Students' Performance Prediction by using Institutional Internal and External Open Data Sources

The ability to predict students’ mark could be useful in a great number of different ways associated with university-level learning. In this study, student’s mark prediction models have been developed using institutional internal databases and external open data sources. The results of empirical study for undergraduate students’ first year mark prediction show that prediction models based on institutional internal and external data sources provide better performance with more accurate models compared to the models based on only institutional internal data sources. Moreover, this study explores the external data sources (such as National Student Survey result) as one of the best predictors in students’ mark prediction. Also, we found that students’ first semester performance is the most informative for their first year performance. We envisage that results such as the ones described in this study may increasingly improve the design of future students’ predictive models to support students to perform better in their study.