Towards automatic classification of teacher feedback on student writing

Towards automatic classification of teacher feedback on student writing

• Cheng, Kwok Shing The Education University of Hong Kong
• Chen, Julia The Hong Kong Polytechnic University
• Foung, Dennis The Hong Kong Polytechnic University
• Lam, Vincent The Hong Kong Polytechnic University
• Tom, Michael The Hong Kong Polytechnic University

International Journal of Information and Education Technology, 8(5), 342-346, 2018

International Association of Computer Science and Information Technology

2018

• G Curriculum, Teaching & Learning: Specific Disciplines
  • GA Chinese Language
    • GAB Teaching Conditions & Facilities, Teaching Materials
• S School Environment & Facilities, Organization
  • SA Text Books, Stationery, Teaching Equipments & Facilities, Educational Technology

Teacher Feedback Draft Essay Automatic Classification EFL Writing

• Hong Kong

• 1997.7 onwards

• Unknown or Unspecified

This paper reports and discusses the results of a study aimed at automatically categorising teacher feedback on student writing. A total of 3412 teachers’ written comments on 90 students’ draft essays were collected from an EFL course offered by a Hong Kong university during the first semester of 2016/17. The data were primarily used to design and implement an automated tool to classify teachers’ comments with respect to a taxonomy of their characteristics. The findings of this study show that the performance of the automated tool is comparable to that of human annotators, suggesting the feasibility of using the automatic approach to identify and analyse different types of teacher feedback. This study can contribute to future research into the investigation of the impact of teacher feedback on student writing in a big data world. Copyright © 2018 International Journal of Information and Education Technology. All rights reserved.

• English

Journal Articles

• 20103689

https://bibliography.lib.eduhk.hk/en/bibs/d89e2560

2018-10-03