Ep127 - Predicting the scores of university entrance examinees

In this episode, we talk with Diether C. Montejo about his study on predicting how university entrants perform in the State University Aptitude and Scholarship Test (SUAST) — the college admission exam of Davao Oriental State University. Despite a 54% passing rate over a five-year period, non-passers continued to be accepted due to policy changes, raising questions about what factors actually determine exam performance. Using both Multiple Linear Regression and Multi-Layer Perceptron Neural Network approaches, the study found that family income, library access, senior high school grades, intrinsic motivation, and openness and intellect were significant predictors. The neural network model also slightly outperformed traditional regression in predicting exam scores.

📚Reference: Singh, N. A., & Montejo, D. C. (2023). Bridging the gap: A comparative analysis of traditional and neural network regression methods for predicting university entrant performance in SUAST examination. Davao Research Journal, 14(2), 116–137. 🤝This episode is co-presented by the Davao Research Journal. Submit here.