Dropout Prediction in a Web Environment Based on Universal Design for Learning

Abstract

Dropout prediction is an essential task in educational Web platforms to identify at-risk learners, enable individualized support, and eventually prevent students from quitting a course. Most existing studies on dropout prediction focus on improving machine learning methods based on a limited set of features to model students. In this paper, we contribute to the field by evaluating and optimizing dropout prediction using features based on personal information and interaction data. Multiple granularities of interaction and additional unique features, such as data on reading ability and learners’ cognitive abilities, are tested. Using the Universal Design for Learning (UDL), our Web-based learning platform called I3Learn aims at advancing inclusive science learning by focusing on the support of all learners. A total of 580 learners from different school types have used the learning platform. We predict dropout at different points in the learning process and compare how well various types of features perform. The effectiveness of predictions benefits from the higher granularity of interaction data that describe intermediate steps in learning activities. The cold start problem can be addressed using assessment data, such as a cognitive abilities assessment from the pre-test of the learning platform. We discuss the experimental results and conclude that the suggested feature sets may be able to reduce dropout in remote learning (e.g., during a pandemic) or blended learning settings in school.