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
2012. 11. 04. Corvinno Technology Transfer 2
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
2012. 11. 04. Corvinno Technology Transfer 4
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)
2012. 11. 04. Corvinno Technology Transfer 5
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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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
2012. 11. 04. Corvinno Technology Transfer 10
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
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
2012. 11. 04. Corvinno Technology Transfer 17