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Predictive models with machine learning algorithms to forecast students' performance
Citation
Khor, E. T. (2019). Predictive models with machine learning algorithms to forecast students' performance. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), Proceedings of the 13th International Technology, Education and Development (pp. 2831-2837). International Academy of Technology, Education and Development. https://doi.org/10.21125/inted.2019.0757
Abstract
Machine learning is gaining increasing popularity in the education sector due to its potential to improve various aspects of the education system. The present study aims to develop eight types of predictive models to forecast students’ academic performance. The first four predictive models were developed based on the (i) demographical features, (ii) academic background features, (iii) parents support features, and (iv) learning behaviour features and the other four predictive models were generated based on the (i) demographical features, (ii) academic background features, and (iii) parents support features. The machine learning algorithms used to develop the models include the decision tree, the naïve Bayes, the support vector machine and the neural network. From the result analysis of the dataset, it showed that support vector machine algorithm performs high prediction on students’ academic performance. The findings also revealed that learning behaviour features are significant and have a greater impact on students’ academic performance.
Date Issued
2019
ISBN
9788409086191