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Case Study: Predicting Academic Success in Business English through Learning Behavior and Psychological Support Analysis with Machine Learning

by Charlotte Green 1,*
1
Anglia Ruskin University, Faculty of Science and Engineering, United Kingdom
*
Author to whom correspondence should be addressed.
JGSR  2023 5(2):89; https://doi.org/10.xxxx/xxxxxx
Received: 17 October 2023 / Accepted: 23 November 2023 / Published Online: 31 December 2023
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Abstract

This report takes learning behavior and psychological support as the factors to predict the students' academic performance. A total of 13 factors were selected at the initial stage. Firstly, PCA and MDT algorithms were used for data preprocessing, and we used CNN to extract deep student behavior features. Then, the maximum pooling approach is used to pick the significant features; next, the selected features are used as the input of LSTM. A temporal attention mechanism is introduced at the output of LSTM to allocate attention weights for different weekly student behavior characteristics. This paper divides students' academic performance into several score intervals and makes a multi-classification prediction. This study uses 39 algorithms based on six types to predict the student's academic performance. The result shows that: (1) The average prediction accuracy based on the deep learning model is the highest (85.46%) in the six types of models; (2) The accuracy of the LSTM algorithm (88.36%) outperformed all the benchmark models. (3) The attention mechanism introduced can effectively improve the model's prediction accuracy; the proposed model in this paper outperformed all the benchmark models. Furthermore, the statistical analysis found that (1) Students who have academic confidence (confidence in teachers, courses, and themselves) may obtain higher academic performance; (2) A clear learning motivation is essential to final academic performance; (3) Lack of confidence in others in the study group may ultimately improve students' academic performance by encouraging them to work harder.

Keywords: Learning Behavior; Academic Performance; Psychological support; Academic Confidence; Convolutional neural networks (CNN); Attentional mechanisms; Long-and short-term memory network (LSTM); Mutiway Delay Embedding (MDT);
Copyright: © 2023 by Green. 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
Green, C. Case Study: Predicting Academic Success in Business English through Learning Behavior and Psychological Support Analysis with Machine Learning. Journal of Globe Scientific Reports, 2023, 5, 89. doi:10.xxxx/xxxxxx
AMA Style
Green C. Case Study: Predicting Academic Success in Business English through Learning Behavior and Psychological Support Analysis with Machine Learning. Journal of Globe Scientific Reports; 2023, 5(2):89. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Green, Charlotte 2023. "Case Study: Predicting Academic Success in Business English through Learning Behavior and Psychological Support Analysis with Machine Learning" Journal of Globe Scientific Reports 5, no.2:89. doi:10.xxxx/xxxxxx

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