Designing Student's Study Plan: Decision-Based Recommendation System Towards Program Completion Using Forward Chaining Algorithm

• Published in: 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) pp. 1 - 6
• Main Authors: Ortega, John Heland Jasper C., Lagman, Ace C., de Angel, Roman M., Balmeo, Henry Dyke A., Rubio, Alexis John M., Lazaro, Joan P., Rodelas, Nelson C., Damian, Maria Amelia E., Mangaba, Joel B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.11.2023
• Subjects: Academic Advisory; Content management; Decision making; Education; Forward Chaining Algorithm; Fuzzy IF THEN rules; Humanoid robots; Planning; Prediction algorithms; Predictive and Statistical Analysis; Predictive models; Recommendation System; Security
• DOI: 10.1109/HNICEM60674.2023.10589118

Ensuring students' timely and satisfactory graduation requires evaluating their future performance based on ongoing academic records and implementing pedagogical interventions. Within an educational context, students can be categorized as regular or irregular, each subject to distinct academic rules. Regular students follow a predetermined curriculum, enjoying a clear path to graduation and improved access to required courses, facilitating efficient progress toward degree completion. Conversely, irregular students face challenges such as disruptions and delays, necessitating additional time and support to meet degree requirements. Guiding both regular and irregular students and enhancing their study plans requires proper guidance and academic intervention. To bridge the existing research gap, this study introduces a Decision-based Recommendation System towards Program Completion Using Forward Chaining Algorithm. This system automatically generates a study plan by considering defined constraints and parameters, enabling students to assess the term and year of their degree program completion. Leveraging the forward chaining algorithm with fuzzy IF-THEN-ELSE rules, the system's predictive model captures intricate relationships and dependencies within the data, yielding valuable insights and predictions. This adaptive approach refines predictions with the availability of new data, enhancing accuracy and usefulness in guiding decision-making processes related to generating a study plan.