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@article{GradeTrack: A Predictive Analytics Tool for Forecasting Graduation Likelihood Among CITE Students: GradeTrack: A Predictive Analytics Tool for Forecasting Graduation Likelihood Among CITE Students_2026, volume={4}, url={https://semj.ndmc.edu.ph/index.php/ndmc-journal/article/view/35}, DOI={10.64887/70wv2869}, abstractNote={
This study examined the relationship between students’ pre-admission academic profiles and their likelihood of graduation in the College of Information Technology and Engineering Department (CITE), regardless of program specialization. The analysis included several pre-admission variables, namely age, sex, Senior High School (SHS) Grade Point Average (GPA), SHS strand, academic awards, and type of high school attended. Logistic regression was used to determine which factors significantly predicted graduation outcomes. The results showed that sex, SHS strand, and SHS GPA were significant predictors of graduation likelihood. Female students had higher odds of completing their degree than male students. Students from the STEM and TVL-ICT strands were nearly three times more likely to graduate than those from non-aligned strands. Likewise, each one-point increase in SHS GPA was associated with an approximate fifteen percent increase in the probability of graduation. However, age, academic awards, and type of high school attended were not statistically significant predictors. Guided by these results, GradeTrack was developed to forecast students’ graduation likelihood using pre-admission academic data. The tool assists institutional decision-making by allowing early identification of students who may require academic intervention. By supporting evidence-based retention strategies and applying predictive analytics to institutional planning, this study aligns with the United Nations Sustainable Development Goals 4 (Quality Education) and 9 (Industry, Innovation, and Infrastructure).
}, number={1}, journal={San Eugenio: A Multidisciplinary Journal}, year={2026}, month={Aug.} }