Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes
A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7083 |
| _version_ | 1797525794278342656 |
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| author | Daiva Goštautaitė Jevgenij Kurilov |
| author_facet | Daiva Goštautaitė Jevgenij Kurilov |
| author_sort | Daiva Goštautaitė |
| collection | DOAJ |
| description | A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment. |
| first_indexed | 2024-03-10T09:19:02Z |
| format | Article |
| id | doaj.art-9b5fa4550d0242f8b5d80d3f4d5c6998 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:19:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9b5fa4550d0242f8b5d80d3f4d5c69982023-11-22T05:24:09ZengMDPI AGApplied Sciences2076-34172021-07-011115708310.3390/app11157083Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis PurposesDaiva Goštautaitė0Jevgenij Kurilov1Departament of Information Technologies, Vilnius Gediminas Technical University, LT-10223 Vilnius, LithuaniaDepartament of Information Technologies, Vilnius Gediminas Technical University, LT-10223 Vilnius, LithuaniaA lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment.https://www.mdpi.com/2076-3417/11/15/7083exemplar-based modelcase-based reasoningnearest neighborslearning styleBayes networksimilarity |
| spellingShingle | Daiva Goštautaitė Jevgenij Kurilov Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes Applied Sciences exemplar-based model case-based reasoning nearest neighbors learning style Bayes network similarity |
| title | Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes |
| title_full | Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes |
| title_fullStr | Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes |
| title_full_unstemmed | Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes |
| title_short | Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes |
| title_sort | comparative analysis of exemplar based approaches for students learning style diagnosis purposes |
| topic | exemplar-based model case-based reasoning nearest neighbors learning style Bayes network similarity |
| url | https://www.mdpi.com/2076-3417/11/15/7083 |
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