文章基本信息

标题：Prediction of student academic performance using Moodle data from a Further Education setting.
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作者：Rory Joseph Quinn ; Geraldine Gray
期刊名称：Irish Journal of Technology Enhanced Learning
电子版ISSN：2009-972X
出版年度：2020
卷号：5
期号：1
页码：1-19
DOI：10.22554/ijtel.v5i1.57
出版社：Irish Learning Technology Association
摘要：Increasingly, educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. In contrast, classifiers trained on the first ten weeks of data improved statistically significantly on a noinformation rate (p<0.008) though slightly more than half of failing students were still misclassified. The ability to detect early in the course even a minority of students at risk of failure is likely to be of use to course administrators given the economic cost involved. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of an early-warning system at ten weeks.
其他摘要：Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A