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

Autor/inn/en	Flanagan, Brendan; Hirokawa, Sachio
Titel	An Automatic Method to Extract Online Foreign Language Learner Writing Error Characteristics
Quelle	In: International Journal of Distance Education Technologies, 16 (2018) 4, S.15-30, Artikel 2 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-3100
DOI	10.4018/IJDET.2018100102
Schlagwörter	Profiles; Second Language Learning; Native Language; Classification; Feedback (Response); Second Language Instruction; Prediction; Language Usage; Error Analysis (Language); Computational Linguistics; English (Second Language); Error Correction; Social Networks; Web Sites; Japanese; Chinese; Spanish; Korean; Sino Tibetan Languages; Writing (Composition) + Suchen Sie Ihr Suchwort? Charakterisierung; Profilanalyse; Zweitsprachenerwerb; Classification system; Klassifikation; Klassifikationssystem; Fremdsprachenunterricht; Vorhersage; Sprachgebrauch; Error analysis; Language; Fehleranalyse; Linguistics; Computerlinguistik; English as second language; English; Second Language; Englisch als Zweitsprache; Korrektur; Social network; Soziales Netzwerk; Web-Design; Japaner; Japanisch; China; Chinesen; Spanisch; Koreanisch; Schreibübung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01