Using Natural Language Processing Technology to Analyze Teachers’ Written Feedback on Chinese Students’ English Essays
Keywords:
Teacher Written Feedback; Advanced Educational Technology; Natural Language ProcessingAbstract
Writing an essay is a very important skill for students to
master, but a difficult task for them to overcome. It is particularly true
for English as Second Language (ESL) students in China. It would be
very useful if students can receive timely and effective feedback about
their writing. In order to build an automatic feedback system, we need
to understand the relationship between textual features and human
teacher feedback, and how well those features were used for predicting
feedback rating. In this study, we analyzed 105 Chinese English majors’
essays with teachers’ feedback and used Coh-Metrix, a computational
linguistic tool, to extract features from their writing. The study results
showed some feedback was moderately correlated to some textual
features (e.g. text easability cohesion and lexical diversity were related
to coherence feedback) and those feedback are more predictable, such as
spelling, grammar, supporting ideas and coherence. This finding has
important implications for building automated writing feedback tool.
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Copyright (c) 2015 Ming Liu, Weiwei Xu, Qiuxia Ran, Yawen Li
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