Predicting At-Risk Students’ Performance Based on LMS Activity using Deep Learning

Abstract

It is of great importance for Higher Education (HE) institutions to continuously work on detecting at-risk students based on their performance during their academic journey with the purpose of supporting their success and academic advancement. This is where Learning Analytics (LA) representing learners' behaviour inside the Learning Management Systems (LMS), Educational Data Mining (EDM), and Deep Learning (DL) techniques come into play as an academic sustainable pipeline, which can be used to extract meaningful predictions of the learners' future performance based on their online activity. Thus, the aim of this study is to implement a supervised learning approach which utilizes three artifcial neural networks (vRNN, LSTM, and GRU) to develop models that can classify students' final grade as Pass or Fail based on a number of LMS activity indicators; more precisely, detect failed students who are actually the ones susceptible to risk. The three models alongside a baseline MLP classifier have been trained on two datasets (ELIA 101-1, and ELIA 101-2) illustrating the LMS activity and final assessment grade of 3529 students who enrolled in an English Foundation-Year course (ELIA 101) taught at King Abdulaziz University (KAU) during the first and second semesters of 2021. Results indicate that though all of the three DL models performed better than the MLP baseline, the GRU model achieved the highest classification accuracy on both datasets: 93.65% and 98.90%, respectively. As regards predicting at-risk students, all of the three DL models achieved an = 81% Recall values, with notable variation of performance depending on the dataset, the highest being the GRU on the ELIA 101-2.