Using Bayesian Networks and Machine Learning to Predict Computer Science Success

Nudelman, Z and Moodley, D and Berman, S (2019) Using Bayesian Networks and Machine Learning to Predict Computer Science Success, 47th Annual Conference of the Southern African Computer Lecturers' Association, SACLA 2018 Gordon's Bay, South Africa, June 18–20, 2018 Revised Selected Papers, ICT Education, Communications in Computer and Information Science, 963, 207-222, Springer.

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Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links AQ1 inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with ≈91% accuracy. This could help to increase throughput as well as student wellbeing at university.

Item Type: Journal article (paginated)
Subjects: Computing methodologies > Machine learning
Computing methodologies > Machine learning > Machine learning approaches > Learning in probabilistic graphical models > Bayesian network models
Applied computing > Education
Date Deposited: 15 Jan 2020 11:32
Last Modified: 15 Jan 2020 11:32

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