RETHINKING EXAMINATION-ORIENTED LEARNING: A STUDY OF ASSESSMENT PRACTICES IN EDUCATION
DOI:
https://doi.org/10.65327/es.v12i1.2529Keywords:
Student performance, academic achievement, educational data mining, predictive modellingAbstract
Student academic performance is influenced by demographic, social, and school-related factors, and understanding these relationships can support early intervention and evidence-based educational planning. This study uses the Student Performance Data Set to examine patterns associated with secondary school achievement. The objective is to identify key variables related to student grades and demonstrate how educational data can be used to predict and interpret academic outcomes. A quantitative, secondary-data approach was applied using student records containing demographic characteristics, family background, study habits, school support, alcohol consumption, absences, and prior grades. Descriptive analysis summarized student characteristics, while predictive modelling and comparative analysis explored relationships between independent variables and final academic performance. Prior academic achievement emerged as the strongest indicator of final performance, suggesting that earlier grades provide substantial information about later outcomes. Study time, attendance, family support, and school-related engagement were also associated with achievement differences. Higher absence levels and risk-related behaviours, such as frequent alcohol consumption, were generally linked with weaker academic outcomes. The findings indicate that student performance is shaped by multiple interacting academic and social factors rather than by a single cause. The dataset provides useful evidence for identifying students who may need academic or social support. Early monitoring of grades, attendance, and learning behaviours can help educators design targeted interventions to improve student success.
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