Reflection has been widely considered as an important element in student learning in higher education. Among different forms of reflective writing, one-minute papers can quickly and easily get students to reflect on their learning. Unlike short quizzes, the responses to one-minute papers could cover a wide open range and could require more time to review and summarize. When one-minute papers are administrated online, their responses are available in electronic form and this facilitates a computational approach for analysis. In this paper, we propose a machine learning approach to analyzing the students’ responses to one-minute papers. We build a text classifier to identify the topics discussed in the responses. Our results of a preliminary study conducted in a blended learning course demonstrate that the classifier can effectively detect the topics and the proposed method can be used to monitor student progress based on the detected topics. Copyright © 2017 Springer International Publishing AG.
|Title of host publication||Blended learning: New challenges and innovative practices: 10th International Conference, ICBL 2017, Hong Kong, China, June 27-29, 2017, Proceedings|
|Editors||Simon K.S. CHEUNG, Lam-for KWOK, Will W.K. MA, Lap-Kei LEE, Harrison YANG|
|Place of Publication||Cham|
|Publication status||Published - 2017|
CitationPoon, L. K. M., Li, Z., & Cheng, G. (2017). Topic classification on short reflective writings for monitoring students’ progress. In S. K. S. Cheung, L.-F. Kwok, W. W. K. Ma, L.-K. Lee, & H. Yang (Eds.), Blended learning: New challenges and innovative practices: 10th International Conference, ICBL 2017, Hong Kong, China, June 27-29, 2017, Proceedings (pp. 236-246). Cham: Springer.
- Topic classification
- One-minute papers
- Reflective writings
- Blended learning
- Learning analytics