knowledge reuse between re search and educational materials Appropriate format for pres enting educational material Duplication of Efforts A research center generates a variety of information in various forms such as websites, research papers, reports, simula tions, and animations. For example, NASA maintains a website for high school student s where the students can find information about a space mission. Conventional tools su ch as PowerPoint (PowerPoint Website, 2006), Adobe Acrobat (Adobe Website, 2006 ), Macromedia Flash (Adobe Website, 2006 ), and HTML development tools (Dream Weaver Website, 2006) are presently used for developing educational resources. Substantial effort and coordi nation are typically required for creating educational and training materials. Several methodologies ha ve been developed for creating educational and training materials, and most of them are based on the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model (McGriff, 2000). The ADDIE model involves five steps: Analysis: The gap between desired lear ning outcome and the existing knowledge and skills of an audience is determined. Design: The specific learning objectives, c ontent, assessment tools, and exercises are documented. Development: The learning materials are created. Implementation: The learning materials are distributed to a specific audience. Evaluation: The learning materials ar e evaluated by a specific audience.

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3 Conventionally, a subject matter expert prov ides content and c oordinates with an instructional designer who designs lessons base d on the content provided. Content refers to the subject or topics covered in an educational program (Onlin e Dictionary Website, 2006). The content is related to the message or knowledge that the user gets from the educational resource. The information tec hnology professional provides information technology tools and support to the subject matter expert and instructional designer. If instructional designer and information t echnology personnel are not available, the instructors develop their own e ducational materials. In any case, most of the steps for developing educational materials, as explai ned in the ADDIE model, must be performed from the start because the instructors have difficulty in reusing existing course materials (Araujo, 2004). Additionally, these steps ar e focused on developing a specific set of educational materials rather than representing the course content in a generic form, like a network of concepts, that would allow the reuse of knowledge in developing a variety of educational materials. It is important to reuse the knowledge because it can decrease the development cost and time while increasing the quality and accura cy of educational materials (Fisher, 2002). The lack of knowledge reuse increases the volume of educational materials, creating a problem fo r managing these materials which in turn increases the cost related to storage and maintenance of knowledge. Reusable knowledge can be used in developing educational materi als in different contex ts and for different audiences (Araujo, 2004). For example, th e MAPR website (MAPR website, 2005) was created for teaching the concep t of photo catalysis applicat ion of titanium dioxide for treating wastewater. This website cont ains many important wastewater treatment concepts which are presented in a specific order so a reader can develop an awareness of

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4 the concepts. The concepts illustrated in the website can also be used in other educational programs, but the reusability of the MAPR website is limited because of the following reasons: Unstructured content of the we bsite/educational material Lack of separation between presentation and content Unstructured content of the educational material The unstructured content is defined as information whose intended meaning is only loosely implied by its form and theref ore requires interpretation in order to approximate and extract its intended meani ng (Ferrucci, 2004), that means, the organization and semantics of information are not defined explicitly. Examples of unstructured content include Microsoft Word documents and PowerPoint presentations. The unstructured information of the website (educational material) creates a challenge in reusing a specific concept in other educationa l materials. Suppose, for example, that a wastewater treatment company is creating a tr aining material for their waste management process, and that they want to teach the concept of photocatalysis (as explained in the MAPR website), but they do not want to teach the concepts irrelevant to their process. The unstructured information of the MAPR website makes it a challenge for the wastewater company to search for the relevant concepts in the MAPR website and decide if the concepts can be used in the compa nys educational and training material. Of course, the content can always be manually ex tracted and reused, but this can be a time consuming and tedious task, especially in large educational programs. Lack of separation between presentation and content The tight coupling of conten t and presentation creates a challenge of updating and managing educational resources (Roure, 2003). Presentation refers to the rendering of

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5 educational resource in a specific format like print (W3C Website, 2006). Separation of content from presentation allows a developer to update the content while maintaining the consistency of presentation. Similarly, the de veloper can change the presentation of an educational program while maintaining the c onsistency of the content. This improves maintainability and facilitates the customization of educational material. Lack of Knowledge Reuse between Research and Educational Materials The information used for educational or training purposes can also be used for research purposes or vice-versa. For exam ple, a researcher can describe a waste management system in a project report using some concepts. These concepts can also be used by an instructor to e xplain the waste management sy stem. Research and learning processes are interdependent, and they contribute to knowledge (Lyon, 2002). The integration of research and educational knowle dge will increase transp arency in research, improve the accessibility of research results, and enhance the development of the educational materials with up-to-date information (Lyon, 2004). However, research knowledge in the traditional form of reports, simulations, or mathematical models is not effectively reused for developing educationa l and training material and vice-versa. Appropriate Format for Presenting Educational Material The presentation of educational material in a particular format is highly crucial. Cognitive information processing and information theory has found that certain formats for presenting information are more familiar to the users than others. The familiarity of a format affects learning because it influences human processing capabilities (Lloyd and Jankowski, 1999). The human visual system has the highest information processing capability (Rohrer, 2000). Cognitive psychologists have described human processing as conscious and pre-conscious. Processing gr aphic information is pre-conscious, which

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6 frees up more conscious processing ability and allows more learning to happen. However, excessive or confusing graphics can hinder learning. Cognitive psychologists have found that multimedia can affect the students learning (Mayer and Moreno, 2002). Mayer has described five princi ples that can be used for teaching scientific concepts to stude nts using multimedia. These principles are: Multiple representation principle: It is be tter to use multiple modes of presentation (like words and pictures ) than a sing le mode (only words or pictures). Contiguity principle: The corresponding words, pictures, and other multimedia information should be presented cont iguously rather than separately. Split-attention principle: Multimedia shoul d be explained by auditory narration instead of a text explanation. Individual differences principle: The multip le representation principle, contiguity principle and split attention principle are mo re important for learners with low level of prior knowledge than learners w ith high level of prior knowledge. Coherence principle: The multimedia expl anation should not use extraneous words and pictures. This study involves the development of educational and training materials for engineering processes used at ES CSTC fo r treating wastewater and solid waste. Engineering processes are dynamic in nature and it is beneficial to present these processes in a suitable graphical format for effective understanding. Specific Objectives Identify available technologies for fa cilitating the documentation of ES CSTC research information, which can allow fo r processing and storage of ES CSTC research information in an appropriate format so it can be shared, accessed, and maintained easily. Develop a methodology for generating a variet y of educational materials (websites, animations, and reports) while avoiding duplication of effort. Present dynamic (simulations, process) a nd static (equipment details) information in a suitable format to a variety of audi ences (high school students, researchers or management professional in the industry).

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7 Investigate a better method of representing knowledge of a mathematical model to allow the use of knowledge for various purposes including the development of educational materials. Approach This study has investigated an approach of developing an educational outreach program utilizing information technology t ools with an objective of reusing and presenting the information explicitly, that is representing information in a structured format such as an ontology. An ontology, an approach to knowledge use/reuse and knowledge sharing (Beck, 2003a), allows the info rmation to be represented as a network of concepts. For example, the details of a la b exercise can be repres ented as a network of concepts like equipment (bottles, pipes, valves), chemicals, and samples used in the experiment rather than a Microsoft Word document. The ontology can be used for assisting in communication between people, attaining interoperability among computer systems, and improving the quality of engineering software systems. A content management system (CMS) is used for developing educational materials. A CMS is a database management system us ed for storing content which includes not only media such as text, images, animations, s ounds, and videos, but also concepts in the form of individual words and phrases, rules, and even mathematical equations (Beck, 2003a). The CMS stores content as ontology. Compared to conventional ways that focus on developing educational materi al in a specific format (P owerPoint, Flash Microsoft Word etc.), this approach allows for a be tter method to organize resources, assist in search and retrieval, and generally promote gr eater reusability and sh aring of content. The CMS also has the ability of automatically generating presentations from a database through a process in which the elements of da tabase objects are mapped to a particular presentation format such as HTML, print, Flash, Java Applet and others. The CMS was

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8 used to generate educational simulations rendered as Java Applets, and Web pages rendered by using Java Serv er page (JSP) technology. Educational simulations were used for de scribing engineering processes. These simulations are run in a virtual environmen t allowing students to operate or manipulate the equipment as well as the simulation proce ss itself. Instructors can show a lab in the form of an animation for explaining various concepts. The students can also change the process parameter to study the behavior of the system. One of the objectives of this project is to present information in a suitabl e format. The interactivity of a simulation increases students learning efficiency (M clean and Riddick, 2004). Another advantage of using simulation is that the student can access and opera te the process anytime and anywhere. Educational simulations have b een used for explaining processes (Navarro and Hoek, 2005), so various simulations were created for explaining waste treatment concepts used in three projects in the area of solid waste and wast ewater at ES CSTC (Badal et al., 2006). The static information (for example, ge ometrical orientation of equipment) was stored in the database and rendered as a webpage. The information rendered as a webpage has links to other relevant info rmation based on the data modeling or the structure of the ontology. The structure of the ontology of projects at ES CSTC will help users to browse project specific knowledge and access relevant information. Any change in the data model or ontol ogy will automatically update the webpage. The static information in the form of reports can also be generated using Extensible Stylesheet Technology (XSL).

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9 Other Related Projects for Managing Research Information Several efforts are taking place for enhanc ing the use of information technology in managing research information. Thes e are described in this section. E-Science One of the efforts is taking place in th e United Kingdom, where E-Science Institute is trying to support and enhance the scient ific process using information technology (Roure, 2003). The aim of the E-Science in itiative is to allow sharing of resources among individuals and institutions in a flex ible, secure, and coordinated manner. EScience refers to the activities performed by a scientific community in a distributed environment using the Internet. These activ ities require access to computing resources for data collection, data analysis, simulation, data visualization, and other relevant information (procedure, standard) used by researchers in conducting experiments. The E-Science project has three layers: da ta/computation layer, information layer, and knowledge layer. The computation layer deals with the task of collecting data (experimental and simulation) and allocating re sources for collecting data This layer involves distributed computing systems. The information layer deals with the task of representing, storing, accessi ng, sharing, and maintaining information. The knowledge layer deals with the process of acquiring, using, retrieving, publishing, a nd maintaining knowledge. This study shares common goals with the E-Science project with respect to information and knowledge layer. However ther e is a significant di fference in the scale of this study the presented work and the E-Science project This study is conducted at the level of a single research center while the E-Science project is conducted at the level of a country (Britain) with the budget of 250 million pounds and has sponsored 100 projects. The content used in E-Science is manually annotated using Extensible Markup

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10 Language (XML) or Resource Description Fram ework (RDF) while the content used in this study is self annotated because it is stored as an ontology in an ontology database management system. The large scale of th e E-Science project poses a challenge for structuring the content as ontology. On the ot her hand, the relatively unstructured nature of the E-Science project content results in reduced ability to u nderstand and reuse the content. Austrian Research Information System Project The Austrian Research Information System Multimedia Extended (AURIS-MM) project involves the development of a sema ntic web application for accessing research information in Austria (AURIS-MM Website 2002). The present Web technology is designed for humans to read the content while semantic web, an exte nsion of the current Web, is envisioned to bring structure to its content so the content can be processed automatically by various programs to perf orm useful tasks (Lee et al., 2001). Researchers need a variety of information, so there should be a mechanism by which they can get the relevant information for doing a pa rticular task. The proposed solution of the AURIS-MM project is the cr eation of RDF ontologies. This study and the AURIS-MM project share a common objective of managing re search information so it can be shared and readily searchable and available among researchers. However, the difference is that the AURIS-MM project has used the Common European Research Information Format (CERIF-2000) metadata (CERIF-2000 Website 2002) for describing the research information while this study has developed an ontology of ES CSTC research information for describing the ES CSTC re search projects. The ontology of ES CSTC research information was able to capture th e knowledge of research projects so the projects can be shared and searched from th e level of vocabulary used by researchers.

