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TopIntroduction
According to Wren (1995), timetabling is defined as:
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.
This topic has been a challenge for more than two decades. 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. Most of the published research focuses on situational conditions based on a solution (Hosny, 2019). However, there is still a lack of a general approach to timetabling. As discussed by McCollum and Ireland (2006), there is a gap between theory and practice. A general effective solution also lacks.
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). In this paper, the authors’ solution considers student registration patterns (course timings during the week) and academic standings from completed semesters. The authors suggest a timetable to match student registration patterns, ensuring that the registered courses suit students’ academic progress. Learners can adjust the predicted course lists. Learners’ academic standings and registration patterns are based on earlier research on mined information as summarized in the subsection titled “Mining Students’ and Faculty Information.”
Another serious issue that requires planning and study is faculty members’ load allocation and timetabling. There are several constraints, including teaching specialty, experience, preference, and availability. The proposed solution analyzes faculty members’ teaching history based on the allocation of appropriate teaching loads. This automated allocation considers best time and course allocation for faculty members. Faculty members can edit their list of courses to cater for changes to load allocation and timetabling. The main purpose of the paper is to present flexibility in timetable generation for both students and faculty members. This fills the growing need for a learner-centered solution in which individual preferences are possible using data mining techniques combined with heuristics. The authors’ proposed algorithm does not look for an optimal solution in terms of time and space because the resultant optimal timetable is more important than time and space consumption.
The remainder of this paper is organized as follows. The next section presents the theoretical perspective. This is followed by research methodology, implementation, results and discussion, limitations and future directions, and the conclusion.
TopTheoretical Perspective
This section presents an overview of timetabling algorithms, the importance of students’ preferences, the FP-tree, and the authors’ previous work as summarized in the final subsection.