Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data

Kiran Fahd; Shah J. Miah

doi:10.30935/cedtech/17453

Kiran Fahd ¹, Shah J. Miah ¹ ^*

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CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep606

https://doi.org/10.30935/cedtech/17453

Submitted: 27 February 2025, Published Online: 26 November 2025

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Abstract

The increasing reliance on the learning management system (LMS) in this era of digital education offers a vital source of student data that can be leveraged to predict student academic progress. Predicting student academic progress in higher education (HE) supports timely intervention and enhances student retention. This study develops and compares multiple machine learning (ML) and deep learning (DL) models to identify at-risk students based on students’ interaction data with LMS by leveraging an integrated DSR methodology. Multiple predictive models are developed by incorporating data augmentation and balancing techniques to address class imbalance and enhance the accuracy of the predictive model. The study compares ten different models to achieve the highest classification accuracy in predicting students at risk of failing through the integration of through the integration of both ML and DL algorithms, including random forest, decision tree, convolutional neural networks, multi-layer perceptron, and long short-term memory (LSTM). The comparison results unscored the value of the DL based predictive model in the HE setting to precisely predict student academic performance, particularly the LTSM based model, which has the highest and nearly perfect accuracy. The existing LMS systems can incorporate this DL based predictive model to provide educational stakeholders with benefits and insights that support students’ academic journeys and institutional success.

Keywords: machine learning, deep learning, design research, higher education, LMS data, student academic progress

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Citation

Fahd, K., & Miah, S. J. (2025). Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data. Contemporary Educational Technology, 17(4), ep606. https://doi.org/10.30935/cedtech/17453

Fahd, K., and Miah, S. J. (2025). Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data. Contemporary Educational Technology, 17(4), ep606. https://doi.org/10.30935/cedtech/17453

Fahd K, Miah SJ. Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data. CONT ED TECHNOLOGY. 2025;17(4), ep606. https://doi.org/10.30935/cedtech/17453

Fahd, Kiran, and Shah J. Miah. "Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data". Contemporary Educational Technology 2025 17 no. 4 (2025): ep606. https://doi.org/10.30935/cedtech/17453

Fahd, Kiran et al. "Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data". Contemporary Educational Technology, vol. 17, no. 4, 2025, ep606. https://doi.org/10.30935/cedtech/17453

Fahd K, Miah SJ. Enhanced predictive performance: A comparative analysis of ML and DL models using on augmented LMS interaction data. CONT ED TECHNOLOGY. 2025;17(4):ep606. https://doi.org/10.30935/cedtech/17453