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

Speaking Practice Outside the Classroom: A Literature Review of Asynchronous Multimedia-based Oral Communication in Language Learning

[EN] Classroom instruction provides a limited amount of quality speaking practice for language learners. Asynchronous multimedia-based oral communication is one way to provide learners with quality speaking practice outside of class. Asynchronous multimedia-based oral communication helps learners develop presentational speaking skills and raise their linguistic self-awareness. Twenty-two peer-reviewed journal articles studying the use of asynchronous multimedia-based oral communication in language learning were reviewed, (1) to explore how asynchronous oral communication has been used to improve learner speaking skills, and (2) to investigate what methodologies are commonly used to measure and analyze language gains from using asynchronous multimedia-based oral communication to improve learner speaking skills. In this study we present three principal findings from the literature. First, asynchronous multimedia-based oral communication has been used in conjunction with a variety of instructional methods to promote language gains in terms of fluency, accuracy and pronunciation. Second, the methods found in this review were technical training, preparatory activities, project-based learning, and self-evaluation with revision activities. Third, the majority of previous studies demonstrating the effectiveness of these methods have relied on learner perceptions of language gains rather than on recordings of learner speech.Young, EH.; West, RE. (2018). Speaking Practice Outside the Classroom: A Literature Review of Asynchronous Multimedia-based Oral Communication in Language Learning. The EuroCALL Review. 26(1):59-78. doi:10.4995/eurocall.2018.8599SWORD5978261American Council on the Teaching of Foreign Languages. (2012). Performance descriptors for language learners. http://www.actfl.org/publications/guidelines-and-manuals/actfl-performance-descriptors-language-learnersAbuseileek, A. F., & Qatawneh, K. (2013). Effects of synchronous and asynchronous computer-mediated communication (CMC) oral conversations on English language learners' discourse functions. Computers and Education, 62, 181-190. doi:10.1016/j.compedu.2012.10.013Bakar, N. A., Latiff, H., & Hamat, A. (2013). Enhancing ESL learners speaking skills through asynchronous online discussion forum. Asian Social Science, 9(9), 224-234. doi:10.5539/ass.v9n9p224Baker-Smemoe, W., Dewey, D. P., Bown, J., & Martinsen, R. A. (2014). Does measuring L2 utterance fluency equal measuring overall L2 proficiency? Evidence from five languages. Foreign Language Annals, 47(4), 707-728. doi: 10.1111/flan.12110Castañeda, M., & Rodríguez-González, E. (2011). L2 speaking self-ability perceptions through multiple video speech drafts. Hispania, 94(3), 483-501.Clark, R. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21-29. doi: 10.1152/advan.00094.2010Clifford, R. (2002). Achievement, performance, and proficiency testing. Paper presented at the Berkeley Language Center Colloquium on the Oral Proficiency Interview, University of California at Berkley.Crookes, G. (1989). Planning and interlanguage variation. Studies in Second Language Acquisition, 11(4), 367-383.Delaney, T. (2012). Quality and quantity of oral participation and English proficiency gains. Language Teaching Research, 16(4), 467-482. doi: 10.1177/1362168812455586Dixon, E. M., & Hondo, J. (2014). Re-purposing an OER for the online language course: A case study of Deutsch Interaktiv by the Deutsche Welle. Computer Assisted Language Learning, 27(2), 109-121. doi: 10.1080/09588221.2013.818559Dona, E., Stover, S., & Broughton, N. (2014). Modern languages and distance education: Thirteen days in the cloud. Turkish Online Journal of Distance Education, 15(3), 155-170.Engin, M. (2014). Extending the flipped classroom model: Developing second language writing skills through student-created digital videos. Journal of the Scholarship of Teaching and Learning, 14(5), 12-26. doi:10.14434/josotlv14i5.12829Fukushima, T. (2002). Promotional video production in a foreign language course. Foreign Language Annals, 35(3), 349-355.Gleason, J. & Suvorov, R. (2012). Learner perceptions of asynchronous oral computer-mediated communication: Proficiency and second language selves. Canadian Journal of Applied Linguistics, 15(1), 100-121.Goulah, J. (2007). Village voices, global visions: Digital video as a transformative foreign language learning tool. Foreign Language Annals, 40(1), 62-78. doi: 10.1111/j.1944-9720.2007.tb02854.xGromik, N. A. (2012). Computers & education cell phone video recording feature as a language learning tool: A case study. Computers & Education, 58(1), 223-230. doi: 10.1016/j.compedu.2011.06.013Graham, C. (2006). Blended learning systems: Definition, current trends, and future directions. In Bonk, C. & Graham, C. (eds.), Handbook of blended learning: Global perspectives, local designs (pp. 3-21). San Francisco: Pfeiffer. doi: 10.2307/4022859Hastie, P., Brock, S., Mowling, C. & Eiler, K. (2012). Third grade students' self-assessment of basketball dribbling tasks. Journal of Physical Education and Sport, 12(4), 427-430. doi: 10.7752/jpes.2012.04063Hirotani, M. (2009). Synchronous versus asynchronous CMC and transfer to Japanese oral performance. Calico Journal, 26(2), 413-438. doi: 10.1016/j.cpen.2012.02.001Hirotani, M. & Lyddon, P. A. (2013). The development of L2 Japanese self-introductions in an asynchronous computer-mediated language exchange. Foreign Language Annals, 46(3), 469-490. doi: 10.1111/flan.12044Hung, S. T. (2011). Pedagogical applications of Vlogs: An investigation into ESP learners' perceptions. British Journal of Educational Technology, 42(5), 736-746. doi: 10.1111/j.1467-8535.2010.01086.xJamshidi, R., LaMasters, T., Eisenberg, D., Duh, Q. Y. & Curet, M. (2009). Video self-assessment augments development of videoscopic suturing skill. Journal of the American College of Surgeons, 209(5), 622-625. doi: 10.1016/j.jamcollsurg.2009.07.024Karweit, N. (1984). Time on task reconsidered: Synthesis of research on time and learning. Educational Leadership, 41(8), 32-35.Kirkgöz, Y. (2011). A blended learning study on implementing video recorded speaking tasks in task-based classroom instruction. Turkish Online Journal of Educational Technology, 10(4), 1-13.Kitade, K. (2000). L2 learners' discourse and SLA theories in CMC: Collaborative interaction in internet chat. Computer Assisted Language Learning, 13(2), 143-166. doi: 10.1076/0958-8221(200004)13Kormos, J. & Dénes, M. (2004). Exploring measures and perceptions of fluency in the speech of second language learners. System, 32(2), 145-164. doi: 10.1016/j.system.2004.01.001Lamy, M.-N. & Goodfellow, R. (1999). "Reflective conversation" in the virtual classroom. Language Learning & Technology, 2(2), 43-61.Lepore, C. E. (2014). Influencing students' pronunciation and willingness to communicate through interpersonal audio discussions. Dimension, 73-96.Lin, H. (2015). Computer-mediated communication (CMC) in L2 oral proficiency development: A meta-analysis. ReCALL, 27(3), 261-287. doi: 10.1017/S095834401400041XMcIntosh, S., Braul, B. & Chao, T. (2003). A case study in asynchronous voice conferencing for language instruction. Educational Media International, 40(1), 63-73. doi: 10.1080/0952398032000092125Ono, Y., Onishi A., Ishihara M. & Yamashiro M. (2015). Voice-based computer mediated communication for individual practice to increase speaking proficiency: Construction and pilot study. In Zaphiris P. & Ioannou A. (eds.), Learning and collaboration technologies. LCT 2015. Lecture Notes in Computer Science, 9192. New York: Springer.Pop, A., Tomuletiu, E. A. & David, D. (2011). EFL speaking communication with asynchronous voice tools for adult students. Procedia - Social and Behavioral Sciences, 15, 1199-1203. doi: 10.1016/j.sbspro.2011.03.262Sauro, S. & Smith, B. (2010). Investigating L2 performance in text chat. Applied Linguistics, 31(4), 554-577.Segalowitz, N. (2010). Cognitive bases of second language fluency. New York: Routledge.Shih, R. (2010). Blended learning using video-based blogs: Public speaking for English as a second language students. Australasian Journal of Educational Technology, 26(6), 883-897.Sun, Y. C. (2012). Examining the effectiveness of extensive speaking practice via voice blogs in a foreign language learning context. CALICO Journal, 29(3), 494-506.Sun, Y.C. & Yang, F.Y. (2015). I help, therefore, I learn: Service learning on Web 2.0 in an EFL speaking class. Computer Assisted Language Learning, 28(3), 202-219. doi: 10.1080/09588221.2013.818555Tiraboschi, T. & Iovino, D. (2009). Learning a foreign language through the media. Journal of E-Learning and Knowledge Society, 5(3), 133-137.Tognozzi, E. & Truong, H. (2009). Proficiency and assessment using WIMBA voice technology. Italica, 86(1), 1-23.Yaneske, E. & Oates, B. (2010). Using voice boards: Pedagogical design, technological implementation, evaluation and reflections. Australasian Journal of Educational Technology, 26(8), 233-250. doi: 10.3402/rlt.v18i3.10767Ziegler, N. (2013). Synchronous computer-mediated communication and interaction: A research synthesis and meta-analysis (Doctoral dissertation). Washington, DC

