Predicting students' performance in online courses using multiple data sources
This work addresses the problem of improving educational outcomes through data-driven predictions for educators and institutions, but it is incremental as it builds on existing methods without major breakthroughs.
The study tackled predicting student performance in online courses by integrating multiple data sources, including a custom dataset, and found preliminary insights into which data types are most relevant for this task.
Data-driven decision making is serving and transforming education. We approached the problem of predicting students' performance by using multiple data sources which came from online courses, including one we created. Experimental results show preliminary conclusions towards which data are to be considered for the task.