Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

DOI

10.2991/978-2-38476-196-8_18How to use a DOI?

Keywords

Imbalanced Data; Deep Learning; Predictive Modelling; Student Performance

Abstract

Accurate prediction of student performance is crucial in learning analytics to prevent course failures and improve academic outcomes. However, publicly accessible educational data often contains noise and imbalanced data distributions, requiring effective handling techniques. In this study, we propose a novel approach that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Long Short-Term Memory (LSTM) and Feed-Forward Neural Network (FFNN) models for performance prediction in virtual learning environments (VLEs). Our experimental results show that utilizing the SMOTE technique significantly improves the accuracy of predicting student withdrawals, with the LSTM model achieving the highest accuracy of 94.90% in the 25th week of data testing. These findings indicate the effectiveness of the SMOTE technique in addressing data imbalance issues in VLE datasets and the potential of our pro- posed deep learning models in accurately predicting student performance. The implications of our study are significant for learning analytics and educational institutions, as accurate prediction of student performance can inform early interventions and personalized support. Future research could explore the generalizability of our approach in diverse educational contexts and the integration of additional features for further improving prediction accuracy. Hence, our study con- tributes to the field of learning analytics by proposing a novel approach that com- bines SMOTE with deep learning models for student performance prediction in VLEs. Our findings highlight the potential of our approach in addressing data imbalance challenges and accurately predicting student performance, with implications for enhancing student success in educational settings.

Copyright

© 2023 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  - Edi Ismanto
AU  - Hadhrami Ab Ghani
AU  - Nor Hidayati Binti Abdul Aziz
AU  - Nurul Izrin Md Saleh
AU  - Noverta Effendy
PY  - 2024
DA  - 2024/01/25
TI  - Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach
BT  - Proceedings of the 4th International Conference on Communication, Language, Education and Social Sciences (CLESS 2023)
PB  - Atlantis Press
SP  - 192
EP  - 202
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-196-8_18
DO  - 10.2991/978-2-38476-196-8_18
ID  - Ismanto2024
ER  -