Planning, Scheduling, and Timetabling in a University Setting

Methods and procedures for modeling university student populations, predicting course enrollment, allocating course seats, and timetabling final examinations are studied and proposed. The university enrollment model presented uses a multi-dimensional state space based on student demographics and the Markov property, rather than longitudinal data to model student movement. The procedure for creating adaptive course prediction models uses student characteristics to identify groups of undergraduates whose specific course enrollment rates are significantly different than the rest of the university population. Historical enrollment rates and current semester information complete the model for predicting enrollment for the coming semester. The course prediction model aids in the system for reserving course seats for new students during summer registration sessions. The seat release model addresses how to estimate seat need each session, how to release seats among multiple course sections, and how to predict seat shortages and surpluses. Finally, procedures for creating reusable university final examination timetables are developed and compared. Course times, rather than individual courses, are used as the assignment elements because the demand for course times remains relatively constant despite changes in course schedules. Our heuristic procedures split the problem into two phases: a clustering phase--to minimize conflicts--and a sequencing phase--to distribute exams throughout finals week while minimizing the occurrence of consecutive exams. Results for all methods are compared using enrollment data from Clemson University

Modeling postgraduate students flow

The average time taken to complete a course and the completion rate in higher education vary among students at a university according to their field of study, age and mode of study. Moreover, without any quantitative tools in predicting student admissions into the university, its management will encounter problems in setting up their strategic planning and allocating their resources. Hence, this study aims to predict the number of student enrolments each semester and understand the flow of students in a system, such as the mean time they spend in their postgraduate programs, and the estimated probability of graduating as well as dropping out from their studies.A data of full-time and part-time postgraduate students at Universiti Utara Malaysia (UUM) was chosen for this study.Previous works have been discussed on several methods used in an enrolment projection, and the most suitable method to be used in this study is the Markov Chain Model.The validity of the model is evaluated by comparing it with the historical data using mean absolute percent errors (MAPE).The result shows that the Markov Chain Model excels in making the enrolment projection, and the absorbing state used can analyse the system in further depth