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Understanding E-learners' Behaviour Using Data Mining Techniques

Published: 30 March 2019 Publication History

Abstract

The information from Higher Education Institutions (HEIs) is primarily relevant for decision maker and educators. This study tackles e-learners behaviour using machine learning, particularly association rules and classifiers. Learners are characterized by a set of behaviours and attitudes that determine their learning abilities and skills. Learning from data generated by online learners may have significant impacts, however, few studies cover this resource from machine learning perspectives. We examine different data mining techniques including Random Forests, Logistic Regressions and Bayesian Networks as classifiers used for predicting e-learners' classes (High, Medium and Low). The novelty of this study is that it explores and compares classifiers performance on the behaviour of online learners on four variables: raise hands, visiting IT resources, view announcement and discussion impact on e-learners. The results of this study indicate an 80% accuracy level obtained by Bayesian Networks; in contrast, the Random Forests have only 63% accuracy level and Logistic Regressions for 58%.

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Cited By

View all
  • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
  • (2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122(106071)Online publication date: Jun-2023
  • (2022)Use of Data mining Tools in Educational Data Mining2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT)10.1109/CCiCT56684.2022.00075(380-387)Online publication date: Jul-2022
  • Show More Cited By

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    ICBDE '19: Proceedings of the 2019 International Conference on Big Data and Education
    March 2019
    146 pages
    ISBN:9781450361866
    DOI:10.1145/3322134
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Univ. of Greenwich: University of Greenwich

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 March 2019

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    Author Tags

    1. Accuracy
    2. Association Rules
    3. Bayesian Networks
    4. Precision
    5. Radom Forests

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    • Refereed limited

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    ICBDE?19
    ICBDE?19: 2019 International Conference on Big Data and Education
    March 30 - April 1, 2019
    London, United Kingdom

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    Cited By

    View all
    • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
    • (2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122(106071)Online publication date: Jun-2023
    • (2022)Use of Data mining Tools in Educational Data Mining2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT)10.1109/CCiCT56684.2022.00075(380-387)Online publication date: Jul-2022
    • (2022)Big Data in Education: Present and FutureComputational Intelligence in Data Mining10.1007/978-981-16-9447-9_54(721-739)Online publication date: 7-May-2022

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