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Vol.11, No.2, May 2022. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Data Analysis of Short - term and Long - term Online Activities in LMS
Carmen Carrión
© 2022 Carmen Carrión, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 11, Issue 2, Pages 497-505, ISSN 2217-8309, DOI: 10.18421/TEM112-01, May 2022.
Received: 27 January 2022. Revised: 15 March 2022.
Abstract:
Online teaching activities based on increasingly used computer-based educational systems lacks standard rules for its implementation. This paper describes the design of online training activities using Moodle as a Learning Management System (LMS) and, evaluate short-term and long-term students’ learning outcomes applying data mining techniques. Clustering and classification algorithms are combined to uncover valuable, non-obvious students’ patterns from a well-defined collection of data. Data results from online quiz-based activities in a subject of Computer Science show that students who are not engaged in the training activity during the short-term learning process fail. Data analysis also shows that the number of trials is a key attribute. Hence, it is important to develop user-friendly online activities with real-time feedback based on student behaviour. Moreover, according to our experiment, online training activities decrease in efficiency over time..
Keywords –higher education, Learning Management System, Data Mining, students’ behaviour, students’ outcomes. |
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