Fairness in examination timetabling: Student preferences and extended formulations

Abstract

Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual educational institutions, particularly universities, including specific examination criteria and the students involved. Although much has been achieved and published on the state-of-the-art problem modelling and optimisation, a lack of attention has been focussed on the students involved in the process. This work presents and utilises the results of an extensive survey seeking student preferences with regard to their individual examination timetables, with the aim of producing solutions which satisfy these preferences while still also satisfying all existing benchmark considerations. The study reveals one of the main concerns relates to fairness within the students cohort; i.e. a student considers fairness with respect to the examination timetables of their immediate peers, as highly important. Considerations such as providing an equitable distribution of preparation time between all student cohort examinations, not just a majority, are used to form a measure of fairness. In order to satisfy this requirement, we propose an extension to the state-of-the-art examination timetabling problem models widely used in the scientific literature. Fairness is introduced as a new objective in addition to the standard objectives, creating a multi-objective problem. Several real-world examination data models are extended and the benchmarks for each are used in experimentation to determine the effectiveness of a multi-stage multi-objective approach based on weighted Tchebyceff scalarisation in improving fairness along with the other objectives. The results show that the proposed model and methods allow for the production * Corresponding author of high quality timetable solutions while also providing a trade-off between the standard soft constraints and a desired fairness for each student.

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

“…Quality in education has been discussed in one of the following themes: measure the impact of knowledge [3], [20]- [22], teaching quality [23], [24], assessment quality [25] and timetabling quality [26]. Higher Educational Institutions must guarantee quality for all stakeholders: students, teachers, directors, government and society.…”

Pendekatan Hyper Heuristic Dengan Kombinasi Algoritma Pada Examination Timetabling Problem

“…Beberapa survei state-of-the-art dari formulasi exam timetabling problem, disingkat sebagai ETP, dimulai dari algoritma tradisional graph colouring, hingga meta-heuristic dan hyper-heuristic telah dilakukan pada beberapa penelitian [7]. Pendekatan meta-heuristic, disingkat sebagai MH, telah sukses diaplikasikan untuk memecahkan berbagai masalah optimasi dengan kombinasi yang kompleks termasuk dalam pengembangan state-of-the-art pada area penjadwalan.…”

An Assignment Problem and Its Application in Education Domain: A Review and Potential Path

“…It was found that the choice function appeared to be the best alternative for SA with reheating move acceptance within the hyper-heuristics approach. Next, Sabar et al [39] introduced a measurement of difficulty index to measure examination difficulty within a hybridization of hierarchical lowlevel graph colouring heuristics approach, whereas Muklason et al [40] investigated the fairness in examination timetabling by introducing multiobjective solution, also using the hyperheuristics approach.…”