Prediction of students' failure using VLE and demographic data: case study on Open University data Online publication date: Fri, 11-Feb-2022
by Rahila Umer; Sohrab Khan; Jun Ren; Shumaila Umer; Ayesha Shaukat
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 20, No. 2, 2022
Abstract: Use of technology such as learning management system (LMS) in higher education institutes is getting very common. LMS provides support to teaching staff for communication, delivery of resources and in design of learning activities. A large amount of data is produced using these technologies which can be analysed using machine learning methods to extract knowledge regarding students' behaviour and learning processes. In this study we focus on the Open University's project for predicting a student's failure in the course by using their data. In this study multiple machine learning algorithms are applied on historical virtual learning environment (VLE) data and demographic data. This study confirms the importance of VLE and demographic data in the prediction of academic performance. This study highlights the importance of demographic data, which improves the accuracies of models for predicting a student's outcome in courses in which they are enrolled.
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