ontology driven e-learning environment

Ontology Driven E-Learning Environment

Dr. András Gábor, Corvinno Ltd

Hungary

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

2023.04.18. Corvinno Technology Transfer 2

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).

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


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)


Educational Ontology


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


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


Coospace

Learner Registration

Authentication

Administration

Sales

Accounting Adaptive Testing

OntologyTest repositoryE-learning material

Coospace Coospace Extended(Community Server)

……….

Learning Infrastructure


E xternal Modules

mL MS (C ooS pace)S tudent Adminis tration

ONTOLOGY-BASED AUTHORING ENVIRONMENT

Ontology Editor

EducationalOntology

RepositoryPackaging

Test Bank

Content Developer

Adaptive Testing Engine

ONTOLOGY DRIVEN ENVIRONMENT

Test ItemEditor

E xternal Modules

mL MS (C ooS pace)S tudent Adminis tration

ONTOLOGY-BASED AUTHORING ENVIRONMENT

Ontology Editor

EducationalOntology

RepositoryPackaging

Test Bank

Content Developer

Adaptive Testing Engine

ONTOLOGY DRIVEN ENVIRONMENT

Test ItemEditor

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)

2023.04.18. Corvinno Technology Transfer Center 11

Ontology modell of decision theory

Ontology editor

Classes

Class instances

Instance attributes

DecisionDecision Satisfactory decisionSatisfactory decision

Optimal decisionOptimal decision

ModellModell NormativeDecision modell

NormativeDecision modell

Descriptive decisionmodell

Descriptive decisionmodell

Problem-categoryProblem-category

State spacerepresentation

State spacerepresentation

RationalDecision making

RationalDecision making

Decision theoryDecision theory

ProblemsolvingProblemsolving

Problem spaceProblem space

Modell constructionlevels

Modell constructionlevels

Knowledge levelsKnowledge levels

Functional separationOf human brain

Functional separationOf human brain

Human beingAn information

Processing entity

Human beingAn information

Processing entity

Decision environmentDecision environment

Decision classesDecision classes

XX

X

X

XX

Decision theory - grid

X

XDecisionSupportsystems

DecisionSupportsystems

0,2

0,5

0,3

0,6

0,4

0,4

0,6

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


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


Thank you for your attention…

…Q & A!

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