An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

Ilie Gligorea, Muhammad Usman Yaseen, Marius Cioca, Hortensia Gorski and Romana Oancea
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Ilie Gligorea: Department of Technical Science, Faculty of Military Management, “Nicolae Bălcescu” Land Forces Academy, 550170 Sibiu, Romania
Muhammad Usman Yaseen: Computer Science Department, Comsats University, Islamabad 45550, Pakistan
Marius Cioca: Department of Industrial Engineering and Management, Faculty of Engineering, “Lucian Blaga” University of Sibiu, 550025 Sibiu, Romania
Hortensia Gorski: Department of Technical Science, Faculty of Military Management, “Nicolae Bălcescu” Land Forces Academy, 550170 Sibiu, Romania
Romana Oancea: Department of Technical Science, Faculty of Military Management, “Nicolae Bălcescu” Land Forces Academy, 550170 Sibiu, Romania

Sustainability, 2022, vol. 14, issue 14, 1-21

Abstract: Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

Keywords: training system; e-learning platform; machine learning; regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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