Classification of Student Achievement Using Data Mining Techniques: A Comparative Study
DOI:
https://doi.org/10.5281/zenodo.18234488Keywords:
Data mining, Student performance, Classification, Machine learning, Neural networksAbstract
This study investigates the application of data mining techniques to classify secondary school students’ academic performance. The Student Performance Dataset, obtained from the UCI Machine Learning Repository, was used for analysis. After excluding two of the exam results, the dataset comprised 31 attributes for 395 students. The classification was based on final exam grades: scores between 0–10 were labeled as "unsuccessful" (0) and scores between 11 and 20 as "successful" (1). The dataset was preprocessed to correct CSV format errors, making it suitable for analysis in the WEKA software. Four classification algorithms—Iterative Classifier Optimizer, OneR, LogitBoost, and Artificial Neural Networks—were evaluated using 5, 7, and 10-fold cross-validation. Results showed that OneR achieved the highest average accuracy (92.15%) and sensitivity (96%), while LogitBoost yielded the best specificity (88%). The findings suggest that OneR is the most effective method for classifying student success using this dataset.
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