Cluster-Based Performance of Student Dropout Prediction as a Solution for Large Scale Models in a Moodle LMS

Abstract

Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.