Predicting Student Outcomes in Open High Schools Using Educational Data Mining
DOI:
https://doi.org/10.19173/irrodl.v27i3.8858Keywords:
educational data mining, dropout prediction, open high school, machine learning, distance education, student retention, early warning systemAbstract
Open high schools fulfill a critical function by offering flexible educational pathways for students encountering diverse socioeconomic and personal challenges. Nevertheless, escalating dropout rates represent a significant concern, necessitating robust predictive models to facilitate early interventions. This study employed educational data mining (EDM) techniques to analyze the academic trajectories of 484,158 students enrolled in Turkish open high schools. We evaluated multiple classification algorithms—J48, decision tree, k-nearest neighbors (kNN), naïve Bayes, and random forest—across four distinct data preprocessing scenarios to predict student status (dropout/delayed graduation/graduation). The J48 algorithm demonstrated superior performance, achieving an accuracy of 80.47% and a kappa statistic of 0.61. Key findings reveal that academic and administrative features, notably total credit accumulation and initial enrollment type, are more important predictors than demographic variables. This research provides empirically grounded insights for the early identification of at-risk students. It offers data-driven recommendations to enhance student retention policies within open high school systems, contributing a large-scale, multi-class prediction analysis within a unique, under-researched national distance education context.
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