Using an automatic approach to classify reflective language learning skills of ESL students

Kwok Shing CHENG, Juliana CHAU

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.
Original languageEnglish
Title of host publicationProceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015
EditorsDemetrios G SAMPSON , Ronghuai HUANG , Gwo-Jen HWANG , Tzu-Chien LIU , Nian-Shing CHEN, KINSHUK, Chin-Chung TSAI
Place of PublicationNew York
PublisherIEEE
Pages375-379
ISBN (Print)9781467373333
DOIs
Publication statusPublished - 2015

Fingerprint

rating
language
learning
English language
student
semantics
evaluation
performance
experience

Citation

Cheng, 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.

Keywords

  • Automatic classification
  • L2 learning
  • Reflective skills
  • Text mining