Academic Recommender System Using Engagement Advising and Backward Chaining Model

Abstract

Background: The goal of academic supervision is to help students plan their academic journey and graduate on time. An intelligent support system is needed to spot potentially struggling students and identify the issues as early as possible. Objective: This study aims to develop an academic advising recommender system that improves decision-making through system utility, ease of use, and clearly visualized information. The study also aims to find the best advising relationship model to be implemented in the proposed system. Methods: The system was modeled by following the hybrid approach to obtain information and suggest recommended actions. The recommendation was modeled by backward chaining to prevent students from dropping out. Results: To validate the recommendations given by the proposed system, we used conformity level, and the result was 94.45%. To evaluate the utility of the system, we used the backbox method, resulting in satisfactory responses. Lastly, to evaluate user acceptance, we used the technology acceptance model (TAM), resulting in 85% ease of use and 91.2% perceived usefulness for the four main features, study planning, graduate timeline simulation, progress report, and visualization of academic KPIs. Conclusion: We propose an academic recommender system with KPIs visualization and academic planning information. Keywords: Academic advising model, recommender system, backward chaining, goal-driven, technology acceptance model, certainty factor