Understanding and assembling user behaviours using features of Moodle data for eLearning usage from performance of course-student weblog

In reality, students learn via eLearning (electronic online learning) system in different ways depending on their learning needs, learning behaviours as well as eLearning system policy for users. However, most learning outcome prediction models of eLearning systems are still not stable and still can...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-04, Vol.1869 (1), p.12087
Main Authors: Chayanukro, S, Mahmuddin, M, Husni, H
Format: Article
Language:English
Subjects:
Distance learning
Machine learning
Online instruction
Physics
Prediction models
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Summary:In reality, students learn via eLearning (electronic online learning) system in different ways depending on their learning needs, learning behaviours as well as eLearning system policy for users. However, most learning outcome prediction models of eLearning systems are still not stable and still cannot be applied in many situations as the use of eLearning is considered to be highly dynamic. Therefore, the objective of this work is understand if eLearning system can be predicted based eLearning usage by exploiting Moodle log data. To understand it, features from web log course-student in Moodle is being considered, a number of machine learning techniques also have been applied for benchmarking in this study. The result found that the current group doesn’t give better understanding and significant groups of factors that could be able to predict the learning outcome.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1869/1/012087