International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 11, Issue 5 (May 2024), Pages: 140-150

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 Original Research Paper

A novel approach to mitigate academic underachievement in higher education: Feature selection, classifier performance, and interpretability in predicting student performance

 Author(s): 

 Safira Begum *, M. V. Ashok

 Affiliation(s):

 Department of Computer Applications, HKBKDC, Bangalore, India

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2883-9994

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.05.015

 Abstract

The main goal of this study is to address the ongoing problem of low academic performance in higher education by using machine learning techniques. We use a dataset from a higher education institution that includes various information available at student enrollment, such as academic history, demographics, and socio-economic factors. To address this issue, we introduce a new method that combines the Slime Mould Algorithm (SMA) for efficient feature selection with a Forest-Optimized Neural Network (FO-NN) Classifier. Our method aims to identify students at risk of academic failure early. Using the SMA, we simplify the feature selection process, identifying important attributes for accurate predictions. The Forest Optimization technique improves the classification process by optimizing the neural network model. The experimental results of this study show that our proposed method is effective, with significant improvements in feature selection accuracy and notable enhancements in the predictive performance of the neural network classifier. By selecting a subset of relevant features, our approach deals with high-dimensional datasets and greatly improves the quality and interpretability of predictive models. The innovative combination of the SMA and the FO-NN classifier increases accuracy, interpretability, and the ability to generalize in predicting student performance. This work contributes to a more effective strategy for reducing academic underachievement in higher education.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Academic underachievement, Machine learning, Slime mould algorithm, Feature selection, Predictive performance

 Article history

 Received 24 August 2023, Received in revised form 10 February 2024, Accepted 1 May 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Begum S and Ashok MV (2024). A novel approach to mitigate academic underachievement in higher education: Feature selection, classifier performance, and interpretability in predicting student performance. International Journal of Advanced and Applied Sciences, 11(5): 140-150

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 

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