The document describes an ontology-driven e-learning environment that uses adaptive testing to identify knowledge gaps. It consists of the following: 1. An ontology is used to structure educational content and provide the underlying logic for an adaptive multiple choice test. 2. The adaptive test evaluates student answers and tailors subsequent questions to map the student's knowledge based on the ontology. 3. Students receive customized learning materials based on the concepts in the ontology that their test responses indicated they do not fully understand. 4. The system aims to minimize guessing on tests and provide individually tailored feedback and learning instructions to bridge differences in competencies between educational levels.

1. Ontology
Driven
E-Learning
Environment
Dr. András Gábor, Corvinno Ltd
Hungary

2. Adaptive testing: the new method of e-learning
• Ontology based knowledge gap discovery system
• Multiple choice test combined with modularized eLearning
material
• The novelty: the underlying logic of test question answers are
evaluated with the help of domain ontology
• Customized learning instructions according to the learners
actual knowledge
• Harmonization of knowledge of novice imput level
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3. Abstract
• One of the most challenging problems is that the outputs of different bachelor
programs (1st cycle) do not provide homogeneous input for a given master program
(2nd cycle). Accordingly the primary objective of our approach is to provide support in
exploring missing knowledge areas of candidate students in the frames of an
electronic learning environment in order to help them to complement their educational
deficiencies.
• The ontology-based approach provides support for capturing regularities in a single
framework, general enough to model the curriculum content management
requirements of multiple institutions.
• Content Management System (CMS) is specialized for the needs of the ontology-
driven environment. Content is also structured according to the ontologies, meaning
that every concept in the ontology is connected to a specific piece of content,
describing details or relations of the concept with other items in the same ontology.
• In the course of testing the Adaptive Testing Engine walks through the ontology
structure and asks questions about concepts in the ontology. It evaluates the student's
answers and decides on the following knowledge elements to be tested. At the end,
the user's knowledge is mapped thoroughly and a tailored learning content is offered.
The customized material consists of learning objects, which is part of the Content
Management System (CMS).

4. Adaptive testing: the new method of e-learning
• Adaptive Testing minimizes the ”lucky strike” in answering MC tests
• Individually tailored feedback and guided learning instruction
• Combined with relevant learning material
• Domain independent framework, adaptable to any learning domain
• Mobility requirements between knowledge levels
• Significant differencies in competencies between the output of the previous level
and input of the next level
• Leveling of knowledge
• Strong mobility driver bridging over the knowledge gaps
• Efficient and cost effective tool for HR in corporate training
 Missing knowledge can be precisely discovered
 Customized learning content can be delivered
 As a by product, the domain ontology serves as company knowledge base
 Competence based job profile creation
 On the job training
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5. Technology Description
• The „bad teacher’s attitude”
 What the learner does NOT know?
 Evaluation is based on the domain ontology
 Multiple choice type questions
• The incorrect answer is OK
• If the answer is correct, than the underlying knowledge is tested
• If the underlying knowledge testing is OK, than the answer is accepted
• If the underlying knowledge testing is NOT OK, than the answer is not accepted
• Output: Comprehensive list of the incorrect answers
 explanation (why it was incorrect)
 customized learning material (what has to be study)
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6. Educational Ontology
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7. Components
• Ontology building
 Domain ontology development
 Controlled use of ontology editor
• Content Authoring
 Semantic MediaWiki
 Scorm compatibility
 Multimedia
 Embedded applications
• Repository
 Multimedia elements
 Competence based knowledge elements
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8. Components
• Packaging
 Seamlessly integrated
 LMS (Learning Management System)
 LCMS (Learning Content Management System)
 Authetication system
• Adaptive Test Engine
 Test Editor
 Test Bank
• External Modules
 MS Power Point Slideshow
 Adobe PDF
 HTML format – also accessible for some WAP browsers
 FLASH format
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9. Coospace Coospace Extended
(Community Server)
Ontology
………. Test repository
E-learning
Authentication material
Coospace
Administration
Learner Sales
Registration
Accounting Adaptive Testing

10. Learning Infrastructure
S tudent mL MS (C ooS pace)
Adminis tration
Adaptive Testing Engine
E xternal Modules
Educational Packaging
Repository
Ontology
Content
Developer Test Bank
Test Item
Ontology Editor
Editor
ONTOLOGY-BASED AUTHORING ENVIRONMENT
ONTOLOGY DRIVEN ENVIRONMENT
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11. The process
1. Selection of the domain
2. Building ontology
3. E-learning material development
(multimedia components, wiki)
4. Multiple test questions
5. Interlink the learning materials,
MCQ and ontology
6. Integrating adaptive testing into
LMS (CooSpace, Moodle)
2012. 11. 04. Corvinno Technology Transfer Center 11

12. Ontology modell of decision theory

13. Ontology editor
Classes
Instance
attributes
Class
instances

14. Decision theory - grid
Problem space
X
Problemsolving
X 0,2
0,3
Optimal decision
Decision Satisfactory decision
0,5
Rational
Problem-category
X 0,6 Decision making X
State space
0,4
representation
0,4
Decision
Support
systems
Decision theory
Modell
X Normative
Decision modell X
Decision environment
Human being
X 0,6
Descriptive decision
modell X
An information
Processing entity
Functional separation
Of human brain
Knowledge levels
Modell construction
levels
Decision classes

15. Value
• In-depth knowledge gap analysis
 Exhaustive explanation
 Customized learning material
• Several learning methods and pace can be applied
• Detailed statistics and analysis of MC questions
 Correct answers’ distribution
 Incorrect answers’ distribution
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16. Potential Challenges
• Cultural challenges
 No tradition of eLearning
 Minimal disciplinary control on the learner
 Correct and in-time feedback
• Didactical / Pedagogical challenges
 Minimum contact hours
 Customized and personnel contacts
 Solving tests vs. Learning
• Business challenges
 No tradition of individual use
 Scaleable pricing, intelligent value transfer
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17. Thank you for
your attention…
…Q & A!
agabor@corvinno.hu