Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59360-9_21 | BLENDED LEARNING: NEW CHALLENGES AND INNOVATIVE PRACTICES, ICBL 2017 |
Keywords | DocType | Volume |
Topic classification,One-minute papers,Reflective writings,Blended learning,Learning analytics | Conference | 10309 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonard K. M. Poon | 1 | 94 | 10.96 |
| Zichao Li | 2 | 1 | 1.71 |
| Gary Cheng | 3 | 3 | 2.73 |