A chow-liu score-based structure learning approach to refining course curricula in higher education
High failure and dropout rates have challenged higher education learning institutions to support students and keep them motivated throughout their undergraduate and postgraduate learning. This is not only beneficial to tertiary institutions but also to South Africa as a whole. This research adds to the field of curriculum learning using structure learning graphical modelling to refine course curricula at a tertiary level. This will assist faculties in ensuring that students are provided sufficient knowledge in their respective fields through the co-requisite and prerequisite subjects of each program. This problem is constantly being looked at in literature, with most solutions embedded in manual methods; however, in recent years, there has been a shift to looking at how artificial intelligence and machine learning can be used to produce unbiased solutions. Chow-Liu’s score-based structure learning method in conjunction with K2 scoring is used in this research, due to its ability to handle large node spaces and reduce complexity with its treestructured approach. The method is first validated using synthetically generated data before it is exposed to the real-world observational database. The data set used for this study is obtained from a South African university after removing all socio-economic and demographic data. The results have two noted benefits; one is to help refine course curricula for undergraduate degrees by suggesting co-requisite and prerequisite courses to be added for various programs and the second is to help prescribe subject selection for postgraduate students.
A research report submitted in partial fulfilment of the requirements for the degree Master of Science to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023
Chow-Liu, Score-based structure learning, Graphical modelling