Motivating EFL Students with Conversation Data

Motivating learners of English as a Foreign Language (EFL) to improve their speaking fluency is challenging in environments where institutions emphasize reading and listening test performance. The focus tends to shift to strategic reading and listening first in order to attain acceptable test results, often at the expense of communicative competence. Computer Assisted Language Learning (CALL) is well positioned to assess and develop communicative competence for EFL learners, and to motivate them to speak. This article introduces the Objective Subjective (OS) Scoring system, a CALL system which sets clear immediate goals on the path to better communicative competence with data from videoed conversation sessions. It motivates learners to improve on their data in every consecutive conversation session, whereby an environment is created which facilitates conversation practice as well as individual error correction

A freely-available authoring system for browser-based CALL with speech recognition

[EN] A system for authoring browser-based CALL material incorporating Google speech recognition has been developed and made freely available for download. The system provides a teacher with a simple way to set up CALL material, including an optional image, sound or video, which will elicit spoken (and/or typed) answers from the user and check them against a list of specified permitted answers, giving feedback with hints when necessary. The teacher needs no HTML or Javascript expertise, just the facilities and ability to edit text files and upload to the Internet. The structure and functioning of the system are explained in detail, and some suggestions are given for practical use. Finally, some of its limitations are described.O'brien, M. (2017). A freely-available authoring system for browser-based CALL with speech recognition. The EuroCALL Review. 25(1):16-25. doi:10.4995/eurocall.2017.6830.SWORD1625251Aist, G., (1999). Speech recognition in Computer-Assisted Language Learning. In Cameron, K. (Ed.), CALL: Media, design & applications (pp. 165-181). Lisse: Swets & Zeitlinger.Bernstein, J., Najmi, A., Ehsani, F. (1999). Subarashii: Encounters in Japanese Spoken Language Education. CALICO Journal, 16(3), 361-384. Retrieved from https://calico.org/html/article_619.pdf.Bernstein, J., Van Moere, A., & Cheng, J. (2010). Validating automated speaking tests. Language Testing, 27(3), 355-377. doi:10.1177/0265532210364404Ellis, R. (2008). A typology of written corrective feedback types. ELT Journal, 63(2), 97-107. doi:10.1093/elt/ccn023Eskenazi, M. (1999). Using automatic speech processing for foreign language pronunciation tutoring: Some issues and a prototype. Language Learning & Technology, 2(2), 62-76.Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2012). Technologies for foreign language learning: a review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70-105. doi:10.1080/09588221.2012.700315Guénette, D. (2007). Is feedback pedagogically correct? Journal of Second Language Writing, 16(1), 40-53. doi:10.1016/j.jslw.2007.01.001Levy, M. & Stockwell, G. (2006). CALL dimensions: Options and issues in computer-assisted language learning. Mahwah, NJ: Lawrence Erlbaum Associates.Munro, M. J. (2011). Intelligibility: Buzzword or buzzworthy? In. J. Levis & K. LeVelle (Eds.). Proceedings of the 2nd Pronunciation in Second Language Learning and Teaching Conference, Sept. 2010. (pp.7-16),Ames,IA: Iowa State University. Retrieved from http://jlevis.public.iastate.edu/2010%20Proceedings%2010-25-11%20-%20B.pdfDe Vries, B. P., Cucchiarini, C., Bodnar, S., Strik, H., & van Hout, R. (2014). Spoken grammar practice and feedback in an ASR-based CALL system. Computer Assisted Language Learning, 28(6), 550-576. doi:10.1080/09588221.2014.889713Strick, H. (2012). ASR-based systems for language learning and therapy. In O. Engwall (Ed.), Proceedings of the International Symposium on Automatic Detection of Errors in Pronunciation Training (pp. 9-20). Retrieved from http://www.speech.kth.se/isadept/ISADEPT-proceedings.pdf.Van Doremalen, J., Boves, L., Colpaert, J., Cucchiarini, C., & Strik, H. (2016). Evaluating automatic speech recognition-based language learning systems: a case study. Computer Assisted Language Learning, 29(4), 833-851. doi:10.1080/09588221.2016.1167090Witt, S.M. (2012). Automatic Error Detection in Pronunciation Training: Where we are and where we need to go. In O. Engwall (Ed.), Proceedings of the International Symposium on Automatic Detection of Errors in Pronunciation Training (pp. 1-8). Retrieved from http://www.speech.kth.se/isadept/ISADEPT-proceedings.pdf

Assessment of EFL speaking skills in Qatari public secondary Schools: teachers' practices and challenges

This thesis aims to conduct a quantitative investigation into the practices and challenges of EFL teachers in assessing their students’ speaking skills. To collect data for this study, all EFL teachers currently working for the Ministry of Education and Higher Education in Qatar were invited to participate in an online survey using Google Forms Software. A total of 120 teachers took part in the data collection process by completing the questionnaire. Using SPSS 23 Software, the data was analyzed under five sets of assessment practices and three categories of challenges. Descriptive statistics revealed that EFL teachers were committed to providing enough time for the assessment of students’ EFL speaking skills. In addition, results proved that teachers were careful to differentiate speaking assessment tasks, use a rating scale in scoring students’ performance and provide students with feedback. However, teachers’ challenges in the assessment of EFL speaking skills were mainly related to practicality issues, the lack of relevant training and the students’ low levels of motivation and English proficiency

初級・中級レベル日本人英語学習者の英語ライティングとスピーキングにおける語彙の多様性

Tohoku UniversityWannerPeterJohn課