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DEVELOPING EDUCATIONAL MATERIALS IN BIOPROCESSING USING AN
ONTOLOGY DATABASE MANAGEMENT SYSTEM
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
To my mother
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
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
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
ACKNOWLEDGMENT S ............ ...... ._._ .............._ iv...
LI ST OF FIGURE S .............. .................... ix
LI ST OF ACRONYMS .............. .................... xi
AB S TRAC T ......_ ................. ..........._..._ xiii..
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
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......
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
A EVALUATION FORM OF BMP SIMULATION. ......____ ........__ ..............76
B EVALUATION FORM OF MAPR SIMULATION ..........._.... .........._............79
REFERENCES .............. ...............82....
BIOGRAPHICAL SKETCH .............. ...............90....
LIST OF FIGURES
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
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
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
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
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.
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
* 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
* 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
* 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
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.
* 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
* 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
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
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.
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
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
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
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.
Chapter 5 summarizes contributions and conclusions, and identifies future
CONTENT MANAGEMENT APPROACH FOR DEVELOPING EDUCATIONAL
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.
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
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).
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
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".
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
* Managing: Storing and maintaining the content and their metadata in a repository
* Publishing: Retrieving and extracting the content for producing information in a
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.,
* 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
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
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
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
Figure 2-1. Components of a content management system used for developing
educational materials -
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
* 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
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
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
* 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
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
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
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
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
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
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
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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
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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
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.
EDUCATIONAL SIMULATION: AN APPROACH FOR PRESENTING DYNAMIC
INFORMATION OF A PROCESS
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.
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
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:
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
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.-....... 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
Figure 3-2. Interface for the BMP laboratory for determining biodegradability of a
sample in interactive mode
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,
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,
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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.
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.
Lun].I.J .I rIJ.-
Figure 3-3. Interface of the SEBAC process for treating solid waste in movie mode.
Figure 3-4. Interface of the SEBAC process for treating solid waste in interactive mode
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.
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.
wastewater concentrate nano pure water
1-~ l .- In ~
Simulation Status _ll H: : :
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
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.
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Figure 3-8. Interface of MAPR laboratory for treating a sample of wastewater in
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
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
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
The results showed the significant difference in the following aspects of subj ective
* 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
* 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 =
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
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
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.
AN ONTOLOGY-BASED APPROACH TO MATHEMATICAL MODELING
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).
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
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
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.
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
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 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.
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.
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
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
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
* 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
* Carbon dioxide
* Soluble substrate and insoluble substrate
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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
qulbimconstant for dissociation
I oal Nitrogen
Ioal nitrogen formation rate
~..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
Show all Equations/Symbols for:
SSEBAC Nitrogen content
Figure 4-4. Interface of EquationEditor to input the concepts in a particular element of
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
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
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.
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
Equations Syrrbols //Units
Nirgncontenrt rate equation NH4_plus = Nt NH3
Nirgnformation rate equation
Show all Equations/Syrrbols tor:
SSEBAC Nitrogen content
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
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
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.
ti115 I fillH fillH 0115l
Figure 4-7. Interface for presenting results of SEBAC simulation using animation
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.
CONCLUSIONS, CONTRIBUTIONS, AND FUTURE DIRECTIONS
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
* 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.
* 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
* 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.
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.
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.
EVALUATION FORM OF BMP SIMULATION
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?
3. Inoculum is added at --- degree C.
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
2. Serum bottle is used for making medium and Aspirator bottle is used for storing
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
5. Purging is done with which gas?
4. N2 CO2
6. Purging is done for removing which gas?
1 N2 CO2
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.
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
7. The learning experience was interactive
8. I had a good learning experience
EVALUATION FORM OF MAPR SIMULATION
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
4. Activated carbon
2. What is the primary oxidant in photocatalysis?
1. Hypochlorous acid
2. Hydrochloric acid
3. Hydroxyl radical
4. Hydroxyl ion
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
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
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
Evaluation of Simulation
1. The alternating current magnetic field is generated by
2. UV light
4. all of the above
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
3. Which of the following is *not* used in MAPR experiment
2. UV light
3. Frequency generator
4. What is one of the methods used for determining the extent of photocatalysis?
1. Counting by hand
4. Inductively coupled plasma
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
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.
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
7. The learning experience was interactive....
8. I had a good learning experience.....
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