GRADETRACK: A PREDICTIVE ANALYTICS TOOL FOR FORECASTING GRADUATION LIKELIHOOD AMONG CITE STUDENTS
Keywords:
graduation likelihood, predictive analytics, logistic regression, educational data mining, student retention, SDG 4, SDG 9Abstract
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).
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References
Alipio, M. (2020). Academic Adjustment and Performance among Filipino Freshmen College Students in the Health Sciences: Does Senior High School Strand Matter? Education and Management. https://doi.org/10.35542/osf.io/xq4pk
Almonteros, J. R., Matias, J. B., & Victoria, J. (2024). Forecasting Students’ Success To Graduate Using Predictive Analytics. International Journal of Computing and Digital System/International Journal of Computing and Digital Systems, 15(1), 697–711. https://doi.org/10.12785/ijcds/150151
B. Manugas, S., T. Pepito, M., S. Fernandez, J. J., & A.Canque, M. (2022). Senior High School Tract as Determinant for College GPA: A Correlational Study. International Journal of Science and Management Studies (IJSMS), 5(3), 230–234. https://doi.org/10.51386/25815946/ijsms-v5i3p126
Bahr, P. R., Boeck, C. A., Chen, Y., & Clasing-Manquian, P. (2024). Less is more, or is it? Age and Gender Differences in How Students Build Momentum toward College Graduation. Research in Higher Education, 65(8), 1771–1803. https://doi.org/10.1007/s11162-024-09806-6
Bahr, P. R., Columbus, R., & Chen, Y. (2021). Are There Age Disparities in Community College Completion? Evidence from Ohio’s Community Colleges. Community College Journal of Research and Practice, 46(10), 755–762. https://doi.org/10.1080/10668926.2021.1976308
Bernardo, A., Cordel, M., Lapinid, M. R., Teves, J. M., Yap, S., & Chua, U. (2022). Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning. Journal of Intelligence, 10(3), 61. https://doi.org/10.3390/jintelligence10030061
Bidya Raj Subedi, & Powell, R. (2016). Factors Influencing College Readiness: A Multilevel Study to Measure School Effects. 15(11), 71–86. https://www.researchgate.net/publication/311426439_Factors_Influencing_College_Readiness_A_Multilevel_Study_to_Measure_School_Effects
Bliven, A., & Jungbauer, M. (2021). The Impact of Student Recognition of Excellence to Student Outcome in a Competency‐based Educational Model. The Journal of Competency-Based Education, 6(4). https://doi.org/10.1002/cbe2.1264
Canque, M., Derasin, L. M., & Pinatil, L. (2021). Senior High School Background and GPA of the Education Students in a State University in the Philippines. Turkish Journal of Computer and Mathematics Education, 12(13), 3560–3566. https://doi.org/https://doi.org/10.17762/turcomat.v12i13.9185
Carballo-Mendívil, B., Arellano-González, A., Ríos-Vázquez, N. J., & Pilar. (2025). Predicting Student Dropout from Day One: XGBoost-Based Early Warning System Using Pre- Enrollment Data. Applied Sciences, 15(16), 9202–9202. https://doi.org/10.3390/app15169202
Celine, S., Dominic, M., & Devi, S. (2020). Logistic Regression for Employability Prediction. International Journal of Innovative Technology and Exploring Engineering, 9(3), 2471–2478. https://doi.org/10.35940/ijitee.c8170.019320
Chen, Z., Cen, G., Wei, Y., & Li, Z. (2023). Student Performance Prediction Approach Based on Educational Data Mining. IEEE Access, 11, 131260–131272. https://doi.org/10.1109/access.2023.3335985
Cheryan, S., Ziegler, S., Montoya, A. K., & Jiang, L. (2017). Why Are Some STEM Fields More Gender Balanced than others? Psychological Bulletin, 143(1), 1–35. https://doi.org/https://doi.org/10.1037/bul0000052
Dita, B., & Velasco, M. J. (2025). The Relationship of Senior High School Strands and Academic Performance in College among Computer Engineering Students: Basis for Policy Guideline Development. 10(1). https://doi.org/10.5281/zenodo.14631683
Forghani, R., Savadjiev, P., Chatterjee, A., Muthukrishnan, N., Reinhold, C., & Forghani, B.(2019). Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Computational and Structural Biotechnology Journal, 17, 995–1008. https://doi.org/10.1016/j.csbj.2019.07.001
Galla, B., Shulman, E., Plummer, B., Gardner, M., Hutt, S., Goyer, J. P., D’Mello, S., Finn, A., &Duckworth, A. (2019). Why High School Grades Are Better Predictors of On-TimeCollege Graduation Than Are Admissions Test Scores: The Roles of Self-Regulation andCognitive Ability. American Educational Research Journal, 56(6), 2077–2115. https://doi.org/10.3102/0002831219843292
Gifford, M., & Bayrak, T. (2023). A predictive analytics model for forecasting outcomes in the National Football League games using decision tree and logistic regression. Decision Analytics Journal, 8(100296). https://doi.org/10.1016/j.dajour.2023.100296
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R Second Edition. https://www.casact.org/sites/default/files/2022-12/James-G.-et-al.-2nd-edition-Springer-2021.pdf
Kalita, E., Oyelere, S. S., Gaftandzhieva, S., Rajesh, K. N. V. P. S., Jagatheesaperumal, S. K.,Mohamed, A., Elbarawy, Y. M., Desuky, A. S., Hussain, S., Cifci, M. A., Theodorou, P.,Hilčenko, S., Hazarika, J., & Ali, T. (2025). Educational data mining: a 10-year review. Discover Computing, 28(1). https://doi.org/10.1007/s10791-025-09589-z
