The rise of online courses brings in its wake new avenues for measuring student learning. Teachers don’t have to wait for end-of-term course evaluations to know their students’ thoughts; they can make inferences about performance and engagement by analyzing big data captured online. In this study, researchers used big data to gauge how learning design affected undergraduate engagement in online beginner and intermediate French and Spanish classes.
The researchers analyzed online learning behavior for four open-enrollment language-learning classes, each with about 500 students of diverse ages. The study divided the learning activities for each course into seven categories: assimilative, finding and handling information, productive, experiential, interactive/adaptive, and assessment. The researchers then measured how much time per week students spent on various types of learning activities.
Across all four courses, they found that the first weeks engaged learners in mostly assimilative and productive activities, which involve taking in information and applying new knowledge in order to memorize the foreign language. However, experiential learning, which applies language to real-life settings, began earlier in the beginners’ courses than in the intermediate classes. Also, while students in the beginner classes spent about the same amount of time engaged with the online learning platform each week, the intermediate classes saw a spike in activity during week five, followed by a drop. The researchers noted that the peak corresponds to an assessment week. They concluded that, in addition to tracking student time spent online, knowing the types of learning activities taking place in each part of a course is essential to understanding learners’ engagement.
The researchers propose that tracking students’ online activity can give teachers real-time insight into what kinds of learning activities promote more engagement. However, this study only captured how much time students’ spent on each activity; it did not measure their foreign language improvement. In order to know whether more study time translates into better outcomes, future research will have to examine students performance as well.
Rientes, B., Lewis, T., McFarlane, R., Nguyen, Q., & Toetenel, L. (2018). Analytics in online and offline language learning environments: The role of learning design to understand student online engagement. Computer Assisted Language Learning, 31(3), 272-293.Image: by Pixabay via Pexels