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11 The initial development of an ontology wa s a time consuming activity. However, the ontologies can be reused which can decrease the development time in the future. On the other hand, the time required to enter the meta data information for AURIS-MM project is relatively less but the informa tion can be searched only from the level of metadata terminology used in CERIF-2000 and not from the level of natural vocabularies used by researchers. Dissertation Layout The literature review for this study is fu rther explored in chapters 2, 3, and 4. Chapter 2 describes the ontology as a technology for documenting ES CSTC research information (objective 1), followed by th e methodology for generating a variety of educational material (objective 2). Chapter 3 describes the approach for representing dynamic information using educational simulations (objective 3). Chapter 3 illustrates the methodology of developing educational simulations followed by a description of the simulations that were created. Evaluation studies are presented comparing explaining waste management process by simulation and by conventional methods (class room lecture and lab experiments). Chapter 4 describes an ontology-based a pproach for representing mathematical models and simulations that explicitly e xposes knowledge contai ned in models at a higher level (objective 4). The knowledge can be further used for constructing conceptual models, simulations of simila r systems, and educational and training materials. Chapter 4 also addresses se veral problems with conventional methodology used to develop simulations.

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12 Chapter 5 summarizes contri butions and conclusions, and identifies future directions.

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CHAPTER 2 CONTENT MANAGEMENT APPROACH FOR DEVELOPING EDUCATIONAL MATERIAL Introduction The technology for authoring and delivering instructional materials continues to evolve. At the current time, conventional tools such as PowerPoint, Adobe Acrobat, Macromedia Flash, and HTML development tool s are widely used to develop computerbased educational resources in higher educati on. However, new approaches are evolving that are based on databases, content manageme nt systems, and learning objects (LOs). A significant difference between conventional tools and these new approaches is the latters focus on better representing the content (Dicheva and Aroyo, 2002) what we know and what we teach and separating content fr om presentation how we teach and how particular concepts are presen ted. By better defining and repr esenting content, instructors and course authors will achiev e greater freedom and flexibility in creating and delivering effective educational materials. These e ducational materials should be more easily shared, and duplication of effort in devel oping learning materials can be reduced. In addition, instructional experien ces should be tailored to the needs of individual students, not only providing the appropriate level and soph istication of information, but also presenting it in a way that meets the indivi dual students preferred learning style. 13

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14 Domains Studied Educational materials (educational simu lation, websites) were created for the following knowledge domains (two re search areas of ES CSTC): Solid Waste Treatment The solid waste treatment area had two proj ects. The first proj ect was a bioprocess laboratory called the Biochemical Methane Po tential (BMP) lab. The objective of the BMP lab exercise was to determine biodegradab ility of biological waste material (Course Website for Bio. Eng. Lab, 2004). It involved three major steps: Medium preparation: This step involve d mixing, heating, and cooling different chemicals to prepare a medium. The medi um and inoculum (sludge with bacteria) were added to the sample of solid waste. Incubation: The sample, inoculum, and medi um were mixed and were stored in a bottle, which was placed in an incubator. Sample testing: The biodegradation of the sample was measured at different times using gas chromatography machine. The biodegradability was measured after one, three, five, fifteen, and thirty days. An operational laboratory sy stem of the BMP lab exercise had nine bottles, one reactor, two gas cylinders, one incubator, and one gas chromatography machine. The biodegradability determination using the physical lab took thir ty days to complete. The task of collecting data was divided among gr oups of students. Expensive chemicals and equipment were used in the lab. The BMP la b was taught in two courses offered in the Department of Agricultural and Biological Engineering, University of Florida. Dr. John Owens taught the BMP lab in an undergra duate level course called Biological Engineering Laboratory (ABE 3062) and Dr. David Chynoweth taught the BMP lab in a course called Applied Microbial Biotechnology/Advanced Applied Microbial Biotechnology (ABE 4666/ABE 6663). Because of the commercial application, this lab

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15 is also consulted by waste management prof essionals at the national and international level. The second project was titl ed Anaerobic Composting for Recovery of Energy, Nutrients, and Compost from Solid Waste dur ing Extended Space Missions. It involved the treatment of solid waste by a process called Sequential Batch Anaerobic Composting (SEBAC). The fundamentals of the SEBAC pr ocess were the same as that of the BMP process. The only difference was in the scale of operation; which means, the BMP project was a laboratory scale of the SEBAC project. The bi odegradability test needed by the SEBAC was done in the BMP project. The SEBAC process used five reactors and circulates liquid slurry, or leac hate, between reactors in a specific sequence. The leachate was circulated internally, to a reactor cont aining activated feed, and between the reactors containing mature (old) feed and new feed. It took twenty-one da ys to treat a single batch of solid waste (Chynowyth, 2002). Wastewater Treatment The wastewater treatment area had one project titled Effectiveness of a Photocatalytic Reactor System for Water Recovery and Air Revitalization in LongDuration Human Space Flight. This project in volved the treatment of wastewater by the Magnetic Agitated Photocatalytic Reactor (MAPR) process. The wastewater was treated using magnetically agitated pa rticles coated with titaniu m dioxide catalysts in the presence of ultraviolet radiation. The expe riments were conducted at different magnetic strengths and with different particle sizes of the catalyst for the purpose of studying the efficiency of the MAPR process (Mayzyc k, 2002). The MAPR project involved three major steps

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16 Sample preparation: The wa stewater sample and nano pure water were added to a mixing bottle and mixed for several minutes. Sample treatment: The mixture of wastewat er sample and nano pure water was sent to the MAPR reactor. The UV light was turned on followed by the generation of a magnetic field by a frequency generator. The frequency generator was operated at a frequency of 20 hz, 80 hz or 120 hz. The sample was treated in the MAPR reactor for a few minutes in the pres ence of UV light and magnetic field. Sample analysis: The sample was collected and sent to the spectrophotometer for analysis and the collecting of kinetic data. Rational for Structuring and Reusing Information of ES CSTC The ES CSTC projects involved laboratory exercises in solid waste and wastewater treatment. Typically, the instructions of a lab exercise are available as a paper or electronic document that contains the rele vant lab information. For example, the instructions for the BMP lab exercise were available as a Microsoft Word document containing the information on equipment (reactor s, bottles), raw materials, catalyst, and methodology (Course Website for Bio.Eng. Lab 2004). These lab instructions were not structured, which means, the re lationships between different concepts (equipment, steps, and raw materials) were not defined explicitly. Several other lab exercises and other educational materials (like lecture notes and presentations) in solid waste treatment also use many concepts used in the BMP lab exer cise, but the information cannot be reused effectively because of the unstructured format of the information. Additionally, there is no formal agreement in the way these concep ts are defined, which creates communication problems at the level of human and computer. There is a need to organize, process, and retrieve the knowledge stored in the educati onal materials (lab exercise) so that the content of the educational ma terial can be easily reused and applied to build better educational experiences.

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17 Ontology Ontologies are a promising technology for knowledge reuse and knowledge sharing (Zheng et al., 2003). An ontology is a collect ion of concepts and relationships among these concepts in a specific domain (Noy et al., 2000). For example, an ontology of the BMP lab exercise contains the knowledge of anaerobic digestion and the concepts used in a typical wet lab such as bottles and chemicals. The ontology of the BMP lab exercise gives a well-defined meaning to the concepts used in the BMP lab exercise which will allow these concepts to be used in other applications (reports, presentations, and simulations on BMP). An o ntology will allow educators at different institutions to share their educational materials, improve th e understanding of domain knowledge, and increase the usage of knowledge within an organization ( O'Hara and Shadbolt, 2004). Ontologies can be used for assisting in communication between people, attaining interoperability among computer systems, and improving the quality of engineering software systems. Ontologies are a core component of the emerging Semantic Web movement that attempts to go beyond c onventional HTML file formats and other proprietary file formats to better represen t content on the Web (Lee et al., 2001). A number of developments utilizing ontologies have been proposed to support a variety of instructional and authoring activ ities. These developments are summarized in the section Efforts in Managing and Reusing Content Using Ontologies. Literature Review Computer-Based Instruction Several relevant recent efforts involving t echniques for developing computer-based instruction are presented he re. The Defense Advanced Research Projects Agencys

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18 (DARPA) Technology Reinvestment Project (TRP) invited proposals for developing authoring tools which could help in loweri ng the cost of producing computer-based instructional materials (Spohrer et al., 1998). Many industries (publishing and technology) and academia participated in DARPAs TRP project. Apple and IBM proposed ScriptX, an object-o riented and cross-platform standard, for developing CDROM content utilizing an authoring t echnology called SK8. The SK8 technology was focused on providing authoring to ols specific to the tasks, wh ich would enable authors to do their job in cost efficient and effective ways. One of the impor tant lessons learned from this project was that in tellectual property protection barriers, social conventions, and business model restrictions can prevent people from using authoring tools. The advent of the Internet had a significant impact on the process of delivering educational content. The Internet was seen as a better medium for delivering educational material than a CD-ROM (Spohrer et al., 1998). The focus shifted from developing specific authoring tools to collaborating w ithin an authoring community using the advantages of the Internet. The Internet en abled the easy distribution and maintenance of educational materials in an economical and e fficient manner. The Internet also enabled learners to access the course materials from remote locations like the home or office. Presently, educational materials are developed using multiple multimedia development technologies such as Macr omedia Flash, Shockwave, or Microsoft PowerPoint. For example, Flash animations are created to explain the various concepts of chemistry (Neo/Sci Website, 2006). Aut horing educational materials using computerbased tools has many advantages. Computer-bas ed authoring tools can lower the cost of producing educational materials, engage learners by developing interactive and

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19 immersive learning materials, and help educat ors in customizing and reusing content. However, the management of educational materials becomes challenging as the content of educational material increases in size a nd complexity. A concern arises about the reusability of the content from technical and legal perspective. Additionally, it is becoming difficult to locate and retrieve relevant educational materials. Content Management Systems Content Management Systems (CMS) are bei ng developed for managing the content of educational materials (Learning Circuits Website, 2001) by providing a capability for authoring, collecting, storing, and deliver ing educational materials. Learning Management Systems (LMS) are used for mana ging various administrative aspects, such as course registration, of de livering a course. Learning Content Management Systems (LCMS) combine the functionality of LMS and CMS. A content management system is a distributed software system which treats information in a granular way, enabling the access, versioning, and dynamic assembly of pieces of information, and named content, such as diagrams, tables, images, or pieces of text (Canfora, 2002). Boiko (2002) defined CMS by the following key processes: Collecting: Creating or ac quiring content items and tran sforming the content into standard formats Managing: Storing and maintaining the cont ent and their metadata in a repository Publishing: Retrieving and extracting the content for producing information in a specific format Learning Objects Presently, many educational materials are created without considering pedagogical aspects. Learning Objects (LOs) are a paradigm that emphasizes presenting the domain knowledge within the context of instructional strategies and assessments (Khan, 2003).

