Abstract
Understanding learners’ errors is significant for language teachers, researchers, and learners. Computer learner corpora enable us to carry out computer-aided error analysis, and as compared to traditional error analysis, it has an advantage in the storing and processing of enormous amounts of information about various aspects of learner language. The present study aims to explore the error patterns across proficiency levels in second language spoken English with data mining techniques. It also attempts to identify error types that can be used to discriminate between English learners at different proficiency levels. Spoken data for the present study were sourced from the NICT JLE Corpus, a computerized learner corpus annotated with 46 different error tags. The results of the present study indicate that there is a substantial difference in the frequencies of five types of errors, namely (a) article errors, (b) lexical verb errors, (c) normal lexical preposition errors, (d) noun number errors, and (e) tense errors, between lower- and upper- level learners. The findings will be useful for L2 learner profiling research and for the development of automated speech scoring systems
Yuichiro Kobayashi. (2014) Computer-aided Error Analysis of L2 Spoken English: A Data Mining Approach, Conference on Language and Technology 2014.
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Publisher
Center for Language Engineering
Country
Pakistan
City
Karachi
From
13-11-2014
To
15-11-2014