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An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics

An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics

Brendan Flanagan, Sachio Hirokawa
Copyright: © 2018 |Volume: 16 |Issue: 4 |Pages: 16
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781522542438|DOI: 10.4018/IJDET.2018100102
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MLA

Flanagan, Brendan, and Sachio Hirokawa. "An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics." IJDET vol.16, no.4 2018: pp.15-30. http://doi.org/10.4018/IJDET.2018100102

APA

Flanagan, B. & Hirokawa, S. (2018). An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics. International Journal of Distance Education Technologies (IJDET), 16(4), 15-30. http://doi.org/10.4018/IJDET.2018100102

Chicago

Flanagan, Brendan, and Sachio Hirokawa. "An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics," International Journal of Distance Education Technologies (IJDET) 16, no.4: 15-30. http://doi.org/10.4018/IJDET.2018100102

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Abstract

This article contends that the profile of a foreign language learner can contain valuable information about possible problems they will face during the learning process, and could be used to help personalize feedback. A particularly important attribute of a foreign language learner is their native language background as it defines their known language knowledge. Native language identification serves two purposes: to classify a learners' unknown native language; and to identify characteristic features of native language groups that can be analyzed to generate tailored feedback. Fundamentally, this problem can be thought of as the process of identifying characteristic features that represent the application of a learner's native language knowledge in the use of the language that they are learning. In this article, the authors approach the problem of identifying characteristic differences and the classification of native languages from the perspective of 15 automatically predicted writing errors by online language learners.

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