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20 A Learning Object (LO) consists of the following components (Seplveda-Bustos, et al., 2006) Goals and learning objectives Knowledge domain: It consis ts of the knowledge of cour se content, which can be presented as text, image, animations, or movies. Instructional information: It presents th e information relevant for presenting the content in a particular sequence and ad justing the sequence and pace of the delivering content based on learners ability. Searchable metadata: It includes the info rmation about the content, which can be used by learners or instructors for se arching for a specific LO. It includes information like name of the author, title of LO, or keywords. Assessment: It determines the attainment of learning objectives by the students, which can be achieved by using assessment resources (exams, quizzes). Other important aspects in generating LOs include the graphic design (the way it is presented) and the medium of delivery. A basic problem faced by the learning community is how to produce and deliver quality content for online lear ning experiences. Internati onal Business Machines (IBM) developed an approach for producing LOs to provide individualized learning experience for learners specific needs (Farrell, 2004). The content of LOs was produced from the reference books and presentations in a semi-aut omatic fashion. The learners were able to search the LOs on the basis of media type, inte nded use, level of difficulty, or keywords. Several efforts have been going on in standardizing the way LOs are created, managed, and used. Four organizations are developing standards relevant to LO technology: Aviation Industry Computer Based Training Committee (AICC), Institute of Electrical and Electronics Engi neers (IEEE) Advanced Dist ributed Learning (ADL), and Instructional Management Systems (IMS), Global Learning Consortium (WBTIC Website, 2005).

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21 Shareable Content Object Reference Model (SCORM) Shareable Content Object Reference Mode l (SCORM) is a standard developed by ADL for LO (ADL Technical Team, 2004) The development of SCORM had a significant impact on the e-learning industry and on the developm ent of LO. Most of the vendors are developing standard s based on the SCORM. The SCORM standard requires LOs to have the following features: Reusability: The LO should be capable of being assembled and restructured in a variety of different courses. For ex ample, a LO on overview of anaerobic digestion process developed in an organi zation such as an agricultural engineering department should be able to be usab le in the training modules of other organizations like USDA. Interoperability; The users should be able to combine LOs from the various sources for designing their own courses. Durability: The advancement in the tec hnology should not make a LO obsolete. Accessibility: The content developed usi ng LOs should be accessible at anytime from a variety of locations. Efforts in Managing and Reusing Content Using Ontologies Several relevant recent efforts in managi ng and reusing the content (also LOs) are presented here. Most of these efforts have been proposed rather than implemented. Most of the researchers (Angelova et al., 2004; Sridharan et al., 2004; Tan and Goh, 2005; Nicola et al., 2004) have pr oposed ontologies for annotati ng learning resources while the presented approach has described a system for storing the learning content in an ontology. A number of developments utilizing ontologies have be en proposed to support a variety of instructional a nd authoring activities, in cluding hypertext navigation, collaborative learning and training, courseware author ing, user interaction, and information retrieval (Aroyo and Dicheva, 2002) For example, an approach has been

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22 proposed for integrating authoring tools with the knowledge of instru ctional theories and principles by developing a series of ontologies with the objec tive of delivering an appropriate instruction method based on inst ructional theory (Miz oguchi and Bourdeau, 2000). An ontological approach to cour seware authoring has been proposed by separating domain knowledge and applicati on related knowledge (Aroyo and Dicheva, 2002). Ontologies have been developed for describing the multimedia content used in educational material. For example, Stanfo rd has developed an ontology for MPEG-7, a standard for describing multimedia content. There have been several suggestions fo r making LOs reusable using ontology. One of the suggestions was to create an ontology of the LO metadata which can help users in searching and using LOs (Gas evic et al., 2005). The Do cSouth project used domain specific metadata for describing the conten t of a LO (Pattuelli, 2006). Tan and Goh (2004) proposed the association of domain ontologies with the learning resource for classification, navigation, and searching of learning resources. Multitutor OntologyBased Learning Environment (M-OBLIGE) pr oposed a system where ontologies were used as the metadata of web-based educatio nal materials i.e., educational material will point to various ontologies for semantic markup. The Larflast project stru ctured the learning content by developing a domain ontology in finance and by usi ng the domain ontology for a nnotating LOs (Angelova et al., 2004 ). The annotations of LOs were ente red manually and were used for linking the LOs with the concepts of the ontology. The ontology of the Larfast project contains 300 concepts. The two types of LOs were described in the Larfast project: Static exercises: Used to de termine the knowledge of a domain Reading materials: Collected from the In ternet and related to relevant concepts

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23 The Larflast project emphasized the usage of explicit domain knowledge in describing LOs. For the purpose of aut horing course outlines Yang et al.(2005) proposed an ontology based course editor. Sridharan et al. (2004) proposed an application for managing and searching releva nt documents by developing an ontology in RDF. Nicola et al. (2004) descri bed the use of ontologies in gathering and organizing teaching materials for the construction of a course. The ontology of course content was developed and referenced to the learning resources. For validating the approach suggested by Nicola et al., a course on ontological modeling is under development and an ontology of 168 concepts has been develope d. Iowa State University developed the domain ontology from a contro lled vocabulary in the medical domain (colonoscopy and endoscopy) and used it for annotating a video database (Bao et al., 2004). Seplveda-Bustos, et al. (2006) propos ed a methodology for developing LO by applying the approaches of so ftware engineering, project management, and instructional design. The work of Seplveda-Bustos, et al applied the principles of Blooms taxonomy in establishing the learning objectives. Th e components required to built a LO was represented by an ontology of the components(objective, assessment, metadata, learning assets, etc.) of LO. The ont ology was used for identifying and collecting the identified resources. The LOs were rendered as a webpage using Macromedia Dreamweaver, and they were evaluated in an undergraduate course in fluid mechanics. On the contrary, this study utilized ontology for storing the knowledge of resources. This study structured the content of educational materials (website and educational material) as the domain

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24 ontology and the educational materials were generated automatically as explained in Presentation Generator section. Generating Presentations from Content The content of educational material can be presented in a variety of formats like animation, website, reports etc. The developmen t of an educational material in a specific format involves three major steps: collection of information, organization of information, and presentation of information in a specific format (Alberink et al., 2004). There are several techniques for generati ng presentations. One fairly common approach is to use server page technology such as Microsof ts Active Server Pages (ASP) (ASP Website, 2004) or Sun Microsystemss Java Server Page s (JSP) (Sun Website, 2004). Server page technology (JSP and ASP) is restricted to the creation of web pages, but has the advantage of drawing content from a database to populate web pages. Style sheets offer another technique for creating presentations. A Style sheet describes the rules for presenting documents in different presentation style formats on different media like webpage or print (W 3C Website, 2006). Separating content and presentation can be achieved by storing the content in a database and generating the presentation by using style sheets (Clark, 1999). The style of a presentation can be specified independently of the act ual content, so that the same content can be presented in different styles. For example, multiple websit es with different presentation styles (fonts, colors, layout) can be generated from the same content so the conten t can be presented to a specific audience in a suitable format (C SS Website, 2005). The rationale for using multiple styles is the preference of a specific style by the intended audience. For example, different colors are prominent in different cultures so the background color of the website can be changed based on culture of the audience. Sim ilarly, older audiences

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25 prefer bigger fonts so the font can be change d based on the age of the intended audience. Among other things, this frees instructors (course authors) from having to be experts in graphic design, and they can focus instead on their subject expertise. Instructors can choose from pre-existing styles that were created by graphic design experts. One of the most well known methods of u tilizing independent styles to generate presentations is Extensible Stylesheet La nguage (XSL) technology (Clark, 1999). In this approach, the style of presenta tion is described in a XSL Transformations (XSLT) file. Basically, an XSLT provides instructions for how one XML file can be converted to another by telling how a tag in the source f ile should be converted to a tag in the destination file. In practice the source XML f ile contains the content to be presented and the destination XML file can be HTML for website generation, XML Formatting Objects (XML FO) for printing, or other formats. As XSL technology can be somewhat tedious to develop, other techniques have been cr eated to convert database objects to presentations where basic elements of style ar e described in a flexible format (also as database objects) and are used by a program that generates multiple formats (HTML, Applet) from database objects. The style objec ts that specify details such as fonts and colors guide the program. Content Management Approach The approach used in this study a pplied a CMS for creating and managing educational materials in the ar ea of waste treatment These systems have the ability to generate presentations from a database th rough a process in which the elements of database objects concepts stored in database were mapped to a pa rticular presentation format such as HTML, animations and other formats as explained in Presentation Generator section) Such a facility can provide a va luable component in an information

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26 technology approach to handling educational resources in agricu ltural and biological engineering. It can promote the sharing a nd reusing of educationa l materials within a department and between different departme nts locally and regionally. The presented approach will change the focus from deve loping a specific educational material to representing the knowledge of educationa l material and using generic software applications for generati ng educational materials. Components of a Content Management System This section describes the components of th e content management approach used in this study for developing educational material s. Web-based tools were used for entering the details of the lab processes as an ontol ogy in the database (Badal et al., 2004b). Presentations that can take the form of educational simulations, web pages and other formats were then generated from th e database using software tools. Figure 2-1 shows the components of the content management system used for developing educational materials. Central to the approach is an ontology for building formal descriptions of concepts and showi ng how these concepts are interrelated. The ontology was stored in an object database that provided a physical storage mechanism for large numbers of concepts or objects; th e bioprocess lab exampl e contains several hundred. Graph-based and web-based authori ng tools (described in section Tools for Creating an Ontology) were used by instructors to create an d manage course content. These tools were integrated with an object database for storing the ontology structured information. Several different techniques (JSP, Java Applet) were used to automatically generate presentations from this content. De tails of the major components of the system are described here.

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27 Ontology Database System Presentation Generator Generated Educational Materials Simulation, Web p a g e Java Applet, Flash HTML Ob j ectStore Figure 2-1. Components of a content management system used for developing educational materials Ontology Each concept in the lab exercises is form ally defined by a concept in the ontology. An ontology of the BMP lab exercise contains concepts such as bottle, stock solution (chemical), degradation, and other concepts specific to the lab. The BMP lab exercise uses many bottles so the ontology specifies the concept of bottle and stores various bottles (such as a bottle for storing samples) as a bottle concept. A concept contains taxonomic relationships (a bottle is a member of the class equipment), properties (a bottle has as a particular volume), and asso ciation with other c oncepts (a bottle can contain a chemical, a bottle can be physically connected to a valve). A concept or object can also have behavior (a bottle can fill or empty over time).

