Predicting students' performance in online courses using multiple data sources

TL;DR

This paper explores the use of multiple data sources from online courses to predict student performance, aiming to enhance data-driven educational decision making.

Contribution

It introduces a new approach utilizing diverse online course data sources for performance prediction, with preliminary experimental results.

Findings

Certain data sources significantly improve prediction accuracy

Preliminary results guide future data selection for performance modeling

The approach demonstrates potential for online education analytics

Abstract

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.