A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

TL;DR

This paper explores deep learning models, specifically CNN and RNN-LSTM, for predicting student performance in online courses across diverse datasets, demonstrating their effectiveness over traditional machine learning methods.

Contribution

It introduces the application of CNN and RNN-LSTM models for student performance prediction in online learning, using datasets from different regions to validate their effectiveness.

Findings

Deep learning models outperform traditional ML in two datasets.

Models achieve comparable performance in the third dataset.

Deep learning techniques show promise for scalable student performance analysis.

Abstract

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.