Applying Data Mining Techniques to Develop a Model for Predicting Suitable Academic Fields for University Applicants at Kamphaeng Phet Rajabhat University
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Abstract
This research aimed to develop predictive models for determining the suitability of academic programs for undergraduate students by applying five data mining techniques: Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), Random Forest, and AdaBoost. The study utilized a dataset of 1,392 students across 10 academic programs at Kamphaeng Phet Rajabhat University. Students’ academic performance was grouped into four levels include Excellent (A), Good (B), Fair (C), and Low (E). The experimental results indicated that Naive Bayes achieved the highest average F1-Score in 6 out of 10 programs, particularly in programs with simple data structures and clearly separable classes. In contrast, SVM performed well in programs with complex or overlapping data structures, while Random Forest demonstrated outstanding performance in handling high-variance data, especially in the General Management program, where it achieved the highest F1-Score of 0.75. The findings suggest that selecting an appropriate model should consider the underlying structure of the dataset in each specific context. Although Naive Bayes yielded the best overall results, data overlapping between classes in several programs remained a limiting factor, resulting in moderate classification accuracy. Future research should consider incorporating behavioral, interest-based, or skill-related features to enhance prediction accuracy and support educational guidance systems that better align with each student’s potential.
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References
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