Enhancing Pre-Tertiary Students Decision-Making using a Web-Based Admission Recommender System

Education plays a pivotal role in individual development, imparting growth, values, and cultural understanding. In the realm of education, university education emerges as a transformative phase crucial for professional life. The selection of the right course during these formative years significantly shapes one's life trajectory. Amidst the complexities of this decision-making stage, exacerbated by societal pressures, pre-tertiary students often grapple with confusion. This research addresses the unique challenges faced by pre-tertiary students through the introduction of a Web-Based Admission Recommender System. Unlike existing systems, our recommender system empowers students to autonomously make well-informed decisions about their course of study, considering their distinctive abilities. The system integrates three crucial parameters: preferred core subjects’ combination, Intelligence Quotient, and Career Interest. Implemented with the Catboost Classification utilizing the Gradient Boosting Algorithm, and featuring a user interface designed with Bootstrap 3, Python, and Flask, the system underwent rigorous testing with primary data from 346 secondary school students in their final year. The evaluation showcased commendable accuracy, with a notable 86.71% accuracy rate, 74.4% precision, 80% recall, and an 83% f1 score rate. This research makes distinctive contributions to the field by significantly enhancing the decision-making process for pretertiary students. The recommender system emerges as a reliable tool, uniquely positioned to guide students in selecting courses aligned with their individual capabilities and aspirations. By addressing the nuanced needs of pre-tertiary students, our research sets a new standard in personalized educational guidance.