Abstract—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.
Index Terms—Teacher feedback, draft essay, automatic
classification, EFL writing.
Gary Cheng is with the Department of Mathematics and Information
Technology, The Education University of Hong Kong, Tai Po, Hong Kong
(e-mail: chengks@eduhk.hk).
Julia Chen is with the Educational Development Centre, The Hong Kong
Polytechnic University, Hung Hom, Hong Kong (e-mail:
julia.chen@polyu.edu.hk).
Dennis Foung, Vincent Lam, and Michael Tom are with the English
Language Centre, The Hong Kong Polytechnic University, Hung Hom,
Hong Kong (e-mail: dennis.foung@polyu.edu.hk,
vincent.wk.lam@polyu.edu.hk, michael.tom@polyu.edu.hk).
Cite: Gary Cheng, Julia Chen, Dennis Foung, Vincent Lam, and Michael Tom, "Towards Automatic Classification of Teacher Feedback on Student Writing," International Journal of Information and Education Technology vol. 8, no. 5, pp. 342-346, 2018.