ERIC Number: EJ1254427
Record Type: Journal
Publication Date: 2020-Apr
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0033-6882
EISSN: N/A
Genre-Specific Error Detection with Multimodal Feedback
RELC Journal: A Journal of Language Teaching and Research, v51 n1 p179-187 Apr 2020
A purpose-built online error detection tool was developed to provide genre-specific corpus-based feedback on errors occurring in draft research articles and graduation theses. The primary envisaged users were computer science majors studying at a public university in Japan. This article discusses the development and evaluation of this interactive, multimodal tool. An in-house learner corpus of graduation theses was annotated for errors that affect the accuracy, brevity, clarity, objectivity and formality of scientific research writing. Software was developed to identify the errors discovered and provide learners with actionable advice and multimodal explanations in both English and Japanese. Qualitative evaluation received in usability studies and focus groups from both teachers and students was extremely positive. Preliminary quantitative evaluation of the effectiveness of the error detector was conducted. Through this pedagogic tool, learners can receive immediate actionable feedback on potential errors, and their teachers no longer feel obliged to check for common genre-specific errors.
Descriptors: Feedback (Response), Usability, Error Analysis (Language), Computational Linguistics, Computer Science Education, Majors (Students), State Universities, Theses, Accuracy, Teacher Attitudes, Student Attitudes, Language Styles, Foreign Countries, Computer Software, English for Academic Purposes, Second Language Learning, Writing (Composition)
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Japan
Grant or Contract Numbers: N/A