A Survey on Exam Scheduling Techniques

Gashgari, Raneem; Alhashimi, Lamees; Obaid, Raed; Palaniswamy, Thangam

doi:10.1109/cais.2018.8441950

Cited by 9 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automated activity scheduling tools in higher education have been studied from different perspectives. Although some very specific use cases may be found, for example, applications to calculate the best moment (week) for scheduling an engineering syllabus course [12], in general, reported solutions are focused on two general problems: class scheduling [13] and, mainly, exam scheduling [14].…”

Section: State Of the Artmentioning

confidence: 99%

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

Bordel

Alcarria

Robles

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

In the last ten years, many different innovative learning methodologies have been proposed for engineering education. In those new approaches, students learning, and evaluation are usually supported by a catalogue of creative activities, practices, and presentations which may be very numerous if competencies to be acquired are heterogeneous and must cover a large knowledge area, such as in cybersecurity courses. In addition, when a high number of students are enrolled in the course, deadlines must be strict, to enable professors to evaluate all students and activities properly and on time. Due to this requiring schedule, students often cannot work all competencies with the expected depth, and their learning decreases. New instruments are needed to facilitate the learning of the students and the evaluation process are needed. Therefore, in this paper we proposed a new automated scheduling tool, based on graph theory, to fill this gap. The tool employs graph coloring algorithms to calculate all possible schedules for evaluation activities. Students may freely choose the schedule that best fits their personal situation, learning progress, background, etc. using a web portal where all solutions from the coloring algorithm are displayed. This tool was deployed in the Computer Engineering degree at Universidad Politécnica de Madrid, Spain. A pilot experience was conducted for three years (2020-2022) in the context of a cybersecurity course to analyze how this new tool improves the learning and the evaluation process of students. Results show both academic results and the students’ and professor’ satisfaction improve in a significant manner.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

Bordel

Alcarria

Robles

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…Pandey and Sharma [24] described different types of timetabling problem and some widely used methodologies to solve the timetabling problem like artificial ant colony (ACO), hybrid bee colony (HBC) optimisation method, particle swarm optimisation (PSO), genetic algorithm (GA), etc. Alhashimi et al [25] in their survey focused on examining the major solution approaches for the timetabling problem and comparing them in order to provide directions for academicians and researchers for further work. Alzubair et al [2] conducted a systematic literature review in the field of educational timetabling problems concerning the meta-heuristic approaches to solve the problems.…”

Section: Related Studiesmentioning

confidence: 99%

A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms

Bashab

Ibrahim

AbedElgabar

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Since university timetabling is commonly classified as a combinatorial optimisation problem, researchers tend to use optimisation approaches to reach the optimal timetable solution. Meta-heuristic algorithms have been presented as effective solutions as proven on their leverage over the last decade. Extensive literature studies have been published until today. However, a comprehensive systematic overview is missing. Therefore, this mapping study aimed to provide an organised view of the current state of the field and comprehensive awareness of the meta-heuristic approaches, by conducting meta-heuristic for solving university timetabling problems. In addition, the mapping study tried to highlight the intensity of publications over the last years, spotting the current trends and directions in the field of solving university timetabling problems, as well as having the work to provide guidance for future research by indicating the gaps and open questions to be fulfilled. Primary studies on mapping study that have been published in the last decade from 2009 until the first quarter of 2020, which consist of 131 publications, were selected as a benchmark for future research to solve university timetabling problems using meta-heuristic algorithms. The majority of the articles based on the publication type are hybrid methods (32%), in which the distribution of meta-heuristic algorithms the hybrid algorithms represent the higher application (31%). Likewise, the majority of the research is solution proposals (66%). The result of this study confirmed the efficiency and intensive application of the meta-heuristic algorithms in solving university timetabling problems, specifically the hybrid algorithms. A new trend of meta-heuristic algorithms such as grey wolf optimiser, cat swarm optimisation algorithm, Elitist self-adaptive step-size search and others with high expectations for reliable and satisfying results can be proposed to fill this gap.

show abstract

“…Constraint (1) ensures that each exam is scheduled at a time slot. Constraint (2) ensures that there is only one exam in a classroom on a given day and slot.…”

Section: Proposed Mip Modelmentioning

confidence: 99%

“…Educational timetabling for high school and university provides organization of classrooms and time in the best way for both students and instructors. It is generally divided into two groups: course timetabling and examination timetabling [1]. Course timetabling organizes courses in a term and creates programs that are followed weekly by avoiding conflicts in schedules.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, Qu et al [3] classified solution approaches as graph-based techniques, constraint-based techniques, local-search techniques, population-based algorithms, multicriteria techniques, hyperheuristics, and decomposition. Gashgari et al [1], on the other hand, categorized solution approaches as MIP, genetic algorithms, simulated annealing algorithms, quadratic assignments, and hybrid and tabu searches. It is observed that the number of articles that use metaheuristic approaches is greater than the studies dealing with exact solution methods [3].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A spreadsheet-based decision support system for examination timetabling

Güler¹,

Geçici²

2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Examination timetabling is an inevitable problem of educational institutions. Each institution has its own particular limitations; however, the main structure is the same: assigning exams to time slots and classrooms. Several institutions solve the problem manually, but it becomes more difficult every year with increasing numbers of students and limited resources. There are many studies in the literature addressing the examination timetabling problem (ETP) and providing high quality solutions within reasonable amounts of time. Nevertheless, almost none of them can be used in practice since they are not converted into a decision support system (DSS). Commercial DSSs, on the other hand, are generally transactionally based and do not have optimization capabilities, i.e. they prevent conflicts via functional user interfaces. In this study, we propose a mixed integer programming (MIP) model that addresses the ETP of the

show abstract

A Survey on Exam Scheduling Techniques

Cited by 9 publications

References 15 publications

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms

A spreadsheet-based decision support system for examination timetabling

Contact Info

Product

Resources

About