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Examining the factors affecting students' academic performance is a significant aspect of consideration as it can improve teaching and learning processes. Numerous academic and non-academic facets affect student performance, such as study time, frequency of absences, recreational activities, and interpersonal relationships. Educational data mining (EDM) and learning analytics (LA) are two closely related fields that reveal useful information from educational databases to generate actionable insights. This paper investigates the aforementioned feature sets by hypothesizing the impact of these factors on student performance based on existing studies and employs a combination of LA and EDM techniques to test the hypotheses. Experimental results show that the presented hypotheses were consistent on nearly all accounts. The insights of this study can be used to bolster student performance even beyond an academic scope by educational policy improvement.
Kasi et al. (Tue,) studied this question.