Grouping Students' Learning Patterns with Manaba's Log Data by K-Means

Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess students' learning activities using the log data. To provide teachers with a more comprehensive view of students' distinct learning behaviour patterns, and to supply personalized interventions and support to meet the specific needs of each learning group, this study focuses on how to automatically acquire learning logs from Manaba, a Japanese commercial LMS, and how to cluster students' learning activities using the k-means algorithm. Firstly, we developed a program using Python to scrape students' learning activity log information from the Manaba web pages. We collected 56446 lines of clickstreams log data from 121 students in two computer literacy hybrid classes in the fall semester of 2022 (2022/9-2023/1). Secondly, we convert the raw logs into a structured dataset with 33 features which represent each student's learning activities. Then we extract and select 15 features representing three perspectives: raw activity, time on task, and learning frequency. Thirdly, we grouped students' learning activity patterns with the three perspectives into 5 clusters by the k-means clustering algorithm. As a result, this study identified five distinct learning activity patterns depending on how much, how long and how often the students learned online. For example, cluster 1 seldom learned but spent time on learning whom we considered the disengaged or struggling students, and cluster 5 had more learning activities with little time on each activity whom we considered the well-self-regulated students. The results of this study contribute to how to monitor students' learning activity in online learning and how to assess and support student's learning by their learning activity patterns. [For the full proceedings, see ED636095.]