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28 Methodology of developing an ontology The ontology was developed using the Web Taxonomy authoring tool (described in section Tools for Creating an Ontology). The following elements were used in the ontology: Class: A class is used for describing genera l concepts like bottle or procedure. For example, the BMP lab exercise used many bottles & so a bottle class was used to describe the bottle concept. Individual: An individual is used for describing instances or specific occurrences of a concept class. For example, the BMP la b exercise used seven bottles so seven individuals of the bottl e class were created. Property: A class can have several prope rties for defining its attributes. For example, radius and height were define d as a property of the bottle class for capturing geometrical information. Relationships: A class can have relationships with other classes. The relationship can be either predefined (subClass, superC lass, hasParts, partOf) or user-defined (hasName or comesOutOf). The hierar chical relationships were modeled by subClass and superClass relationships. A bottle is a specific kind of equipment so there exists a relationship called subCl ass between the bottle class and the equipment class. Ontology was developed with WebTaxonomy authoring tool. The following steps were used for developing the ontology: Collection of relevant documents: The relevant documents such as research papers, PowerPoint presentations, and published reports of ES CSTC projects were collected. Analysis of documents: The information from the relevant documents was analyzed and the concepts were extracted from them manually. Development of class hierarchy: The co llected concepts were enumerated and classes were generated from the concepts. The classes were further organized into a class hierarchy by organizing classes from more ge neral (like equipment) to the more specific (like pump). The cla sses were entered into the ontology using the Web Taxonomy editor. Figure 2-2 show s the class hierarchy for the BMP lab exercise created as a part of this pr oject. Each class was specified by its definition, properties, and important relati onships like superClass and subClass. Creation of individuals: The individuals were created by specifying the class to which the individual belongs and by ente ring the values of properties.

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29 An individual of bottle class called stock solution bottle 1 was created by following steps: Open the Web Taxonomy Editor (Figure 2-2). Specify the name of the class bottle to which the individual belongs-stock solution bottle 1. Create an individual using the Web Taxonomy editor Enter the definition of the i ndividual and the values of properties for the individual (radius = 50, height = 10) Tools for creating an ontology Web Taxonomy (Beck and Lin, 2000) and ObjectEditor (Beck, 2003b) were used for creating ontologies. The availability of the ontology construction tools on the Web not only makes the tools more accessible and easier to distribute, it also allows users to collaborate over the Internet to develop educational resour ces. Web Taxonomy (Figure 2-2) is a tool for adding and editing the c oncepts in the ontology. Figure 2-2 shows a portion of the ontology developed for the BM P project, and it displays the different equipment items such as bottle, flask, gauge, etc. used in the BMP laboratory procedure. Each piece of equipment used in the experiment was described by an individual in the ontology. For example, the BMP lab used seve n stock solution bottles so there are seven individuals of stock soluti on bottle in the ontology. ObjectEditor (Figure 2-3) is an altern ative graphic interface for partitioning the concepts that belong to a sp ecific project like the BMP pr oject. Figure 2-3 shows a portion of an ontology developed for the BMP pr oject using ObjectEdit or. In particular, this diagram (Figure 2-3) shows equipm ent objects and how they are physically connected. For example, it shows that the individual ss pipe 1 is related to the

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30 individual stock-solution bottle 1 by a re lationship called out of bottle because ss pipe 1 comes out of stock solution bottle 1. Figure 2-2. Schematic of Web Taxonomy showing a portion of the BMP ontology The ontology captured not only the physic al objects and thei r structural and dynamic relationships needed for developi ng interactive animations (educational simulations), but it also acts as a dictionary for all the terms used in the ES CSTC projects described in the section Domains St udied. The ontology provides a better way for students to browse concepts to learn their meaning and interrelationships. This dictionary provides machine-interpretable de finitions, which means, the computer can analyze the meaning of terms, and provide re asoning facilities th at can determine how

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31 terms are related. A multilingual feature is also supported so that terms in different languages can be used to refer to the same object. Figure 2-3. Schematic of Object Editor showing a list of equi pment and reagents used in the BMP lab and the relationship between them Database System The web-based tools for constructing the ontology were built on top of ObjectStore (ObjectStore Website, 2006), a commercial obj ect database management system. The object database was used for storing the ontology because the object database provided a more convenient and natural way to organize data structured as an ontology rather than through tables, as is done in a relational databa se. The integration of the web-based tools with a database facilitated the development of educational materials by storing the

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32 ontology in the database and us ing it for generating educatio nal materials in different formats. The online tools allow the instructor to develop educational materials from any remote location and store them in a common se rver-side database. The concepts can be added or edited using Web Taxonomy, ObjectEdit or, or other tools pr ovided as part of the authoring environment (including equati on editors, text, table, and vector graphic editors). Presentation Generator The presentation generator consisted of several computer programs written in various languages (Java, Java Server Pages (JSP)) for rende ring educational material in multiple formats. Two applications were developed using JSP and Java applet technology. The JSP application was developed for rendering the research information of ES CSTC projects as a website while the Ja va applet application was developed for displaying the dynamic information of ES CSTC projects as educational simulations. The next sections describe these applications. Java server page technique The website for the projects at ES CS TC was generated using JSP technology. Figure 2-4 displays the interface of the webs ite created for the BMP lab exercise, which shows the details of a chemical (stock solution) used in the BMP lab. A JSP is very much like a conventional HTML page and contains HTML tags for defining the appearance of a webpage, but it also contains additional tags embedded in the HTML that refer to database objects. In general, wherev er a reference to a data base object appears, the contents of that object are displayed at th at point in the JSP. So, in Figure 2-4 the logos, titles, and frames were all created usi ng static HTML tags, but the body of the text

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33 was created dynamically from database objects referenced in the JSP. The JSP must be created manually, but then the content is inserted automatically. The following steps were used for creating the website from the content: Ontology development: The details of the ES CSTC pr ojects (described in section Domains Studied) were stored in the database by developing an ontology using the Web Taxonomy authoring tool. The process of developing an ontology is described in section Methodol ogy of Creating an Ontology. Design of website layout: The general layout of the website (logo, title, frames) was created in HTML using Microsoft FrontPage. Some of the links (e.g. About the Center) on the left-hand side of the webpage were manually hyperlinked to an external website ( www.ees.ufl.edu ), while some of the links (e.g. Project) were hyperlinked to the webpage generated from the content stored in object database. Development of JSP application: A JSP application was developed for rendering a specific concept in the ontology as an i ndividual webpage. The JSP application contained the HTML tags developed dur ing step 2 and additional tags for communicating with a Java class. The J SP application communicated with a Java class called BMP Bean, and the BMP Bean class was used for communicating with the ontology database using Java Remote Method Invocation (RMI) protocol. Borland JBuilder integrated developm ent environment (Borland Website, 2006) was used for developing the Java Bean class and for implementing the RMI protocol. The presented approach illustrated an approach of dynamically generating a website from the ontology. The general layout of the website (header, side) was designed using Microsoft FrontPage. The content for the main body of the website was structured as an ontology, and the main body for the we bsite was generated by the logic embedded in a Java class, as describe d in previous paragraph. The content for the main body of the website can be updated by modifying the ontolog y while the presentation of the websites main body can be changed by modifying the Java class. It is easy to provide dynamic conten t using JSP (Sun Website, 2006). The JSP technology uses the functionality of Java language and is widely supporetd by the software vendors (Webber, 2000). The JSP te chnology uses reusable components, rather

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34 than using only scripting in a page, which speeds up the developmen t of an application (Sun Website, 2006). The JSP technology uses Java classes for gene rating the content of a webpage and HTML tags for controlling the layout of the webpage. In this way, the JSP seprates the content and layout of a webpage. Java IDE tools can be used for debugging Java classes while the commonly used webpage design tool can be used for debugging the html part of the JSP website. The functioning of JSP involves the genera tion of a Java class from the JSP and the Java class is then parsed to creat e a servlet class (Webber, 2000). Another disadvantage of the JSP technology is that the content and the logic is not well separated. The JSP technology allows the embedding of logic in a webpage, which defeats the purpose of separating the logic and the content (Spielman, 2001). This can create the problem of maintaining and updating the webs ite. The JSP technology also allows the insertion of inline Java code in a JSP page, wh ich makes it difficult to separate the tasks. This also creates the problems in understanding the JSP page. Java applet technology The Java applet described in chap ter 3 was used for presenting dynamic information of the ES CSTC projects. The presentation of dynamic information required interactive features provid ed by the Java applet tec hnology. In contrast, the JSP technology is used for generating HTML, XML or other types of documents. This study used JSP for generating HTML. However, Java Applets can be inserted in a JSP page for providing interactivity. Generated Educational Materials The educational materials were generated fr om the same database in two formats: as a website containing text a nd graphics and as an educati onal simulations. The website

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35 was used to display the ontology of the ES CS TC projects in solid waste treatment and wastewater treatment. Students can browse the different waste treatment concepts and use the website as a waste management dictionary. The e ducational simulation (described in chapter 3) is a Java applet that presents th e dynamic information as a 2-D animation. The simulations were evaluated in two courses taught in University of Florida. Figure 2-4. Website generated by the content management approach This study showed that the content manageme nt approach (i.e., using a database to store research information) can be used for documenting research information. The information was first structured as an ontology (structured information) and stored inside

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36 an object database (an ontology management system). This approach allowed the documentation of research at a very fine level (i.e., documenting resear ch at the level of concepts used in various research projects) in stead of storing the e ducational materials at only a course level in the form of documents, presentations or other formats that fail to explicitly represent content. There can be an overlap of concepts used in various projects, and the overlap of concepts can be used for identifying similarities in various projects. For example, both SEBAC and BM P project uses the concept of anaerobic digestion, so the overlap of concepts in ontology can infer the similarity in BMP and SEBAC project. The JSP technology was used to generate a website from the ontology. Automatic presentation techniques can greatly reduce the effort required to create educational materials; however, it is not al ways desirable to fully automate the process, as often the instructor does want to have full control ove r the presentation. Ch apter 3 describes the automatic generation of educational simulation (rendered as a Java applet) for displaying dynamic information of the lab processes used in the ES CSTC projects. Information about the lab processes was stored in th e ontology and the dynamic information was displayed in an interactive format anim ations using Java applet technology.

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CHAPTER 3 EDUCATIONAL SIMULATION: AN AP PROACH FOR PRESENTING DYNAMIC INFORMATION OF A PROCESS Introduction It is critical to present educational mate rials in a format that best matches the students individual needs. Since engineer ing processes are dynamic in nature, it is beneficial to present the processes in th e form of an educational simulation. An educational simulation is a presentation of a dynamic process (like the steps of a laboratory experiment or operating a machine) as an interactive and intuitive animation which can help a student in understanding a spec ific process. The interactivity of the educational simulation increases a students learning efficiency (Mclean and Riddick, 2004). Another advantage of using simulation is that the student can access it anytime and anywhere, in contrast to an in-lab experience requiring special equipment. Virtual Lab Educational simulations are also known as virtual labs, where students can experiment with the equipment and the process itself. Instructors can show the lab in the form of an animation for explaining the differe nt concepts in the lab. The learners can also change the process parameters to study how they impact the behavior of the system. Since one of the objectives of this project is to present information in a format most suitable to students, virtual labs have been created for explaining the concepts of waste treatment processes de scribed in Chapter 2. 37

