Analyzing Student Achievement Through Data-Driven Educational Management: An Empirical Study of Learning Behaviors and Academic Performance
DOI:
https://doi.org/10.65327/es.v12i2.2536Keywords:
Student Achievement, Learning Behaviors, Academic Performance, Educational Data Analysis, Learning AnalyticsAbstract
The variables that affect the academic performance of students are based on an empirical study of the learning behaviors and performance outcomes. The study seeks to establish the correlation between the major variables, including study time, absenteeism, tutoring, extracurricular participation, and parental support, and academic achievement based on Grade Point Average (GPA) using a secondary dataset (consisting of about 2,392 students). The quantitative cross-sectional design was used, which included the use of descriptive statistics, correlation analysis, and multiple regression to determine the most important predictors of student achievement. The results indicate that academic performance depends positively and statistically significantly on study time and tutoring, but there is a strong negative influence of absenteeism. The contextual and behavioral factors are important in this regard as well, since parental support and involvement in extracurricular activities also play a positive role. The findings highlight that regular participation in learning processes and favorable conditions are important in enhancing student achievement. The present study adds to the literature of the educational research field because it incorporates the variables of behavior, demographics, and support in a single analytic model. The results are informative to teachers and policymakers who want to improve student achievement by using data-driven approaches. It is suggested that longitudinal trends and causation be investigated in future studies.
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