Academic Study Plan Recommender and Simulator System Using Forward Chaining and Heuristic Algorithms

The Academic Study Plan Recommender and Simulator System introduces an innovative approach to academic planning, combining Forward Chaining and Heuristic Algorithms to generate personalized and optimized study plans. Forward Chaining enables the dynamic evolution of plans by iteratively applying rules based on individual academic goals, elective preferences, and constraints. Heuristic Algorithms enhance this process by prioritizing courses based on factors such as relevance, difficulty, and scheduling, ensuring optimal decision-making and improved user satisfaction. The system’s simulator component further empowers students by enabling them to explore various academic scenarios and adapt to changes like elective choices or shifts in majors. Feedback from IT experts revealed a 90% call for algorithmic improvements, emphasizing the need for enhanced reliability and performance, even though satisfaction with the system reached 76.8%. Functional suitability received an overall satisfaction rating of 4.27, with high ratings for functional correctness and appropriateness from both faculty and irregular students. Performance efficiency and usability achieved similarly strong ratings, averaging 4.26 and 4.21, respectively. Key usability factors like appropriateness recognizability, operability, and user interface design contributed significantly to user satisfaction. Additionally, reliability and security scored highly, averaging 4.40 and 4.26, underscoring the system's dependable and secure operations. Flexibility and compatibility were also commended, with average ratings of 4.27, reflecting the system’s adaptability and seamless integration with other tools and processes. Maintainability received a high score of 4.23, demonstrating the system’s ease of modification and testing. Faculty and irregular students particularly appreciated features such as modularity, operability, and learnability, which align with studies highlighting the importance of these factors in educational technologies. By digitizing the academic planning process, the system reduces manual effort for instructors and students while delivering accurate, adaptive study plans tailored to individual needs. The results affirm the system’s capability to enhance user experiences and support educational success through personalized and optimized planning

Academic Study Planning, Forward Chaining, Heuristic Algorithms, Usability and Satisfaction Educational Technology Optimization

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