TY - JOUR
T1 - An automatic method to extract online foreign language learner writing error characteristics
AU - Flanagan, Brendan
AU - Hirokawa, Sachio
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 15J04830.
Publisher Copyright:
Copyright © 2018, IGI Global.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85052526612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052526612&partnerID=8YFLogxK
U2 - 10.4018/IJDET.2018100102
DO - 10.4018/IJDET.2018100102
M3 - Article
AN - SCOPUS:85052526612
SN - 1539-3100
VL - 16
SP - 15
EP - 30
JO - International Journal of Distance Education Technologies
JF - International Journal of Distance Education Technologies
IS - 4
ER -