The Convergence of Deep Learning and ICT: Revolutionizing Student Performance Prediction

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : L. Ashish, G. Anitha

10.14445/23488549/IJECE-V11I11P108

How to Cite?

L. Ashish, G. Anitha, "The Convergence of Deep Learning and ICT: Revolutionizing Student Performance Prediction," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 79-94, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P108

Abstract:

Education includes transferring knowledge, integral judgment, and mature wisdom in addition to teaching and practising certain skills. Empowering educators and policymakers to make creative decisions that develop better student engagement, achievement, and equity in education, which ultimately prepares students for future challenges and opportunities, is crucial. Integrating Information and Communication Technology (ICT) into education boosts the learning capabilities that impact societal development, economic prosperity, and individual growth. This study aims to predict student performance using a Deep Neural Network (DNN) optimized with a feature optimization technique for effectively predicting student outcomes on ICT-enhanced learning. The dataset includes various attributes like gender, language skill, computer knowledge, learning type, location, and subject scores. The study evaluates the effects of ICT-enabled learning and finds critical elements impacting student performance by thoroughly examining the dataset and the neural network application. The proposed DNN employs a sequential architecture with multiple hidden layers with a classification accuracy of 93.0%, precision of 93.21%, recall of 93.0%, and an F1-score of 92.91. These findings highlight the model's high effectiveness in predicting student grades. This research underscores the potential of ICT-enhanced education to improve learning outcomes. It provides a robust predictive tool for early detection of at-risk students, thereby enabling prompt and targeted interventions.

Keywords:

Academic Success, Deep Neural Network, Education, Information and Communication Technology, Learning Type.

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