A Review of Mobile Language Learning Applications: Trends, Challenges, and Opportunities

[EN] Mobile language learning applications have the potential to transform the way languages are learned. This study examined the fifty most popular commercially-available language learning applications for mobile phones and evaluated them according to a wide range of criteria. Three major trends were found: first, apps tend to teach vocabulary in isolated units rather than in relevant contexts; second, apps minimally adapt to suit the skill sets of individual learners; and third, apps rarely offer explanatory corrective feedback to learners. Despite a pedagogical shift toward more communicative approaches to language learning, these apps are behaviorist in nature. To better align with Second Language Acquisition (SLA) and L2 pedagogical research, we recommend the incorporation of more contextualized language, adaptive technology, and explanatory feedback in these applications.Heil, CR.; Wu, JS.; Lee, JJ.; Schmidt, T. (2016). A Review of Mobile Language Learning Applications: Trends, Challenges, and Opportunities. The EuroCALL Review. 24(2):32-50. doi:10.4995/eurocall.2016.6402.SWORD3250242Bachman, L. F., & Palmer, A. S. (1996). Language testing in practice: Designing and developing useful language tests. Oxford, UK: Oxford University Press.BLYTH, C. (1997). A Constructivist Approach to Grammar: Teaching Teachers to Teach Aspect. The Modern Language Journal, 81(1), 50-66. doi:10.1111/j.1540-4781.1997.tb01626.xBrown, H. D. (2007). Teaching by principles: An interactive approach to language pedagogy. White Plains, NY: Pearson Education.Burston, J. (2014). Twenty years of MALL project implementation: A meta-analysis of learning outcomes. ReCALL, 27(1), 4-20. doi:10.1017/s0958344014000159CANALE, M. (1980). THEORETICAL BASES OF COMMUNICATIVE APPROACHES TO SECOND LANGUAGE TEACHING AND TESTING. Applied Linguistics, 1(1), 1-47. doi:10.1093/applin/1.1.1Carroll, S., & Swain, M. (1993). Explicit and Implicit Negative Feedback. Studies in Second Language Acquisition, 15(3), 357-386. doi:10.1017/s0272263100012158Chen, C.-M., & Chung, C.-J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624-645. doi:10.1016/j.compedu.2007.06.011Chinnery, G. (2006). Going to the MALL: Mobile Assisted. Language Learning, Language Learning & Technology, 10(1), 9-16.Duman, G., Orhon, G., & Gedik, N. (2014). Research trends in mobile assisted language learning from 2000 to 2012. ReCALL, 27(2), 197-216. doi:10.1017/s0958344014000287Ellis, R., Loewen, S., & Erlam, R. (2006). IMPLICIT AND EXPLICIT CORRECTIVE FEEDBACK AND THE ACQUISITION OF L2 GRAMMAR. Studies in Second Language Acquisition, 28(02). doi:10.1017/s0272263106060141Gamper, J., & Knapp, J. (2002). A Review of Intelligent CALL Systems. Computer Assisted Language Learning, 15(4), 329-342. doi:10.1076/call.15.4.329.8270Godwin-Jones, R. (2011). Emerging technologies: Mobile apps for language learning. Language Learning & Technology, 15(2), 2-11. Retrieved from http://llt.msu.edu/issues/june2011/emerging.pdfGrohtjahn, R. (1987). On the methodological basis of introspective methods. In C. Faerch & G. Kasper (Eds.), Introspection in second language research (54-82). Clevedon, England: Multilingual Matters.Hymes, D.H. (1972) "On Communicative Competence" In: J.B. Pride