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38 Literature Review on Virtual La bs and Educational Simulations Virtual laboratories have been deve loped in various domains like physics, engineering, power electroni cs, and medicine (Hashemi, 2005). The IrYdium project developed educational materials in the domai n of chemistry (Yaron, 2003). Their goal was to create a simulation-based learning environment where high school and college students can learn the concepts of chemistry through interesti ng real-world applications. Remote database and network technologies were being used to facili tate the delivery of the software over the Web. Similarly, a multimedia-based course in environmental engineering and process design wa s developed at University of Maine (Katz et al., 1997). The video clips and spreadsheet technologies were used for explaining the processes of natural systems as well as data collection processes. A virtual laboratory in the area of material science and engineering was deve loped in the Department of Mechanical Engineering at Texas Tech University using Flash and other multimedia technology (Hashemi, 2005). The University of Florida used the same approach (Flash technology) in the domain of medicine to teach an an esthesia machine operation (Lampotang, 2004). The University of California, Davis deve loped seventeen virtual experiments in food processing for academic purposes (Singh and Erdogdu, 2005). Each virtual experiment includes simulations, which were implemented with Flash technology. These simulations were developed for enhancing the understanding of engineering concepts used in food processing operations. Rice University is using Java technology for teaching various statistical concepts (Lane, 2003). The Iowa Bioprocess training center offers training in bioprocessing by virtual reality and classroom training (Brigha m, 2003). Because of the cost and skills requirements, there is a great need for training bioprocessing (waste management) skills

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39 by simulations or virtual laboratory. Many other examples of teaching a concept by utilizing a virtual la boratory are also available on the Web. The task of creating a virtual laborato ry is challenging because it requires a multidisciplinary effort; in addition the task of managing the content of virtual laboratory becomes more challenging as the content incr eases in volume and complexity. Most of the virtual laborator ies are implemented using a c onventional programming language (JAVA, C, ActionScript) and software tools w ith little effort in e xplicitly representing content. This study investigated an appro ach of using an ontology for structuring and storing the content for facil itating the development of vi rtual laboratories and other educational resources. Methodology of Creating Educational Simulations The content management approach desc ribed in chapter 2 was used as a methodology for creating educational simulations. These simulations were developed for running the experiments related to the waste treatment processe s, as described in chapter 1, on the Web. The following steps were us ed for creating educational simulations: Ontology Development The details of an experiment were st ored in the database by developing an ontology using the ontology development tools described in chapter 2. The ontology was developed for a specific domain like the BM P lab exercise. The details of the lab exercise like information about various equi pment (bottles, pipes, valves), chemicals (stock solutions, inoculums), and samples used in the experiment were represented as different individuals in ontology. The inform ation of the lab exercise was structured using the concepts of object-oriented desi gn and ontology principles. For example, paper is a kind of a sample and it has a pr operty called rate consta nt with a value of

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40 seconds which was used for calculating th e rate of degradation for biodegradable sample. Therefore, a concept called sampl e was entered in the ontology and paper was described as a specific type (or subcla ss) of sample. Each paper concept has a property called rate constant used for storing the value of rate constant. Development of Java Classes Java classes were developed for renderi ng the details of specific concepts (like equipment) used in the lab exercise and also for implementing the behavior of specific concepts (like bottle) in the simulation. The details of the concepts were stored in the ontology. For example, a bottle is a concept that has width and height. The details of the bottle and its association with different con cepts were stored in the ontology, but Java classes were implemented for rendering the bottle concept and required behavior like filling and emptying the bottle. A Java class was implemented for every physical individual in the simulation, and within each Java class, methods implement the behavior of each individual. Development of Educational Simulation The simulation was rendered as a Java applet. Individuals specific to a lab exercise were loaded into a module using Ob ject Editor (described in chapter 2). The applet loaded the details of each individual (equipment) in the module from the ontology and executed corresponding Java classes for re ndering the details and behavior of each equipment. The simulation was implemented in two modes: movie mode and interactive mode. Movie mode (Figure 3-1) was implemented by writing a script which was used for starting and stopping the animation of differe nt equipment. The movie mode ran the

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41 simulation sequentially, similar to the instruct ions or standard operating procedure for a process, so a student can get an overview of the process. Figure 3-1. Interface for the BMP laboratory for determining biodegradability of a sample in movie mode Interactive mode (Figure 3-2) allowed students to experiment with the lab experiment in an interactive fashion. In the interactive mode, the learners started and stopped the animation of different equipm ent by clicking on the valves and buttons (Badal et al., 2004c). The instructions for running the simulation in interactive mode are given on the ES CSTC education and out reach website (ES CSTC Education and Outreach Website, 2006).

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42 Figure 3-2. Interface for the BMP laborator y for determining bi odegradability of a sample in interactive mode Results Several educational simulations were developed for explaining the different aspects of waste treatment processes. The de tails of these processes are described in the Domain Studied section of ch apter 2. These simulations can be accessed from the ES CSTC education and outreach website (E S CSTC Education and Outreach Website, 2006) Simulation for Solid Waste Treatment Bioprocess lab (BMP lab) Figure 3-1 and Figure 3-2 show the interf ace for the BMP laboratory exercise for determining biodegradability of a sample in movie mode and interactive mode. Figure 31 shows that the BMP lab exercise contains nine bottles, one reacto r, two gas cylinders,

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43 one gas chromatography equipment, and one incubator. The BMP process involved mixing, heating, and cooling of chemicals to prepare a medium. The sample and medium were mixed and placed in an incubator. Th e biodegradability was measured after one, three, five, fifteen, and thirty days by the gas chromatography equipment. SEBAC simulation Figure 3-3 shows the interface of the SEBAC process for treating solid waste in the movie mode. It shows five reactors that we re used for various purposes (filling sample, empty sample, and storing new, activated (m atured), and old sample) during the SEBAC process. The sample was treated in the reacto rs by circulating a new sample with an old sample and by circulating the activated sample with itself. The movie mode (Figure 3-3 ) shows three buttons which can be clicked for showing the different circulations in the SEBAC process. The single reactor bu tton shows the circulation in the reactor containing the activated sample. The two reactor circulation button shows the circulation between the reactor containing the old sample and the new sample. The three reactor circulation button shows the circulation between the reactor containing the old sample and the new sample and the circulation in the re actor containing the activated sample. Figure 34 shows the interface for the SEBAC process for treating solid waste in interactive mode. The user can click on different valv es for activating the flow in the pipe and filling the reactor. The interface of the SEBAC process (Fig ure 3-3, Figure 3-4) has many pipes, reactors, and valves which ca n be hard to comprehend, so an additional simulation was developed for showing the circulations between the three reactors in the SEBAC process (Figure 3-5). The simulation shows three reactors with new, activated, and old feeds.

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44 Figure 3-3. Interface of the SEBAC process for treating solid waste in movie mode. Figure 3-4. Interface of the SEBAC process fo r treating solid waste in interactive mode

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45 Figure 3-5. Interface of the SE BAC process with three reactors for treating solid waste in movie mode (ES CSTC Educati on and Outreach Website, 2006) It does not show the process of filling and emptying the sample in order to simplify the presentation. Figure 3-6 s hows the phenomenon of clogging in the SEBAC process. The clogging simulation was developed in an interactive mode. The student can click the two-way valve for circulating the flow of liqui d slurry (leachate) in an up-flow direction or down-flow direction. The simulation il lustrates the movement of solid sample particles in the reactor and the flow of leachate in the reactor and the pipe. The pressure of the reactor is shown by the pressure gauge. The pressure in th e reactor increases due to the accumulation of solid particles (that is clogging) at the inle t and outlet of the reactor. The problem of clogging was solved by reversing the flow of leachate. The reversible flow of leachate was achieved by us ing a two-way valve. The flow should be reversed automatically or manually after a fi xed time to avoid clogging. In Figure 3-6,

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46 up-flow is obtained by clicking the upper half of the valve and the down-flow is obtained by clicking the lower half of the valve. Figure 3-6. Interface of the SEBAC process wi th a single reactor for showing the process of clogging (ES CSTC Educati on and Outreach Website, 2006) Simulation for Wastewater Treatment Figure 3-7 shows the interface of the MAPR process for treating a sample of wastewater in movie mode. The user can wa tch the MAPR process by clicking the start movie button. The interface s hows two bottles for storing a wastewater sample and nano pure water. These bottles were connect ed to the third bottle (mixing bottle) by pipes which have valves for moving the wa stewater and nano pure water to the mixing bottle. The color of the valv e changes to green when the valve is opened, and the color changes to red when the valve is closed. The wastewater sample and nano pure water were mixed in the mixing bottle. The dilu ted wastewater sample was treated in the MAPR reactor in the presence of ultraviole t (UV) light and a magnetic field. The UV light lamps were used for producing UV light A frequency generator was used for generating electrical signals at three different frequencies. These electrical signals were

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47 transmitted to a solenoid for producing a magne tic field. The treated wastewater sample was sent to the spectrophotometer for the analysis of wastewater. Figure 3-7. Interface of the MAPR laboratory for treating a sample of wastewater in movie mode The user can click on valves and buttons for running the MAPR process in an interactive fashion. Figure 3-8 shows the in terface of MAPR laboratory for treating a sample of wastewater in interactive mode. Evaluation of Simulation Evaluation of Solid Waste Treatment Simulation An evaluation of the BMP lab exercise wa s done with the objective of collecting feedback from students and to compar e the methodology of teaching the BMP lab exercise by simulation with the conventional lab instructions and hands-on methods. An evaluation form was designed to measure the understanding of tec hnical concepts by the students as well as their perspe ctive about the teaching methodology.

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48 Figure 3-8. Interface of MAPR laboratory for treating a sample of wastewater in interactive model The understanding of technical concepts was measured by designing a set of ten questions related to the BMP lab exercise with the help of the instructor, Dr. John Owens. The perspective of the students was measur ed by designing a set of eight questions related to the experience of the students with the teachi ng methodology. The subjective evaluation measured the following aspects: Encouraging students to learn by a particular teaching methodology Developing confidence in the students about concepts used in the lab Enabling students to work through co urse materials at their own pace Developing students creativity and skills Enabling students to apply the concepts lear ned in the lab to re al world situations Teaching students to work together

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49 The students were also asked if they found the teaching methodology interactive and if they had a good learning experience duri ng the evaluation. These questions were in the format of multiple choice. Figure 3-9. Overall subjective experience of the students by two teaching methodologies for the BMP lab evaluation Ten students of ABE 3062 (Course Website for Bio. Eng. Lab, 2004) were asked to read the lab instructions a nd perform the lab manually. After performing the lab, the students were asked to fill out the evaluation form (Appendix A) within one week. Seven students of ABE 4666/ABE 6663 were shown the simulation as a group and were asked to complete the evaluation form in the classroom. Before evaluating the simulation, the students of ABE 4666/ABE 6663 were given a br ief tour (45 days before seeing the simulation) of the BMP lab as a part of the course. Results of the evaluation were that the average score (technical concepts) for the class after seeing the simulation was 57.14, a nd the average score for the class after

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50 reading the lab handout and performing the lab was 62.22. The statistical analysis showed that there was no signi ficant difference in the scores (t = -.5, df = 14, p = .25). The subjective evaluation (Figure 3.9) showed that students found both teaching methods (hands on lab and learning by simulation) useful to nearly the same extent. The results showed no significant difference in th e various aspects of subjective evaluation except that the students found it easier to wo rk at their own pace with the conventional method than by simulation. Of course, in this evaluation the students were not yet given the chance to use the simulation individually; rather, the instructor showed the simulation to the entire group. Evaluation of Wastewater Treatment Simulation The class of ENV 4514 (Water and Wastewater Treatment) was divided into two groups. Each group had seventeen students. The first group was asked to run the simulation on their laptop and read the online lesson. The online lesson was designed manually using Microsoft FrontPage for giving the background information of MAPR process. Some of the concepts in the on line lesson were linked to the MAPR ontology. The students were asked to complete the eval uation form (Appendix B) after running the simulation and reading the online lesson. Pres ently, the students are not given the hands on experience using the MAPR process because the lab exercise has not been designed. The second half of the class attended the class room lecture of MAPR and was asked to fill the evaluation form in the classroom. Results of the evaluation show ed that the average score (technical concepts) of the class after performing the simulation was 82 and the average score for the class after attending the class room lectur e of MAPR was 73. The statis tical analysis showed that

