An AWE-Based Diagnosis of L2 English Learners' Written Errors
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| Title: | An AWE-Based Diagnosis of L2 English Learners' Written Errors |
|---|---|
| Language: | English |
| Authors: | Lei, Jiun-Iung |
| Source: | English Language Teaching. 2020 13(10):111-119. |
| Availability: | Canadian Center of Science and Education. 1120 Finch Avenue West Suite 701-309, Toronto, OH M3J 3H7, Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: elt@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/elt |
| Peer Reviewed: | Y |
| Page Count: | 9 |
| Publication Date: | 2020 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Adult Education Higher Education Postsecondary Education |
| Descriptors: | Writing Evaluation, Computer Software, Error Analysis (Language), Second Language Learning, Second Language Instruction, English (Second Language), Grammar, Evening Programs, Essays, College Students, Error Patterns, Writing Instruction, Foreign Countries |
| Geographic Terms: | Taiwan |
| ISSN: | 1916-4742 |
| Abstract: | While Automated Writing Evaluation (AWE) can perform an error diagnosis (Chen & Cheng, 2008), previous studies used to exclude it from the process of error analysis. This study aimed to examine the reactions of Grammarly Premium towards a group of night school students' English writings at a Taiwanese technical university. The participants of the research produced 175 essays. The researcher checked the data against the AWE program. 1042 errors were detected and classified into 40 types. The 40 types of errors were at three hierarchical levels: a word and phrase level, a sentence level, and a discourse level. This study suggested future studies to view AWE's functions in a new perspective and find it a space in the process of error diagnosis. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | EJ1272190 |
| Database: | ERIC |
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