A newly published study illustrates the complexities of learning engagement (LE) in asynchronous online settings (AOSs) for university students. For university students it can be difficult to learn in such environments since these lack real-time interactions. This also makes it difficult for teachers to measure how engaged students actually are with their study materials. Through trace data, learning analytics can be used as a foundation to analyze students’ learning methods and LE.
The study investigates whether LE can be characterized by the sub-dimensions: effort, attention and content interest. The study also explores the question of which trace data from student behavior within AOSs can best represent these factors of LE in self-reports.
The research involved 764 university students and utilized best-subset regression analysis to determine which indicators most reliably represent LE. The results showed that a combination of multiple data points could account for a significant proportion of the variance in students’ self-reported engagement levels. Importantly, the set of indicators identified was stable over time, suggesting that these models could be applied across similar learning contexts.
This study has implications for both research and practice in higher education. It demonstrates the potential of learning analytics to provide deeper insights into student engagement and could lead to the development of automated feedback systems that help educators identify and support struggling learners in real-time. As AOSs become increasingly prevalent in university settings, these findings offer a valuable framework for improving the online learning experience and enhancing student outcomes.
Winter, M., Mordel, J., Mendzheritskaya, J., Biedermann, D., Ciordas-Hertel, G.-P., Hahnel, C., Bengs, D., Wolter, I., Goldhammer, F., Drachsler, H., Artelt, C., Horz, H. (2024). Behavioral trace data in an online learning environment as indicators of learning engagement in university students. Frontiers of Psychology. 15:1396881. doi: 10.3389/fpsyg.2024.1396881