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Article

A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software

Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece
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Academic Editors: Antonio Sarasa Cabezuelo and Covadonga Rodrigo San Juan
Received: 29 September 2021 / Revised: 27 October 2021 / Accepted: 28 October 2021 / Published: 29 October 2021
(This article belongs to the Special Issue Present and Future of E-Learning Technologies)
This paper presents a novel cognitive diagnostic module which is incorporated in e-learning software for the tutoring of the markup language HTML. The system is responsible for detecting the learners’ cognitive bugs and delivering personalized guidance. The novelty of this approach is that it is based on the Repair theory that incorporates additional features, such as student negligence and test completion times, in its diagnostic mechanism; also, it employs a recommender module that suggests students optimal learning paths based on their misconceptions using descriptive test feedback and adaptability of learning content. Considering the Repair theory, the diagnostic mechanism uses a library of error correction rules to explain the cause of errors observed by the student during the assessment. This library covers common errors, creating a hypothesis space in that way. Therefore, the test items are expanded, so that they belong to the hypothesis space. Both the system and the cognitive diagnostic tool were evaluated with promising results, showing that they offer a personalized experience to learners. View Full-Text
Keywords: adaptive content; diagnostic model; error diagnosis; learner experience; personalized guidance; repair theory; student bug adaptive content; diagnostic model; error diagnosis; learner experience; personalized guidance; repair theory; student bug
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MDPI and ACS Style

Krouska, A.; Troussas, C.; Sgouropoulou, C. A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software. Computers 2021, 10, 140. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110140

AMA Style

Krouska A, Troussas C, Sgouropoulou C. A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software. Computers. 2021; 10(11):140. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110140

Chicago/Turabian Style

Krouska, Akrivi, Christos Troussas, and Cleo Sgouropoulou. 2021. "A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software" Computers 10, no. 11: 140. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110140

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