and J. Holmes (eds.) Sociolinguistics. Selected Readings. Harmondsworth: Penguin, pp. 269–293.Kukulska-Hulme, A., & Bull, S. (2009). Theory-based Support for Mobile Language Learning: Noticing and Recording. International Journal of Interactive Mobile Technologies (iJIM), 3(2). doi:10.3991/ijim.v3i2.740Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20(3), 271-289. doi:10.1017/s0958344008000335Lee, Kwang-wu. "English teachers' barriers to the use of computer-assisted language learning." The Internet TESL Journal 6.12 (2000): 1-8.Lyster, R., & Ranta, L. (1997). CORRECTIVE FEEDBACK AND LEARNER UPTAKE. Studies in Second Language Acquisition, 19(1), 37-66. doi:10.1017/s0272263197001034Jurafsky, D., & Martin, J. H. (2008). Speech and language processing: An introduction to speech recognition. Computational Linguistics and Natural Language Processing. Prentice Hall.Lee, J.F. & VanPatten, B. (2003). Making communicative language teaching happen (2nd ed.). New York: McGraw-Hill.Moundridou, M., & Virvou, M. (2003). Analysis and design of a Web-based authoring tool generating intelligent tutoring systems. Computers & Education, 40(2), 157-181. doi:10.1016/s0360-1315(02)00119-7Nah, K. C., White, P., & Sussex, R. (2008). The potential of using a mobile phone to access the Internet for learning EFL listening skills within a Korean context. ReCALL, 20(3), 331-347. doi:10.1017/s0958344008000633Nation, P. (2015). Principles guiding vocabulary learning through extensive reading. Reading in a Foreign Language, 27(1), 136.OXFORD, R. L. (1997). Cooperative Learning, Collaborative Learning, and Interaction: Three Communicative Strands in the Language Classroom. The Modern Language Journal, 81(4), 443-456. doi:10.1111/j.1540-4781.1997.tb05510.xPellegrino, J., Chudowski, N., and Glaser, R. (2001). Knowing what students know: the science and design of assessment. National Academies Press.Purpura, J. E. (2004). Assessing Grammar. doi:10.1017/cbo9780511733086Reinders, H., & Pegrum, M. (2016). Supporting Language Learning on the Move: An Evaluative Framework for Mobile Language Learning Resources. In B. Tomlinson (Ed.), Research and Materials Development for Language Learning (pp. 219-232). New York, NY: Routledge.Sauvignon, S. J. (2002). Interpreting communicative language teaching: Contexts and concerns in teacher education. New Haven: Yale University.Stockwell, G. (2008). Investigating learner preparedness for and usage patterns of mobile learning. ReCALL, 20(3), 253-270. doi:10.1017/s0958344008000232Sweeney, P., & Moore, C. (2012). Mobile Apps for Learning Vocabulary. International Journal of Computer-Assisted Language Learning and Teaching, 2(4), 1-16. doi:10.4018/ijcallt.2012100101Ur, P. (2013). The communicative approach revisited. Cambridge: Cambridge University Press. Retrieved from http://www.cambridge.com.mx/pennyur/Penny-TCAR.pdfWarschauer, M. (1996b). Computer-assisted language learning: an introduction. In S. Fotos (Ed.), Multimedia language teaching, 3–20. Tokyo: Logos.Warschauer, M., & Healey, D. (1998). Computers and language learning: an overview. Language Teaching, 31(2), 57-71. doi:10.1017/s0261444800012970Wu, T. Sung, W. and Burston, J (2011) Reexamining the effectiveness of vocabulary learning via mobile phones. Turkish Online Journal on Educational Technology, 10(3), 203-214

Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration

Machine Translation for Indian languages is an emerging research area. Transliteration is one such module that we design while designing a translation system. Transliteration means mapping of source language text into the target language. Simple mapping decreases the efficiency of overall translation system. We propose the use of stemming and part-of-speech tagging for transliteration. The effectiveness of translation can be improved if we use part-of-speech tagging and stemming assisted transliteration.We have shown that much of the content in Gujarati gets transliterated while being processed for translation to Hindi language

Mobile-assisted language learning [Revised and updated version]

Mobile-assisted language learning (MALL) is the use of smartphones and other mobile technologies in language learning, especially in situations where portability and situated learning offer specific advantages. A key attraction of mobile learning is the ubiquity of mobile phones. Typical applications can support learners in reading, listening, speaking and writing in the target language, either individually or in collaboration with one another. Increasingly, MALL applications relate language learning to a person’s physical context when mobile, primarily to provide access to location-specific language material or to enable learners to capture aspects of language use in situ and share it with others. Mobile learning can be formal or informal, and mobile devices may form a bridge connecting in-class and out-of-class learning. When learning takes place outside the classroom, it is often beyond the reach and control of the teacher. This can be perceived as a threat, but it is also an opportunity to revitalize and rethink current approaches to teaching and learning. Mobile learning appeals to a wide range of people for a variety of reasons. It may exclude some learners but it is often a mechanism for inclusion. It is likely that the next generation of mobile learning will be more ubiquitous, which means that there will be smart systems everywhere for digital learning. Mobile learning is proving its potential to address authentic learner needs at the point at which they arise, and to deliver more flexible models of language learning

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

E/Valuating new media in language development

This paper addresses the need for a new approach to the educational evaluation of software that falls under the rubric "new media" or "multimedia" as distinct from previous generations of Computer-Assisted Language Learning (CALL) software. The authors argue that present approaches to CALL software evaluation are not appropriate for a new genre of CALL software distinguished by its shared assumptions about language learning and teaching as well as by its technical design. The paper sketches a research-based program called "E/Valuation" that aims to assist language educators to answer questions about the educational effectiveness of recent multimedia language learning software. The authors suggest that such program needs to take into account not only the nature of the new media and its potential to promote language learning in novel ways, but also current professional knowledge about language learning and teaching

Use of L2 Pronunciation Techniques in and Outside Classes: Students’ Preferences

The present study describes the level of effectiveness of both traditional and computer-assisted second language pronunciation techniques from the students’ perspectives. By traditional techniques we mean those activities which make use of phonetic alphabet, including transcription practice, detailed description of the articulatory systems, drills (e.g. minimal pair drills), reading aloud, tongue twisters, rhymes, etc. (Hismanoglu and Hismanoglu 2010: 985). On the other hand, computer-assisted techniques include activities based on listening and imitating tasks, which use technology, such as self-imitation practice, recordings of L2 learner’s, visual aids, and automatic speech recognition tools. The main aim of this study does not aim to classify L2 pronunciation methods by allocating them to previously mentioned categories but rather attempts to examine the intricate relationship between students’ knowledge, perceptions, attitudes and their most preferable practices which, in their opinion, result in improvement of their L2 pronunciation. 118 study subjects were asked to complete four main questions, within which tasks based on the Likert-scale items gathered data about the students’ most preferable L2 pronunciation teaching and learning techniques. The students were asked to create their own list, starting from the most useful to the least beneficial techniques. The last task was an open-ended question about other techniques than mentioned in the questionnaire. The analysis of the obtained data involved a two-stage process: a) data segmentation; and b) techniques categorisation. The first step was to select pronunciation learning techniques in terms of their frequency and use and to adjust them to the research group. The second stage, techniques categorisation, was based on a careful analysis of the answers given by the students in the questionnaire. Following that, five categories were distinguished: (1) traditional and used only in the classroom, (2) traditional but also used in distance learning, (3) computer-assisted but used only in the classroom, (4) computer-assisted and also used in distance learning, (5) innovative: combining students’ needs and available online.Highlighting the prominence of pronunciation in acquiring communicative competence, the authors propose their own, innovative suggestions for the future creation of teaching materials