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ERIC Number: EJ1093652
Record Type: Journal
Publication Date: 2016
Pages: 23
Abstractor: As Provided
Reference Count: 87
ISBN: N/A
ISSN: ISSN-0958-8221
Causal Discourse Analyzer: Improving Automated Feedback on Academic ESL Writing
Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel
Computer Assisted Language Learning, v29 n3 p494-516 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of evaluating causal discourse. The authors of the present study attempt to fill in this gap by (1) developing an automated causal discourse analyzer and (2) investigating how accurately the analyzer processes learners' causal discourse in academic writing. The accuracy of the analyzer is evaluated on cause-and-effect essays written by 17 non-native undergraduate students. The results indicate precision of 0.93, recall of 0.71, and accuracy of 0.76, which is promising for pedagogical applications of the analyzer, that is, providing learners with automated formative feedback specific to causal discourse.
Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A