Design of a Learner Corpus for Listening and Speaking Performance

A learner corpus is a useful resource for developing automatic assessment techniques for implementation in a computer-assisted language learning system. However, presently, learner corpora are only helpful in terms of evaluating the accuracy of learner output (speaking and writing). Therefore, the present study proposes a learner corpus annotated with evaluation results regarding the accuracy and fluency of performance in speaking (output) and listening (input).

Prediction of General ESL Proficiency Considering Learners’ Dictation Performance

This study analyzes the extent to which dictation performance and linguistic features (linguistic difficulty of sentences during dictation) can predict general proficiency in English as a second language (ESL) learners. To this end, this study constructed a multiple linear and a non-linear regression models that predict general ESL proficiency (in which independent variables were the dictation performance scores and the linguistic features of sentences) and verified the correlation between the predicted and observed general ESL proficiencies. The results showed that general ESL proficiency could be predicted by dictation performance and linguistic features. Furthermore, the results indicated significant effects on dictation accuracy, sentence length, and mean word length

A Listenability Measuring Method for an Adaptive Computer-assisted Language Learning and Teaching System

In teaching and learning of English as a foreign language, the Internet serves as a source of authentic listening material, enabling learners to practice English in real contexts. An adaptive computer-assisted language learning and teaching system can pick up news clips as authentic materials from the Internet according to learner listening proficiency if it is equipped with a listenability measuring method that takes into both linguistic features of a news clip and the listening proficiency. Therefore, we developed a method for measuring listening proficiency-based listenability. With our method, listenability is measured through multiple regression analysis using both learner and linguistic features as independent variables. Learner features account for learner listening proficiency, and linguistic features explain lexical, syntactic, and phonological complexities of sentences. A cross validation test showed that listenability measured with our method exhibited higher correlation (r = 0.57) than listenability measured with other methods using either learner features (r = 0.43) or other linguistic features (r = 0.32, r = 0.36). A comparison of our method with other methods showed a statistically significant difference (p < 0.003 after Bonferroni correction). These results suggest the effectiveness of learner and linguistic features for measuring listening proficiency-based listenability.

Automatic Classification of Language Learner Sentences into Native-like or Non-Native-like Based on Word Alignment Distribution

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