The impact of timetable on student’s absences and performance

Student’s academic performance is the point of interest for both the student and the academic institution in higher education. This performance can be affected by several factors and one of them is student absences. This is mainly due to the missed lectures and other class activities. Studies related to university timetabling investigate the different techniques and algorithms to design course timetables without analyzing the relationship between student attendance behavior and timetable design. This article first aimed at demonstrating the impact of absences and timetabling design on student’s academic performance. Secondly, this study showed that the number of absences can be caused by three main timetable design factors: namely, (1) the number of courses per semester, (2) the average number of lectures per day and (3) the average number of free timeslots per day. This was demonstrated using Educational Data Mining on a large dataset collected from Prince Sultan University. The results showed a high prediction performance reaching 92% when predicting student’s GPA based on absences and the factors related to timetabling design. High prediction performance reaching 87% was also obtained when predicting student absences based on the three timetable factors mentioned above. The results demonstrated the importance of designing course timetables in view of student absence behavior. Some suggestions were reported such as limiting the number of enrolled courses based on student’s GPA, avoiding busy and almost free days and using automated timetabling to minimize the number of predicted absences. This in turn will help in generating balanced student timetables, and thus improving student academic performance.

HYBRID APPROACH TO SOLVING OF THE AUTOMATED TIMETABLING PROBLEM IN HIGHER EDUCATIONAL INSTITUTION

The paper describes a hybrid approach to solving of the automated timetabling problem in higher educational institution based on the ant colony optimization, the genetic algorithm, and the Nelder–Mead method. The ant colony method is the basis of this algorithm, which forms the initial population for the genetic algorithm. The combination of this method with the genetic algorithm and the Nelder–Mead method reduces time of the convergence of an algorithm and eliminates the strong dependence of the results on the initial search parameters, which usually are selected experimentally. The Nelder–Mead method is used to find the parameters of the ant colony optimization method. Use of the genetic algorithm allows for reducing of algorithm running time and increasing of global optimum finding probability. The educational process timetabling in higher school is an important component of the educational process assurance system, since the schedule quality determines the comfort of the educational process participants and its quality and effectiveness. Therefore, the development of methods for computer-aided timetable generation is an important challenge. The subject of study is adaptive methods of automated university timetabling. The objective of the work is development of a hybrid approach to addressing the problem of automated timetabling in university. The results are development and research of a hybrid method and software for university timetabling that been implemented this method