Comparative Analysis of Naive Bayes and K-NN Approaches to Predict Timely Graduation using Academic History

Abstract

Graduation is a pivotal moment in higher education, significantly impacting institutional accreditation and public perception. This study aims to ensure that all students graduate punctually, recognizing the critical role of higher education in achieving this goal. Central to this effort are comprehensive datasets that capture academic performance throughout both undergraduate and graduate studies. These datasets include details such as university and program specifics, undergraduate and master’s GPAs, TOEFL scores, and the duration of study. By leveraging classification techniques within data mining, particularly K-NN and Naive Bayes, a comparative analysis was conducted to precisely predict the on-time completion of graduate students. The process of predicting graduation involves several stages, including data preprocessing, transformation, and the segmentation of data into training and testing sets. Subsequently, the selected methods are applied, and analyses are undertaken to accurately forecast graduation outcomes. Experimental findings reveal an 80% accuracy rate for Naive Bayes and 73% for K-NN. Notably, Naive Bayes demonstrates superior efficacy in predicting on-time graduation. However, to further refine accuracy, it is necessary to expand datasets and diversify the variables used in the analysis, such as incorporating additional academic and non-academic factors that may influence graduation timelines. The insights derived from this research hold significant implications for academic institutions, offering valuable guidance for implementing proactive measures to support students in completing their studies within the expected timeframe. By utilizing the findings of this study, educational institutions can develop tailored strategies and interventions to address potential barriers to timely graduation, such as enhancing academic advising, providing targeted support services, and optimizing course scheduling. These efforts will ultimately foster student success, improve institutional outcomes, and contribute to the overall excellence of higher education institutions.