This paper reports and discusses on a project about designing a digital tool to support Chinese undergraduate students in reflecting on their English language (L2) learning experience. The tool namely ACTIVE was developed primarily based on a classification framework called A-S-E-R and Latent Semantic Analysis. It can automatically classify reflective L2 learning skills into four elements with each divided into four hierarchical levels. This paper begins by presenting the background of the study, followed by the details of methods of automatic classification and performance evaluation. The results of the project indicate that the computer-generated ratings for students' reflection are comparable to human ratings. Copyright © 2015 IEEE.
|Title of host publication||Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015|
|Editors||Demetrios G SAMPSON , Ronghuai HUANG , Gwo-Jen HWANG , Tzu-Chien LIU , Nian-Shing CHEN, KINSHUK, Chin-Chung TSAI|
|Place of Publication||New York|
|Publication status||Published - 2015|
CitationCheng, G., & Chau, J. (2015). Using an automatic approach to classify reflective language learning skills of ESL students. In D. G. SAMPSON, et al. (Eds.), Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015 (pp. 375-379). New York: IEEE.
- Automatic classification
- L2 learning
- Reflective skills
- Text mining