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<p>In order to develop effective teaching methods and computer-assisted language teaching systems for learners of English as a foreign language who need to study the basic linguistic competences for writing, pronunciation, reading, and listening, it is necessary to first investigate which vocabulary and grammar they have or have not yet learned. Identifying such vocabulary and grammar requires a learner corpus for analyzing the accuracy and fluency of learners’ linguistic competences. However, it is difficult to use previous learner corpora for this purpose because they have not compiled all the types of linguistic data that we need. Therefore, this study aimed to solve this problem by designing and developing a new learner corpus that compiles linguistic data regarding the accuracy and fluency of the four basic linguistic competences of writing, pronunciation, reading, and listening. The reliability and validity of the learner corpus were partially confirmed, and practical application of the learner corpus is reported here as case studies.</p>

In this chapter, the authors examined reading evaluation methods for foreign language learners based on learners’ reading processes. The goal of this chapter is twofold. The first is to evaluate text reading, and the other is to evaluate sentence reading. First, the authors assessed a text reading test to evaluate reading proficiency based on reading process, that is, effective reading speed, which is a complex measure of reading speed and comprehension rate. Statistical analysis confirms the adequacy of our effective reading speed test. Next, they propose a reading time model for evaluating reading proficiency at the sentence level. Their reading time model predicts sentence reading time based on the linguistic properties of a sentence and a learner’s proficiency. Linguistic properties consist of lexical, syntactic and discourse properties. Learners’ proficiency is defined using their score on the Test of English for International Communications (TOEIC). Their reading time model resulted in high prediction accuracy. From these results, they conclude that the reading process-based evaluation method is a promising test for foreign language reading proficiency.

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.