Analysis and Prediction of Students' Performance in a Computer-Based Course Through Real-Time Events

Students learn not only directly from their teachers and books, but also by using their computers, tablets and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this work is based on gathering real time events as students interact with learning tools and materials in electronic devices, both in and out of class. Our study shows that the analysis of these events can provide teachers with week-by-week predictions of their students' final grades and help them to identify at an early stage those students at risk of failing. A blended environment university course in which students are expected to work autonomously out of class, but also attend face-to-face lessons was used as case study. Results show that predictions are reasonably accurate even during the first weeks of the course.

data science applications in education; improving classroom teaching; teaching/learning strategies

Analysis and Prediction of Students' Performance in a Computer-Based Course Through Real-Time Events Articles