Prediction of student academic performance using Moodle data from a Further Education setting
- Resource Type
- Authors
- Rory Joseph Quinn; Geraldine Gray
- Source
- Irish Journal of Technology Enhanced Learning, Vol 5, Iss 1 (2019)
- Subject
- Blended learning
Further education
Medical education
LC8-6691
Computer science
ComputingMilieux_COMPUTERSANDEDUCATION
Learning Management
Space (commercial competition)
Virtual platform
Special aspects of education
Term (time)
- Language
- ISSN
- 2009-972X
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. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p