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Peer-reviewedCC BYScientific Reports

Enhancing student performance prediction using an optimized RNN-LSTM model: a step towards personalized education

  • J. Daphney Joann1
  • M. Balasubramanian2
  • A. S. Nisha3
  • P. Kokila4
4 affiliations
  1. 1Department of Computer Science and Engineering, Global Institute of Engineering and Technology
  2. 2Department of Computer Science , Government Arts and Science College
  3. 3Department of Artificial Intelligence and Data Science, St. Joseph’s College of Engineering
  4. 4Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), PES University
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Abstract

Learning Analytics is an emerging field that strives to analyze and predict trends in student performances over time. With the increased usage of Learning Management Systems (LMS), which are equipped with learning content, interaction, and assessment aligned with educational objectives, advanced machine learning techniques such as Long Short-Term Memory (LSTM) are being used to analyze sequential data. This research aims to assess the efficiency of LSTM in student performance prediction by comparing its accuracy with other baseline models such as Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs). The proposed optimized recurrent LSTM model outperforms CNN and GRU baseline models in prediction accuracy and efficiently captures temporal dependencies in sequential student academic data. To enhance training stability and predictive performance, the model was trained using Binary Cross-Entropy loss and validation-based parameter selection. The research indicates that such a model has the ability to be used in personalized learning by adjusting learning paths dynamically to improve student performances.Experiments were conducted using a synthetic dataset designed to simulate realistic academic progress patterns, demonstrating the model’s predictive capability.

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Cite

BibTeXRIS
BibTeX
@article{joann2026enhancing,
  title = {{Enhancing student performance prediction using an optimized RNN-LSTM model: a step towards personalized education}},
  author = {J. Daphney Joann and M. Balasubramanian and A. S. Nisha and P. Kokila},
  journal = {Scientific Reports},
  year = {2026},
  month = sep,
  doi = {10.1038/s41598-026-70691-3},
  url = {https://doi.org/10.1038/s41598-026-70691-3},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Enhancing student performance prediction using an optimized RNN-LSTM model: a step towards personalized education
AU  - J. Daphney Joann
AU  - M. Balasubramanian
AU  - A. S. Nisha
AU  - P. Kokila
PY  - 2026
DA  - 2026/09/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-70691-3
UR  - https://doi.org/10.1038/s41598-026-70691-3
AB  - Learning Analytics is an emerging field that strives to analyze and predict trends in student performances over time. With the increased usage of Learning Management Systems (LMS), which are equipped with learning content, interaction, and assessment aligned with educational objectives, advanced machine learning techniques such as Long Short-Term Memory (LSTM) are being used to analyze sequential data. This research aims to assess the efficiency of LSTM in student performance prediction by comparing its accuracy with other baseline models such as Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs). The proposed optimized recurrent LSTM model outperforms CNN and GRU baseline models in prediction accuracy and efficiently captures temporal dependencies in sequential student academic data. To enhance training stability and predictive performance, the model was trained using Binary Cross-Entropy loss and validation-based parameter selection. The research indicates that such a model has the ability to be used in personalized learning by adjusting learning paths dynamically to improve student performances.Experiments were conducted using a synthetic dataset designed to simulate realistic academic progress patterns, demonstrating the model’s predictive capability.
SN  - 2045-2322
ER  -