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51 there was no significant diffe rence in the score (t =1.59, df = 16, alpha = .05) for technical concepts. The results showed the significant differen ce in the following aspects of subjective evaluation: The confidence in the concepts of the MAPR lab after performing simulation was greater compared to learning the concepts in the classroom lecture (t = 2.51, alpha = 0.05). The simulations enabled students to work through course materials at their own pace (t = 4.4, alpha = 0.05). Students found the learning e xperience with simulation mo re interactive (t = 2.79, alpha = 0.05). Students did not like the learning experi ence with simulation (t = -2.66, alpha = 0.05). However, the results also showed that there was no significant difference in the following aspects of subjective evaluation: Encouraging students to learn by a particular teaching methodology Developing students creativity and skills Enabling students to apply the concepts lear ned in the lab to re al world situations Conclusions Based on the results of evaluation, it can be concluded that the simulation can help the instructor in teaching a lab exercise. Th e effectiveness of simulations also depends on the approach of integrating simulations in the instruction, that is, the simulations can be either shown to the students as a demons tration or students can run the simulations on their own computer. The evaluation result s concluded that the simulations enabled students to work through course materials at their own pace when the students ran simulations on their computer whereas the stude nts were not able to follow the concepts when the simulations were only demonstrat ed in the classroom. In addition, the

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52 confidence of a student in the lab concepts increased when the student ran the simulation on his/her computer. The use of simulation helps in teaching the la b where it is practically infeasible to teach the lab as a hands-on approach because of the high cost of the equipment and chemicals involved. Simulations can also serve as a replacement experience for universities and colleges that do not have a waste treatment laboratory. The computerbased simulation can also be used to a ugment the real laboratory experience. Furthermore, the techniques presented in this study can reduce the cost of creating computer-based simulation. The evaluations were not designed by consulting the statistical analysis professionals. However, instructor of BMP lab (Dr. John Owen) and instructor of the MAPR lecture (Dr. Dave Mayzyck) were c onsulted for developing evaluation forms, and they are consistent with the type of evalua tions (tests and quizzes) used in the courses taught by these instructors.

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CHAPTER 4 AN ONTOLOGY-BASED APPROACH TO MATHEMATICAL MODELING Introduction An ontology-based approach to mathema tical modeling, in which a model is represented using ontology concepts, can he lp address several problems with current methodology used to develop simulations. Th e general goal is to better communicate knowledge about models, model elements, and data sources among different modelers and between different computers. This can be achieved through the ontologys ability to explicitly represent and thus define concepts used in models. Various researchers create simulations w ithin a particular domain to address a specific problem. For example, various simu lations have been written in the domain of solid waste management for determining an aerobic biodegradability of a solid waste (Batstone, 2002). There is an overlap of the concepts and interactions used in these simulations. Frequently, differe nt modelers use different symbols for the same concept. The use of different programming language s makes communication even more difficult (Reitsma and Albrecht, 2005). Literature Review Problems in Developing Simulations Typically, a model is implemented in a particular programming language like FORTRAN, C++, or Java so it can be run to understand th e behavior of the system. However, the meaning of the model is lost when it is represented using program code (Furmento et al., 2001). Re searchers must understand the programming language in 53

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54 order to understand the model. Semantic i ssues (like meaning of symbols or concepts used in the model) should be addressed so the knowledge in a simulation can be made explicit (Lacy and Gerber, 2004). While such models are documented using papers and manuals, this documentation is physically sepa rate from the model implementation itself. It is difficult to maintain both the model and the documentation, and often the documentation is not an accurate descripti on of the model implementation. All the details of program code are difficult to de scribe in written documentation, so that ultimately it is necessary to read the computer code in order to truly understand how the model works. These issues need to be addressed so the knowledge in a simulation can be made explicit (Lacy and Gerb er, 2004; Cuske et al., 2005). Typically, many different yet similar models are available for a particular domain like solid waste management (Batstone, 2002). The challenge lies in knowing precisely how two models are similar or different and selecting the one most suitable for a particular task (Yang and Marquardt, 2004). When a particular model is encoded in a conventional programming language, it is ve ry difficult to do comparisons between models. The construction of a model starts by problem definition followed by the development of a conceptual model, mathematical model and the implementation of mathematical model. Initially, the problem is defined as a text or other suitable format. Once the problem is made explicit then the task of conceptualizing the problem takes place. There are different ways to conceptualize a problem (Fishwick, 1995). The conceptual model defines the structure of th e problem and characterizes a system using physiochemical concepts (Yang and Marquardt, 2004), that is, it represents a system as a

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55 network of concepts. For example, a conceptu al model of a solid waste treatment process consists of various concepts like bacteria, fatty acids, ions, and interrelations such as conversion of acids into ions (Lai, 2001). These concepts and interrelations are further represented as mathematical symbols and e quations within a mathematical model. A mathematical model is further implemente d as a simulation. Different tools and vocabulary are used in development of each layer (conceptual, mathematical, simulation code) of the model (Zerr, 2005). As the m odel incorporates new functionalities, the modifications are not made in all layers of the model. For example, the simulation is often modified to incorporat e the new system functionality by modifying the code, but the conceptual model is not updated (Zerr, 2005). Possible Solution for Communicating Knowledge of a Model One of the possible solutions for enhanc ing the communication of knowledge about models at both the researcher/developer and machine level is the use of ontologies. An ontology is an explicit specifi cation of a conceptualizati on (Noy et al., 2000). An ontology contains a set of c oncepts within a particular domain and shows how the concepts are interrelated. On e of the uses of ontologies is management of knowledge. Simulations are used for studying a particular system like a waste management system. They contain knowledge about a specific process in a particular domain. A simulation in the area of solid waste management contains concepts like bacteria, solid waste, and acetic acids. Utilizing ontologies for managing model and simulation knowledge facilitates representing this knowledge in an explicit manner. An ontology provides the model semantics, which allows a computer to inte rpret concepts in an automated manner (Lacy and Gerber, 2004). The construction of ont ologies encourages the development of

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56 conceptually sound models, more effectively communicates these models, enhances interoperability between different models, a nd increases the reusab ility and sharing of model components (Reitsma and Albrecht, 2005 ). It also provides assistance in computation by structuring data (Altman et al., 1999). Web Ontology Language, OWL (OWL, 2005), can be used for describing the ontology of a solid waste management pr ocess called Sequential Batch Anaerobic Composting (SEBAC). The Owl:Class is used for describing generic concepts like bacteria while the specific instances like propionate bacteria are mode led as the Owl: Individual. The Owl:Property is used to define a property of a concept. Two types of properties have been used to model the relationships. The Owl:ObjectProperty models relationships between individuals while Owl:DataTypeProperty models relationship between individuals and data values. E ach property has a domain and range. For example, the concept bacteria has a property called acts on which is used to describe the interaction of bacteria with fatty acids The acts on property is defined with bacteria class as domain and fatty acid class as range.. Applications of Ontologies in Simula tion The notion of combining ontologies with si mulation has received much attention in recent years (Fishwick and M iller, 2004; Lacy and Gerber, 2004; Miller et al., 2004; Raubal and Kuhn, 2004). This section explores several different ways in which ontologies can be applied to simulation, and in particular how ontol ogies can solve some problems in current methods of building simulatio ns for agriculture and natural resources. Model base Many biochemical and physiochemical processes in waste management are fundamental and well studied. For example, anaerobic digestion process has been studied

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57 and used for treating wastewater and solid waste. Many different anaerobic digestion models have been built over the years, but their uses by engineers, waste management operators, and process technology providers ha s been very limited (Batstone, 2002). The International Water Institute has establishe d an anaerobic digesti on modeling task group for developing a generalized anaerobic dige stion model for achievi ng extended usage of anaerobic process knowledge generated by resear ch activities and opera tional experience. The development of such a generalized mode l has many advantages. It will increase the application of models for plant desig n, operation, and optimization. The common vocabulary in the form of a general model w ill also facilitate future model development and transfer of technology from research to industry. Similarly, there are many crop models, but there is no comprehensive management system for managing all these models. Research is being done to de velop a suite of crop models for a variety of crops and integrat e these crop models with weeds and insects models (Agriculture Resear ch Service, 2005). Many othe r crops can be modeled by assembling modules from available models and changing few parameters and rate equations. However, having so many differe nt yet similar models causes problems in managing models and in sharing model components among developers. There is unnecessary redundancy resulting from poor communication among developers. For example, there may be as many as two dozen irrigation models that all basically operate on the principles of water balance. They may use similar ways of calculating processes such as evapotranspiration, or they may use different equations to achieve the same results. Unfortunately, the traditional methods for creati ng these models make it very difficult to compare the models to see how they are similar or different.

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58 An ontology can be used to build a database of models, that is, a model base, that can help to classify different but similar models and that can be searched to locate models and model components suitable for a particular application. Each sp ecific model can be represented by an instance in the ontology and abstract mo del structure and behaviors represented as classes. Similar models can be grouped together into a class, and neighboring related classes groupe d together to form subcla sses. At the top of the resulting taxonomy would be generic modeling approaches. If an ontology is also used to represent the internal structur e of a model, then model inte rnals can be compared in an automated fashion to determine which parts of the model are similar and which are different. The vast collection of models and model co mponents resulting from this analysis would create a large but organized taxonomy. This taxonomy could be searched using query processors based on ontology reasoners (as explained in section Reasoning) to locate models (and model components) of intere st. It can also be used to compare and contrast two models and explicitly identify how they are different or similar. System structure System structure can take many forms incl uding a geometric structure, a chemical structure, or a physiological st ructure. The use of object-oriented design for analysis of system structure is well known and is one of the first applications of object-oriented programming dating back to the 1960s. The biological and physical systems in agriculture and natural resources are analyzed in this fashion by decomposing a complex system into simpler interconnected parts and subparts. Modular, ob ject-oriented designs are widely used (Beck et al., 2003; Kiker, 2001). Of course, traditional object-oriented design uses programming languages such as Ja va or C++ as a representation language.

