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Volume 5, 2023 - Issue 2

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Enhancing Student Performance Prediction through CNN, LSTM, and Attention Mechanisms

by Nurul Amin
1
Shahjalal University of Science and Technology, School of Agriculture and Mineral Sciences, Bangladesh
*
Author to whom correspondence should be addressed.
JGSR  2023 5(2):85; https://doi.org/10.xxxx/xxxxxx
Received: 1 March 2023 / Accepted: 1 November 2023 / Published Online: 10 December 2023
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Abstract

This paper focuses on the study of student behavior-based grade prediction. This research investigates grade prediction based on student behavior. After evaluating student behavior data from campus events, an attention-based CNN-LSTM student grade prediction model is developed. Initially, we use Convolutional Neural Network (CNN) to extract deep student behavior features and maximum pooling approach to pick the significant features in student behavior features; the retrieved features are then used as the input of Long and Short-Term Memory networks. A temporal attention mechanism is included at the output of LSTM in order to allocate attention weights for different weekly student behavior characteristics in a reasonable manner, hence improving the model's performance in terms of prediction. Experiments revealed that the student performance prediction model suggested in this paper had excellent performance.

Keywords: Convolutional neural networks; Attentional mechanisms; Long-and short-term memory networks; Grade prediction;
Copyright: © 2023 by Amin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Amin, N. Enhancing Student Performance Prediction through CNN, LSTM, and Attention Mechanisms. Journal of Globe Scientific Reports, 2023, 5, 85. doi:10.xxxx/xxxxxx
AMA Style
Amin N. Enhancing Student Performance Prediction through CNN, LSTM, and Attention Mechanisms. Journal of Globe Scientific Reports; 2023, 5(2):85. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Amin, Nurul 2023. "Enhancing Student Performance Prediction through CNN, LSTM, and Attention Mechanisms" Journal of Globe Scientific Reports 5, no.2:85. doi:10.xxxx/xxxxxx

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