🇿🇦Bayesian Networks for Knowledge Discovery and Curriculum Optimisation in Academic Programmes
Presented at the 8th Annual BayesiaLab Conference on October 29, 2020.
Abstract
The purpose of this study was to develop data-driven decision support models relating to higher education. This was done by applying Bayesian networks as an artificial intelligence (AI) method to student throughput data in order to discover relationships between modules in academic programmes. In this study, we developed a Bayesian network which describes the critical pathways to success in academic programmes. We furthermore show that it can be used to optimise existing curricula in academic programmes and understand the impact of interventions such as summer schools on student success. It also identifies weaknesses such as bottlenecks within the curriculum and deficiencies in prior exposure or schooling of students in order to improve student success.
We applied Bayesian networks on two academic programmes: Engineering and Veterinary Science. These two programmes are vastly different in structure as Engineering provides more curriculum options to students and for Veterinary Science, students need to adhere to a strict set of modules for accreditation purposes.
The overall impact of this study is on academic programme decision support such as curriculum optimization and high impact intervention strategies.
Presentation Video
About the Presenter
Alta de Waal, Ph.D. Centre for Artificial Intelligence Research Department of Statistics, Faculty of Natural and Agricultural Sciences University of Pretoria, South Africa
Alta currently holds a senior lecturer position in the Department of Statistics, University of Pretoria, South Africa. She has 20 years of experience in design, development and implementation of different components in the AI value chain. She develops Bayesian network models in application areas such as student throughput models, wildlife security, environmental risk management and transportation. Alta also studies natural language processing (NLP) with a special interest in probabilistic distributional semantic methods.
Previous Presentations
Spatially Discrete Probability Maps for Anti-Poaching Efforts (Paris, 2017)
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