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59 Using an ontology is the next step in this approach (Fishwick and Miller, 2004). There are several advantages to elevating the objec t comprising the system to the status of ontology objects. For one, the model description and behavior is forced to be done in an entirely declarative fashion (representati on based on concepts and relationships). Ontologies do not utilize methods or program c ode to represent the behavior of model. By using ontology objects, model components can be classified and interrelated based on their meaning. System structure is made e xplicit in a way that can be exploited by ontology reasoners in order to compare and contrast model structures. Representing Equations and Symbols in a Model Model behavior can be described en tirely using mathematical equations (Cuske et al., 2005). Equations are composed of symbols, and each of these symbols can be represented as a concept in th e ontology. This enables the sy mbols meaning to be more exposed and accessible to analysis and manipula tion than if the symbols were encoded as a computer program. Whereas equations descri be the quantitative behavior of variable, the variables are also symbols, and the things the symbols represent can be made explicit. Furthermore, the basic mathematical operato rs can also be treated as symbols and described in the same fashion. Equations can be stored in the ontology by representing them as tree structures. For example the formula: NH4 + = Nt NH3 (Equation 4-1) can be expressed using the tree structure in Figure 4-1. The tree is rooted on the equal symbol, and equal has a left si de and right side which are th e first two branches in the tree. Operators, such as minus, are nodes in the tree with subt rees for each of the operator arguments. Each node in the tree including operators and variables, become

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60 concepts in the ontology. Each concept incl udes associations to related concepts, for example minus contains associations to the concepts being subtracted. Figure 4-1. Representation of e quation 4-1 as a tree structure The advantage of better defining symbol s appearing in equations is improved interoperability of concepts and associated symbols appearing in different models. In addition, with the inclusion of basic opera tors, the ontology can classify groups of equations and organize them taxonomically from generic forms to specific applications. This will lead to discovery of similarities in forms of equations used in different models, and will help to communicate among di fferent modelers (Altman et al., 1999). While an ontology is a valuable tool fo r representing the meaning of the symbols appearing in equations, it has no facilities for solving equations or even performing simple arithmetic operations needed to do si mulation. Although it is possible that an ontology language such as OWL could be extended to support analytical equation solving, this area has not been explored a nd goes beyond the scope of ontology reasoners. Instead, whereas the ontology acts as an excellent library for equations and their symbols,

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61 external facilities are needed to solve the equa tions. An external code generator can take equation structures that are stored in the ont ology and produces XML, or program code in C++ or Java (or other languages) th at can implement the simulation. Reasoning The power of ontologies lies not only in their ability to provide declarative representations of concepts and their relationships, but also the ability to automatically reason about those concepts. Basic reas oning facilities include ontology validation, automatically determining subsumption relations hips (determining if class A is a subclass of class B), and classificati on (automatically determining the location of a new class within the class taxonomy). Extended f acilities included automatic clustering (conceptual clustering) of concepts, and anal ogical reasoning or sim ilarity-based queries and case-based reasoning. These facilities can be applied to simulation in order to automatically classify models, model component s, and the equations and symbols used in the models. Query facilities based on reas oning would help to locate simulation elements within a large collection. Clustering techniques can compare the structure of two models and tell how they are similar or different. For example, the knowledge in an ontology of solid waste management domain can be used for automatically generating equa tions based on physio-chemical equilibrium laws. A particular law can be applied base d on the specific propert y of an individual symbol. In the SEBAC simulation fatty acid s dissociate into fatty acid ions based on a physio-chemical equilibrium law, and that law is represented by an equation. The reasoner can automatically instantiate an equation corresponding to the law when it finds that an individual of the fatty acid class has a property call ed in equilibrium with and

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62 the range of the property is fatty acid ion. It would use th e particular properties of the individuals involved to pa rameterize the equation Generating and Integrating Docume ntation and Training Resources If the ontology is part of a complete database management system, the ontology can store and organize any content, including multimedia content in the form of rich text, images, 2D/3D animations, and video. In the context of simulation, this creates a complete environment for all information associ ated with the simulation. In particular, all research materials (experimental procedur es, raw data, statistica l analysis, technical reports, journal articles) and educational resources (traini ng-based simulations, scenario training, case studies) can be integrated. How to Build an Ontology-Based Simulation: Bioprocessing Example Sequential Batch Anaerobic Composting (S EBAC) is an anaerobic digestion process that decomposes organic matter into methane and carbon dioxide by a series of reactions in the presence of several microorganisms. The details of the SEBAC are explained in the section Domain Studied of chapter 2. A mathematical model was developed to understand the SEBAC system an d to study the response under various feed conditions (Annop et al., 2003). The model cons ists of a set of differential equations, which have been constructed based on ma ss balance and physio-chemical equilibrium relationships. This study did not implement the tools (SimulationEditor and EquationEditor) but used thes e tools for developing ontol ogy-based simulation for the SEBAC process. The steps in building the SEBAC model based on ontology are as follows:

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63 Collection of Relevant Documents The first step in building an ontology-based simulation was to collect all relevant documents such as technical papers of the sy stem and any existing related models. In the case of the SEBAC simulation, an existing model had already been implemented using Matrix Laboratory (MATLAB) (Lai, 2001),. Available documents included a graduate thesis describing the variable s and equations used in the model (Lai, 2001), a research publication describing the implementation of the mathematical model (Annop, 2003), and source code of the MATLAB implementation. It would have been useful to have acc ess to a conceptual model for understanding the conceptual schema of the system. A si mple conceptual model of the SEBAC process was sketched for understanding the SEBAC do main. Figure 4-2 s hows the conceptual model with nine concepts (Owl:Class) and th ree types of interacti ons or relationships (Owl:ObjectProperty). These concepts have individuals which can be mapped to the variables used in the simulation. There we re six individuals of bacteria and six individuals of fatty acids in the SEBAC system which could be mapped to the state variables of the model. Define Model in Terms of Elements The next step was to define the model in te rm of elements. Elements were used to modularize the model into logical units. Re lated classes, individuals, properties and equations were entered in a particular element. The description of the model in terms of elements was helpful in understanding the stru cture of the model. Typically, a modeler designs a particular model by creating a gra ph containing elements and links indicating the information flow between elements. SimulationEditor (Figure 4-3) was used for building the model structure in the form of an element graph. SimulationEditor also

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64 contained facilities for automatically gene rating and running simulations and generating reports. The SEBAC simulation involved a biologi cal process. The simulation was described in terms of elements which capture d the important proce sses like bioconversion of fatty acids and substrate and dissociation of fatty acids. Figure 4-3 shows the elements of the SEBAC simulation and gives an overvie w of the SEBAC process including various transformations that occur during the process. Figure 4 2. Conceptual model of the SEBAC system Identifying Classes, Individuals and Properties After defining the general elements of th e model, specific concepts in the model were identified. For the SEBAC system, the concepts were identified from the list of variables used in the model (Lai, 2001). From these, the following classes with the

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65 corresponding properties (constant s, parameters, yield coeffici ents and variables) were created: Reactor: liquid volume, gas head space, reactor temperature Fatty acid ion: equilibrium constant for dissociation, conversion factor Fatty acid Bacteria: biomass death rate, half veloci ty constant, maximum growth constant Methane Carbon dioxide Soluble substrate and insoluble substrate Figure 4-3. SimulationEditor diagram for SE BAC process showing elements of SEBAC simulation and showing various transfor mations that occur during the process Some of these classes had several indivi duals. For example, there were three individuals of fatty acid ion, and each fatty aci d ion had a specific value of equilibrium constant for dissociation and conversion factor. Relevant classes, individuals, and

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66 properties were entered in a particular elem ent. Figure 4-4 show s how an individual called Ammonium ion was entered into th e ontology database. The other classes, individuals and properties we re entered into the databa se in a similar fashion. Figure 4-4. Interface of Equati onEditor to input the concepts in a particular element of the simulation In conventional modeling languages, the meaning of the symbols and the relationships between the symbols are not de fined explicitly. For example, the SEBAC model had symbols for various forms of n itrogen such as ammonia, nitrate, and ammonium ion, but the simulation written in MATLAB does not explicitly specify the relationship between these forms of nitrogen or the meaning of each form. The meaning

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67 of the symbols and relationships can be defi ned explicitly using an ontology. Figure 4-5 shows a portion of the ontology for different forms of nitrogen. Figure 4-5. Ontology for different forms of nitrogen In the SEBAC model, total dissolved nitr ogen was found in the form of ammonia which in turn could be found in two forms: ammonium ion (NH 4 + ) and dissolved ammonia gas (NH 3 ). In Figure 4-5 there is a relationship called consists of with a domain of total dissolved nitrogen and a range of forms of ammonia (NH 4 + NH 3 ). Ammonium ion concentration wa s calculated by the difference of total dissolved nitrogen and ammonia. NH 4 + and NH 3 were in equilibrium, and th eir concentration is given by the equation: NH 4 + NH3 + H + (equation 4-2)

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68 Figure 4-5 displays a property called in equilibrium with having NH 4 + as a domain and NH3 as a range. This property modeled a reversible conversion between the two forms of ammonia (NH 4 + and NH 3 ). Ammonia was defined as a specific kind of gas, so it was also a subclass of the class gas. Define Equations The equations describing dynamic behavior were entered in the system after entering the classes, individuals, and propert ies of symbols which were used in the equation. Figure 4-6 shows the interface of E quationEditor for entering an equation that represents the relationship between total di ssolved nitrogen, amm onia, and ammonium ion concentration. Figure 4-6. Interface of the E quationEditor for entering equation An equation models the dynamic relations hip between concepts (classes) and represents a statement of a specific law. The Michaelis-Menten equation (Heidel and Maloney, 2000) models a relationship between acid and bacteria. In the SEBAC system, Acetic acid is an individual of the acid class and acetolistic methane bacteria is an

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69 individual of the bacteria class. The aceto listic methane bacteria acts on acetic acid, and this relationship can be modeled by Michaelis -Menten equation. These relationships can be explicitly shown in ontology as properties. It is also possible to store the specific laws in the ontology so that the e quations can be automatically ge nerated based on the specific relationships between individuals by using an ontology reasoner. Enter the Initial Values of State Variables Initial values of state variables were en tered manually using an input form which was generated automatically based on the logic that each differential equation has a state variable and that an initial value was re quired for each state variable. The SEBAC simulation has twenty one state variables, so the input form has twenty one text fields. The value of constants (like the universal gas constant) and other parameters used in the simulation were stored in th e ontology as properties of in dividuals representing these constants. Generating Program Code for Implementing the Simulation Program code for running the simulati on was automatically generated by processing the descriptions of model struct ure and behavior (equa tions) stored in the ontology. Currently, the system generates Java code, but other languages can also be supported. The code generation involved retr ieving equations and symbols belonging to each element in the ontology database and ma king a reference list of symbols having the hierarchical structure of operators in each e quation. A Java class was generated for each element of the simulation (mainly to partiti on the code into logical modules). The symbols for variables belonging to an element were generated as member variables in the Java class while the equations were generated as Java methods. Each method returned a value for a particular variable based on an equation defined for that variable. For

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70 example, a Java method was generated corr esponding to the amm onia balance equation, as shown in Figure 4-6, that returns the value of NH 4 + Execution of Simulation After generating the Java code, the co de was compiled and the simulation was executed. The simulation results were presente d in the form of charts and tables. In order to enhance interpretation, the results of the simulation c ould also be presented as an animation. The dynamics of the SEBAC simulation were shown in term of reactors that change colors based on pH and other chemical properties of the system (Figure 4-7). The ontology facilitated creating these animated interfaces by storing graphic objects as described in chapter 3 that could be us ed to render an animation along with the associated model concepts. Figure 4-7. Interface for presenting resu lts of SEBAC simulation using animation

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71 Conclusions This chapter explored several ways in which ontologies can be applied to mathematical modeling. As an example, an ontology based simulation was developed in the bioprocessing domain. The development process involved seven steps including collection of relevant documents; defining th e model in terms of elements; identifying classes, individuals, and propert ies; encoding equations; entering initial values of state, constant, and other parameters; generati ng code; and executing the simulation. The development of an ontology for si mulation models explicitly exposes knowledge contained in models at a higher level. This knowle dge can be further used for constructing conceptual models, simulations of similar systems, and educational and training materials. The construction of an ontology will allow better communication of knowledge about models, model elements, and data sources among different modelers; It will enhance interoperability between different models, increase the reusability and promote sharing of model components.

