The Journal of Information and Systems in Education
Online ISSN : 2186-3679
Print ISSN : 1348-236X
ISSN-L : 1348-236X
Short Note
Effectiveness of Linguistic and Learner Features for Listenability Measurement Using a Decision Tree Classifier
Katsunori KotaniTakehiko Yoshimi
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2017 Volume 16 Issue 1 Pages 7-11

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Abstract

As the ease of grasping the contents of listening material influences learners' motivation and learning outcome, language teachers need to choose materials appropriate for the proficiency of their learners. This heavy task has been addressed by using a traditional readability measurement method to develop an automatic measurement method of the ease of listening comprehension using linear regression analysis for listening materials. Because machine learning such as decision tree classification can properly handle different types of features, recent readability measurement methods use classification approaches such as a decision tree. Then, we proposed a measurement method using decision tree classification for linguistic features of listening materials as well as learner features of listening proficiency. The experimental results showed that the accuracy of our method (47.0%) was better than the baseline accuracy (25.2%), and that the listening test score and visiting experience in English speaking areas among the learner features were discriminative for the measurement accuracy.

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© 2017 Japanese Society for Information and Systems in Education
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