Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Abstract

Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize the strengths and weaknesses of their academic programs, and thereby take the necessary corrective interventions to ameliorate student achievements. To this end, our paper contributes, firstly, a hybrid regression model that optimizes the prediction accuracy of student academic performance, measured as future grades in different courses, and, secondly, an optimized multi-label classifier that predicts the qualitative values for the influence of various factors associated with the obtained student performance. The prediction of student performance is produced by combining three dynamically weighted techniques, namely collaborative filtering, fuzzy set rules, and Lasso linear regression. However, the multi-label prediction of the influential factors is generated using an optimized self-organizing map. We empirically investigate and demonstrate the effectiveness of our entire approach on seven publicly available and varying datasets. The experimental results show considerable improvements compared to single baseline models (e.g. linear regression, matrix factorization), demonstrating the practicality of the proposed approach in pinpointing multiple factors impacting student performance. As future works, this research emphasizes the need to predict the student attainment of learning outcomes.

Educational data mining: prediction of students' academic performance using machine learning algorithms

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Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

“…Instead, it should be studied within a broader context, particularly using the student outcomes, which are now guiding the learning process by looking at the cohort performance. Moreover, recent research recommends exploring the prospect of predicting the attainment of student outcomes to infer student performance [29].…”

“…Learning analytics and educational data mining (i.e., EDM) are proclaimed to improve the attainment of student learning outcomes [108]. There are also several calls for automating the assessment of student outcomes, which represent a proxy for student performance and success [9,29]. However, it is unclear how student outcomes are modeled and predicted at the course and program level using data mining and machine learning models.…”

The Impact of Work-Integrated Learning and Learning Strategies on Engineering Students’ Learning Outcomes in Thailand: A Multiple Mediation Model of Learning Experiences and Psychological Factors

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