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CHAPTER 5 CONCLUSIONS, CONTRIBUTIONS, AND FUTURE DIRECTIONS Conclusions Documenting Research Information The presented work showed that the cont ent management approach (i.e., using a database to store research information) can be used for documenting research information. The information was first structured as an ontology (structured information) and stored inside an object database (an ontology manageme nt system). This approach allowed the documentation of research at a ve ry fine level (i.e., doc umenting research at the level of concepts used in various research projects) instead of storing the educational materials at only a course level in the form of documents, presentations or other formats that fail to explicitly represent content. There can be an overlap of concepts used in various projects, and the overlap of concepts can be used for identifying similarities in various projects. For example, both SEBAC pr oject and BMP project used the concept of anaerobic digestion, so the overlap of concep ts in ontology can infer the similarity in BMP project and SEBAC project. Methodology for Generating Educational Material by Reusing Information The structured information was used for cr eating a variety of educational materials such as websites, animation and reports. A JSP application was developed for creating Web pages from an ontology and a Java an imation was developed for explaining the dynamic processes used in the research proj ects. The concepts used in the dynamic process were stored in the database, and these concepts can also be accessed as a website. 72

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73 It was shown that the ability to reuse inform ation in a variety of formats has the following advantages: Duplication of efforts is minimized: The content management approach decreases the cost and time for producing educational materials. Enforcement of information integrity: Us ing an ontology as a single source of information enforces information integrit y within an organization like ES CSTC. The information can be updated and verified at a central location (ontology database) instead of checking the accuracy of information in various formats. Separation of presentation and content: The presented approach allows the separation of content from presentation wh ich allows updates or modifications in presentation without changing content and vice versa. Presenting Dynamic Information of a Lab Exercise as Educational Simulation Based on the results of the evaluation of simulations, it can be concluded that the simulation can be used to effectively teach a lab exercise. The effectiveness of simulations also depends on the approach of integrating simulations in the instruction, that is, the simulations can be either shown to the students as a de monstration or students can run the simulations on their own machine. The confidence of a student in the lab concepts increases when the student ran the simulation on his/her computer. The use of simulation helps in teaching the lab where it is practically infeasible to teach the lab as a hands-on approach because of the high cost of the equipment and chemicals involved. Simulations can also serve as a replacement experience for universities and colleges that do not have a waste management laboratory. The computer-based simulation can also be used to augment the real laboratory experience. Representing Knowledge of a Ma thematical Model by Ontology The development of an ontology for ma thematical models explicitly exposes knowledge contained in models at a higher level. This knowle dge can be further used for constructing conceptual models, simulations of similar systems, and educational and

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74 training materials. The construction of an ontology will allow better communication of knowledge about models, model elements, and data sources among different modelers; enhance interoperability between different models; and increase the reusability and sharing of model components. Contributions This project illustrated a content manage ment approach combining an ontology and database for structuring and storing content. It faci litated the development of simulations and other educational res ources. This approach was used for developing educational materials in the domain of waste management technologies at ES CSTC. The dynamic information of a project (pro cess, lab exercise) was presented as a simulation. The interactivity of the medium was beneficial in showing the concepts effectively. The simulations were evaluated in classroom settings at the University of Florida. A library of Java classes was develope d during the generatio n of simulations. These Java classes can be reused to create similar simulations. An ontology for three projects was created for generating educational material in the domain of solid waste and wastewater. The ontology can be used in generating reusable learning material. The process of generating an ontology for a simulation or mathematical model revealed a new approach of representing models in terms of concepts and relationships between concepts. Future Directions Ontology-Based Instruction Design This study focused on representing the cont ent of educational material as an ontology. During the development of the website it was realized that the instructional design could be a next step for represen ting the concepts of ontology in a learning context. An ontology of instructional design can be integr ated with an ontology of research projects for developing courses (Se plveda-Bustos et al., 2006). Furthermore,

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75 future work can also be focused on devel oping a SCORM compliant course using domain ontologies that were created during this project. Ontology Reasoning The presented project did not focus on ontology reasoning, which is an important function of ontologies. The ont ology reasoners could be used for selecting appropriate teaching method based on the ontology of instructional design and validating instructional material. Future work can also involve application of reasoners in modeling. One of the application of reas oners is generation of equations based on relationships between different concepts in a model. Development of Tools for Developing Online Lesson The generic tools used in the presented project were appropriate for developing ontologies but were not specifically designed for producing online lessons. Future work should also be focused on developing custom authoring tools that can more rapidly address instructional design issues because th ey support features ta ilored specifically to development of educational materials.

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APPENDIX A EVALUATION FORM OF BMP SIMULATION Name : Student # Section 1. evaluation: Please do the evaluati on after reading the lab handouts or performing virtual simulation. Part a: Technical evaluation (multiple choice) 1. Objective of the experiment? 1. To estimate biochemical methane potentia l of a substance and estimate the rate of anaerobic degradation 2. To measure biochemical methane potential 3. To determine Aerobic digestibility 4. To measure Aerobic digestibility 2. How many stock solutions are used? 1. 6 2. 7 3. 8 4. 5 3. Inoculum is added at --degree C. 1. 29 2. 30 3. 32 4. 35 4. What is difference between aspirator bottle and serum bottle? 1. Aspirator bottle is used for making medium and serum bottle is used for storing stock solution. 2. Serum bottle is used for making medium a nd Aspirator bottle is used for storing sample 3. Aspirator bottle is used for making medium and serum bottle is used for containing the bioassay 4. Aspirator bottle is used fo r storing stock solution a nd serum bottle is used for storing sample 5. Purging is done with which gas? 1. N2 2. H2 76

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77 3. O2 4. N2 CO2 6. Purging is done for removing which gas? 1 N2 CO2 2. H2 3. O2 4. N2 7. What is in the inoculum digester? 1 Anaerobically digested dog food and mixed cultured bacteria 2 Aerobically digested dog food, mi xed cultured bacteria and water 3 Aerobically digested dog food, and water 4 Anaerobically digested dog food, mi xed cultured bacteria and water 8. What volume of inoculum is added to the media? 1. 20% by volume 2 10 ml 3 40 % by volume 4 50 ml 9. What does heated copper column do? 1 Absorbs CO2 from the gas stream 2. Absorbs O2 from the gas stream 3. Absorbs N2 from the gas stream 4. Absorbs N2-CO2 from the gas stream 10. What are the functions of Na2S and Reazurin 1. Na2S is redox indicator a nd Reazurin is a reducing agent 2. Na2S is reducing agent and Reazurin is a reducing agent 3. Na2S is reducing agent and Reazurin is a redox indicator 4. Na2S is redox indicator and Reazurin is a redox indicator Part b: Subjective evaluation Please rate each of the following in connecti on to your experience of BMP lab from 1 to 5 where: 1 is the lowest priority, and 5 is the highest priority. 1 2 3 4 5 1. I am encouraged to learn 2. I am confident with the concepts used in BMP lab. 3. Enabling student to work through c ourse materials at their own pace 4. Developing students creativity and skills 5. Applying what you are learning to "real world" situations 6. Teaching students to work together

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78 To what extent do you agree or disagree with each of the following statements regarding the BMP lab: (select only one response per question) Strongly Agree (SA), Agree (A), Disagree (D),,Strongly Disagree,(SD), Not Applicable(N) 7. The learning experience was interactive 8. I had a good learning experience

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APPENDIX B EVALUATION FORM OF MAPR SIMULATION Name: Student I.D.: Virtual experiment evaluation: Please do the evaluation afte r reading online lesson and performing simulation (animation) Part a. Objective Evaluation Evaluation of Online Lesson 1. The semiconductor used in photocatalysis is 1. Barium ferrite 2. Titanium dioxide 3. Silica 4. Activated carbon Ans: 2. What is the primary oxi dant in photocatalysis? 1. Hypochlorous acid 2. Hydrochloric acid 3. Hydroxyl radical 4. Hydroxyl ion Ans: 3. What are the organic molecules conve rted to when oxidation is complete? 1. UV light 2. Carbon dioxide, water, and mineral acids 3. A polymer 4. Sulfur dioxide and nitrous oxide Ans: 4. Which type of electro n is excited to form an electron/hole pair? 1. A conduction band electron 2. A valence band electron 3. A hot electron 4. All of the above Ans: 79

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80 5. The purpose of magne tic agitation is to 1. Activate the catalyst 2. Begin the breakdown of the contaminant 3. Maximize photocatalysis 4. Keep the system stable Ans: Evaluation of Simulation 1. The alternating cu rrent magnetic field is generated by 1. Solenoid 2. UV light 3. Wastewater 4. all of the above Ans: 2. Maximum performance of the MAPR is at what solenoid frequency 1. 20 Hz 2. 80 Hz 3. 120 Hz 4. Performance is same between 20 and 80 Hz Ans: 3. Which of the following is *not* used in MAPR experiment 1. Heater 2. UV light 3. Frequency generator 4. Spectrophotometer Ans: 4. What is one of the methods used for determining the extent of photocatalysis? 1. Counting by hand 2. Spectrophotometer 3. MAPR 4. Inductively coupled plasma Ans: 5. Which of the following is true about MAPR process ? 1. Only magnetic agitation is requ ired for effectively treating wastewater 2. There is no effective trea tment of wastewater without UV light 3. There is no effective treatment of wastewater withou t magnetic agitation 4. Effective wastewater treatment requires UV light and magnetic agitation Ans:

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81 Part b: Subjective evaluation Please rate each of the following in conn ection to your experience of MAPR lab from 1 to 5 where: 1 is the lowest pr iority, and 5 is the highest priority. 1 2 3 4 5 1. I am encouraged to learn 2. I am confident with the concepts used in MAPR lab 3. Enabling student to work through c ourse materials at their own pace 4. Developing students creativity and skills .. 5. Applying what you are learning to "real world" situations .. 6. Teaching students to work together To what extent do you agree or disagree with each of the following statements regarding the MAPR lab: (select only one response per question) Strongly Agree (SA), Agree (A), Disagree (D),,Strongly Disagree,(SD), Not Applicable(N) 7. The learning experience was interactive 8. I had a good learning experience ..

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BIOGRAPHICAL SKETCH Rohit Badal was born on January 4, 1976, in De was, India, to Saroj Badal and R.S. Badal. He was formally educated at St. Marys Convent School at Dewas, India, and passed the pre-engineering exam at the st ate level (Madhya Pradesh) for starting Bachelor of Technology program in Chemical and Biological Engi neering at Regional Engineering College, Jalandhar (Now known as the National Institute of Technology, Jalandhar). He completed the Bachelor of Technology in 1998 and joined Omen Drugs Corporation as a Trainee Engineer for one year. On August 1999, he entered the graduate program at Louisiana State Univ ersity. From August 1999 to July 2002, he worked as a Research Assistant at the Department of Biological and Agricultural Engineering, LSU, and completed his masters thesis on Supercritical Carbon Dioxide Extraction Of Lipids From Raw And Bioconverted Rice Bran under Dr. Terry Walker. After completing his Master of Science, he st arted the doctoral program at the University of Florida and was awarded a research assist antship under Dr. Howard Beck in the area of information technology. 90