#Timetable generator algorithm registration#
The authors suggest a timetable to match student registration patterns, ensuring that the registered courses suit students’ academic progress. In this paper, the authors’ solution considers student registration patterns (course timings during the week) and academic standings from completed semesters. There is a growing need in higher education institutions to create a learner-centered solution focused on individual preferences (Cook-Sather, Bovill, & Felton, 2014 Vrielink et al., 2017). As discussed by McCollum and Ireland (2006), there is a gap between theory and practice. However, there is still a lack of a general approach to timetabling. Most of the published research focuses on situational conditions based on a solution (Hosny, 2019). A recent literature review by Vrielink, Jansen, Hans, and van Hillegersberg (2017) showed that published research on timetabling has grown from a few thousand in the 1990s to over 10,000 in 2015.
This topic has been a challenge for more than two decades. The allocation of given resources to specific objects being placed in space time, in such a way as to satisfy as nearly as possible a set of desirable objectives, subjected to constraints. Both students' and faculty members timetabling preferences are met (88.8% and 85%).Īccording to Wren (1995), timetabling is defined as: This ensures that time will not conflict within the generated timetables while satisfying both the hard and soft constraints. Then, it divides the timetable problem into subproblems for resolution. This results in clustering students to solve the timetable problem based on the predicted courses for registration. The authors propose a modified frequent pattern (FP)-tree algorithm to process the predicted information. Faculty members' time preferences are also predicted based on historical teaching time patterns and course teaching preferences.
Such information is then processed to build the most suitable timetable for each student in the following semester. 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. In the authors' earlier published work, students' group assessment information was mined to determine individualized achievements and predict future performance. There is a growing need in higher education for a learner-centered solution focused on individual preferences. AbstractEvidence based on ongoing published research shows that timetabling has been a challenge for over two decades.
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