Two case studies evaluating the effectiveness of a flipped classroom compared to a traditional classroom were performed. The studies were conducted from April 2014 to January 2015 at a private university in Tokyo, targeting 60 firstyear and 25 third-year undergraduates, respectively. In the first study, an assessment of pre-and post-treatment Test of English for International Communication (TOEIC) scores revealed students exposed to the flipped lessons improved from a mean of 474 (SD 111) to 649 (SD 96), which was greater than that of the control students who improved from 484 (SD 123) to 617 (SD 115). In the second study, students were exposed to flipped lessons for 24 weeks using a variety of materials such as the 'Lecture Ready II' digital text with iPad, COOORI e-learning software for learning words and phrases related to the digital text, ATR CALL Brix e-Learning, Newton e-Learning, and TED Talks. An assessment of pre-and post-treatment TOEIC scores and Oral Proficiency Interview by computer-based (OPIc) speaking test results showed students improved from a mean of 577 (SD 132) to 758 (SD 105), an improvement of 24% in just the speaking test. Surveys administered after exposure to the flipped lesson activities indicated students were satisfied with their flipped classroom lessons and motivated by the Blended Learning (BL) environment that incorporated mobile learning.

The use of automated systems in second-language learning could substantially reduce the workload of human teachers and test creators. This study proposes a novel method for automatically generating distractors for multiple-choice English vocabulary questions. The proposed method introduces new sources for collecting distractor candidates and utilises semantic similarity and collocation information when ranking the collected candidates. We evaluated the proposed method by administering the questions to real English learners. We further asked an expert to judge the quality of the distractors generated by the proposed method, a baseline method and humans. The results show that the proposed method produces fewer problematic distractors than the baseline method. Furthermore, the generated distractors have a quality that is comparable with that of human-made distractors.

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The present study investigates the best factor for controlling the item difficulty of multiple-choice English vocabulary questions generated by an automatic question generation system. Three factors are considered for controlling item difficulty: (1) reading passage difficulty, (2) semantic similarity between the correct answer and distractors, and (3) the distractor word difficulty level. An experiment was conducted by administering machine-generated items to three groups of English learners. The groups were determined based on their standardised English test scores. In total, 120 items, generated using combinations of the above three factors, were tested. The results reveal that the distractor word difficulty level had the greatest impact on item difficulty, but this tendency changed depending on the proficiency of the test takers. These results will be of use when implementing a fully automatic system for administrating tests.