Clustering Students' Short Text Reflections: A Software Engineering Course Case Study

Student reflections can provide instructors with beneficial knowledge regarding their progress in the course, what challenges they are facing, and how the instructor can provide more effectively to the students' needs. Reading every student reflection, however, can be a time-consuming task that may affect the instructor's ability to efficiently address student needs in a timely manner. In this research, we explore the use of clustering and sorting of student reflections to shorten reading time while maintaining a comprehensive understanding of the reflection content. We obtain student reflections from a software engineering course. Next, we generate transformer-based sentence embeddings and then cluster the reflections using K-Means. Lastly, we sort the reflections based on the distance of each reflection from its cluster center. We conduct a small-scale user study with the course's Teaching Assistants and provide promising preliminary results showing a significant increase in reading time efficiency without sacrificing understanding.