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Criteria-Based Recommender Platform for Achieving Optimal Time-to-Graduation Using Backward Chaining Algorithm

TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON)

(2024), pp. 1290-1293

John Heland Jasper C. Ortega a , Ace C. Lagman a , Roman M. De Angel a , Abigail L. Alix a , Roland A. Calderon b , Rolando B. Barrameda c

a College of Computer Studies, FEU Institute of Technology, Manila, Philippines

b Southern Luzon State University, Quezon, Philippines

c De La Salle University, Cavite, Philippines

Abstract: To ensure students achieve timely and satisfactory graduation, it's essential to assess their future performance based on ongoing academic records and implement instructional interventions. Within the educational context, students fall into two categories: regular and irregular, each governed by distinct academic regulations. Regular students follow a predetermined curriculum, which provides a clear path to graduation and enhanced access to required courses, facilitating efficient progress toward degree completion. On the other hand, irregular students encounter challenges such as disruptions and delays, necessitating additional time and support to fulfill degree requirements. Guiding both regular and irregular students and improving their study plans require appropriate guidance and academic intervention. To address the existing research gap, this study presents a Criteria-based Recommender Platform for Achieving Optimal Time-to-Graduation Utilizing a Backward Chaining Algorithm. This platform automatically generates a personalized study plan by considering predefined criteria and parameters, enabling students to evaluate the timeline for completing their degree program. By leveraging the backward chaining algorithm, the platform's predictive model captures intricate relationships and dependencies within the data, providing valuable insights and predictions. This adaptive approach continuously refines predictions based on new data, enhancing accuracy and utility in guiding decision-making processes related to study plan generation.

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