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Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
Ontology Driven E-Learning Environment
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Ontology Driven E-Learning Environment

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Ontology Driven E-Learning Environment. Latin American Council of Management Schools (CLADEA)

Ontology Driven E-Learning Environment. Latin American Council of Management Schools (CLADEA)

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  1. OntologyDrivenE-LearningEnvironmentDr. 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 level2012. 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 students answers and decides on the following knowledge elements to be tested. At the end, the users 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 training2012. 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
  6. Educational Ontology2012. 11. 04. Corvinno Technology Transfer 6
  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 elements2012. 11. 04. Corvinno Technology Transfer 7
  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 format2012. 11. 04. Corvinno Technology Transfer 8
  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 ENVIRONMENT2012. 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
  12. Ontology modell of decision theory
  13. Ontology editorClasses 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 0,4DecisionSupportsystems 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’ distribution2012. 11. 04. Corvinno Technology Transfer 16
  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 transfer2012. 11. 04. Corvinno Technology Transfer 17
  17. Thank you foryour attention… …Q & A! agabor@corvinno.hu

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