Kamer, J., & Ishitani, T. (2019). First-Year, Nontraditional Student Retention at Four-Year Institutions: How Predictors of Attrition Vary Across Time. Journal of College Student Retention: Research, Theory & Practice, 23(3), 152102511985873. https://doi.org/10.1177/1521025119858732
Kim, B.-H., Vizitei, E., & Ganapathi, V. (2018). GritNet: Student Performance Prediction with Deep Learning. In arxiv.org. https://arxiv.org/abs/1804.07405
Lee, J., Weis, L., Liu, K., & Kang, C. (2017). Which Type of High School Maximizes Students’ College Match? Unequal Pathways to Postsecondary Destinations for Students From Varying High School Settings. The Journal of Higher Education, 88(4), 529–560.https://doi.org/10.1080/00221546.2016.1272327
Lorenzo-Quiles, O., Galdón-López, S., & Lendínez-Turón, A. (2023). Dropout at university. Variables involved on it. Frontiers in Education, 8(1159864). https://doi.org/10.3389/feduc.2023.1159864
Luciano, R. G., Olipas, C. N. P., & Dela Cruz, A. P. (2022). Factors Affecting the Dropout Rate among Information Technology Students: A Basis for an Intervention Plan. In ERIC (Vol. 8, pp. 2013–2017). International Journal Of Advance Research And Innovative Ideas In Education. https://eric.ed.gov/?id=ED622623
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers &Amp; Education, 103, 1–15. https://doi.org/10.1016/j.compedu.2016.09.005
Marini, J., Westrick, P., Young, L., Ng, H., Shmueli, D., & Shaw, E. (2019). Differential Validity and Prediction of the SAT ® Examining First-Year Grades and Retention to the Second Year. https://files.eric.ed.gov/fulltext/ED597325.pdf
Master, A., Cheryan, S., & Meltzoff, A. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437. https://doi.org/10.1037/edu0000061
McNeese, B. (2025, February 26). Forward Selection Regression and Backward Elimination Regression. SPC for Excel. https://www.spcforexcel.com/knowledge/root-cause-analysis/forward-selection-regression-and-backward-elimination-regression/
Namoun, A., & Alshanqiti, A. (2020). Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237
Núñez-Naranjo, A. F. (2024). Analysis of the determinant factors in university dropout: a case study of Ecuador. Frontiers in Education, 9(1444534). https://doi.org/10.3389/feduc.2024.1444534
Okoye, K., Nganji, J. T., Escamilla, J., & Hosseini, S. (2024). Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education. Computers and Education: Artificial Intelligence, 6(2666–920X), 100205. https://doi.org/10.1016/j.caeai.2024.100205
Ong, D., & Pedrasa, J. R. (2021). Student Risk Assessment: Predicting Undergraduate StudentGraduation Probability Using Logistic Regression, SVM, and ANN. TENCON 2021 -2021 IEEE Region 10 Conference (TENCON), 105–110. https://doi.org/10.1109/tencon54134.2021.9707322
Patel, H. J., & Saini, J. R. (2021). A Patient Care Predictive Model using Logistic Regression. International Journal of Advanced Computer Science and Applications, 12(12). https://doi.org/10.14569/ijacsa.2021.0121278
Rajendra, P., & Latifi, S. (2021). Prediction of diabetes using logistic regression and ensemble techniques. Computer Methods and Programs in Biomedicine Update, 1(2666–9900), 100032. https://doi.org/10.1016/j.cmpbup.2021.100032
Regina Student Publication. (2023, May 25). Notre Dame of Midsayap College, INC | NDMC | Making A Positive Difference. Notre Dame of Midsayap College, Inc. https://www.ndmc.edu.ph/content/metamorphosis-ndmc-celebrates-62nd-commencement-exercises-2023-0
Shaker Abdalrada, A., Hashim Yahya, O., Hadi M. Alaidi, A., Ali Hussein, N., TH. Alrikabi, H.,& Al-Quraishi, T. A.-Q. (2019). A Predictive model for liver disease progression based on logistic regression algorithm. Periodicals of Engineering and Natural Sciences (PEN), 7(3), 1255. https://doi.org/10.21533/pen.v7i3.667
T. Vecaldo, R., I. Tamayao, A., T. Mamba, M., Emmanuel L. Asuncion, J., Marl G. Paat, F., & S. Pagulayan, E. (2020). Academic Profile and College Preparedness of K-12 Graduates: The Case of the Indigenous Peoples (IPs) in the Northern Philippines. Journal of Education and E-Learning Research, 7(4), 437–445. https://doi.org/10.20448/journal.509.2020.74.437.445
Thomas, N. S., Barr, P. B., Hottell, D. L., Adkins, A. E., & Dick, D. M. (2021). Longitudinal Influence of Behavioral Health, Emotional Health, and Student Involvement on College Student Retention. Journal of College Student Development, 62(1), 2–18. https://doi.org/10.1353/csd.2021.0001
Tight, M. (2019). Student retention and engagement in higher education. Journal of Further and Higher Education, 44(5), 1–16. https://doi.org/10.1080/0309877x.2019.1576860
Toquero, C. M. (2020). Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context. Pedagogical Research, 5(4), em0063. https://doi.org/10.29333/pr/7947
Vieira do Nascimento, D., Roser-Chinchilla, J., & Mutize, T. (2020). Towards universal access to higher education: international trends. In Unesco.org (p. 86). United Nations Educational and NESCO International Institute for Higher Education in Latin America and the Caribbean (IESALC) . https://unesdoc.unesco.org/ark:/48223/pf0000375686
Winograd, G. (2024, April 3). College Dropout Rates 2024 | Data & Statistics. Mission Graduate. https://missiongraduatenm.org/college-dropout-statistics/
Yağcı, M. (2022). Educational data mining: prediction of students” academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
