OULAD MOOC Student Performance Prediction using Machine and Deep Learning Techniques

DOI

10.2991/978-94-6463-654-3_18How to use a DOI?

Keywords

Performance prediction; Learning Analytics; Machine Learning; Deep Learning; OULAD dataset

Abstract

In online learning, the accurate prediction of student performance is essential for timely interventions and personalized learning experiences. This work leverages the Open University Learning Analytics Dataset (OULAD) to evaluate the effectiveness of various machine learning (ML) and deep learning (DL) techniques in predicting student performance. We implemented a range of models, including traditional ML algorithms like Logistic Regression (LR), Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVMs), as well as DL models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. The performance of each model was assessed using various metrics such as accuracy, precision, recall, and F1-score. The experimental results revealed that, among DL models, LSTM prevailed in terms of accuracy and precision, which are 83.41% and 82.20%, respectively. Additionally, the RF and optimized DT models performed well and provided a strong balance between accuracy and recall, making them a solid choice when computational efficiency is a concern.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wala Torkhani
AU  - Kalthoum Rezgui
PY  - 2025
DA  - 2025/02/24
TI  - OULAD MOOC Student Performance Prediction using Machine and Deep Learning Techniques
BT  - Proceedings of the  International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
PB  - Atlantis Press
SP  - 228
EP  - 241
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-654-3_18
DO  - 10.2991/978-94-6463-654-3_18
ID  - Torkhani2025
ER  -