Improving Courses Management by Predicting the Number of Students

Abstract

Every year, in higher education institutions all around the world, millions of students are required to choose the curricular units they are interested in enrolling for the coming semesters. When managing courses and their respective units, colleges and universities aim to predict and understand these demands in order to better plan the next scholar year. By successfully predicting students' demands, universities are able to ensure their limited budgets and resources are allocated properly. This study intends to answer the needs of a course administrator regarding the number of students each curricular unit will have by analyzing and applying predictive models. The main focus of this work will be the prediction of the number of students enrolling in optional units, number of students per optional unit and number of students per non-optional unit in the syllabus. While some conclusions may be reached by simply measuring and extrapolating the difference in the number of students per year, identifying the cause of this difference is fundamental in the construction of a complete predictive model. Factors such as student grade average per unit, perceived difficulty or unit workload, that aren't always visible in the data, must also be taken into consideration. Once these factors are decided, the study will attempt to apply and compare different predictive functions. For the development phase of the investigation, this project will be applied to the course of Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto in the form of a case study.