Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation

Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation

IntroductionEducational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predict...

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Bibliographic Details
Main Authors: Emi Kalita, Abdullah Mana Alfarwan, Houssam El Aouifi, Ashima Kukkar, Sadiq Hussain, Tazid Ali, Silvia Gaftandzhieva
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Education
Subjects:
student academic outcome
XAI
SHAP
Bi-LSTM
student dropout
statistical test
Online Access:https://www.frontiersin.org/articles/10.3389/feduc.2025.1581247/full
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https://www.frontiersin.org/articles/10.3389/feduc.2025.1581247/full

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