Timetable Generation

Abstract

Evidence based on ongoing published research shows that timetabling has been a challenge for over two decades. There is a growing need in higher education for a learner-centered solution focused on individual preferences. In the authors' earlier published work, students' group assessment information was mined to determine individualized achievements and predict future performance. In this paper, they extend the work to present a solution that uses students' individualized achievements, expected future performance, and historical registration records to discover students' registration timing patterns, as well as the most appropriate courses for registration. Such information is then processed to build the most suitable timetable for each student in the following semester. Faculty members' time preferences are also predicted based on historical teaching time patterns and course teaching preferences. The authors propose a modified frequent pattern (FP)-tree algorithm to process the predicted information. This results in clustering students to solve the timetable problem based on the predicted courses for registration. Then, it divides the timetable problem into subproblems for resolution. This ensures that time will not conflict within the generated timetables while satisfying both the hard and soft constraints. Both students' and faculty members timetabling preferences are met (88.8% and 85%).