Preface : CAA 2010: Computer assisted assessment: supporting student learning

The Preface for the first special issue for the International Journal of e-Assessment highlights the role of the assessment for learning agenda. It also points to papers within the special issue which relate to: (a) Overcoming the barriers to supporting student learning with computer-assisted assessment; (b) Marking and computer-assisted assessment; (c) Harnessing the technology to improve efficiency and pedagogy. The papers in this issue stress the general recognition that times are changing and that assessment needs to become embedded in the teaching/learning cycle and not purely as a checking device for the awarding institution. These papers encourage us to rise to the challenge of developing more sophisticated computer-assisted assessment systems that support student learning and there is still a lot to do

Review of Contemporary Computer-Assisted Language Learning

Book review published in the journal 'Language Learning and Technology'A review of the book 'Contemporary Computer-Assisted Language Learning' edited by M. Thomas, H. Reinders and M. Warschauer and published in 2013 by Bloomsbury

Journal Information

The Latin American Journal of Content & Language Integrated Learning (LACLIL) is a peer-reviewed journal focused on CLIL (Content and Language Integrated Learning), multilingualism, multilingual education, languages for special purposes, interculturality, and CALL (computer-assisted language learning) throughout Latin America and around the world aimed at teachers, researchers, and educational administrators who are interested in researching, implementing, or improving language-learning approaches, techniques, materials, and policies.The Latin American Journal of Content & Language Integrated Learning (LACLIL) is a peer-reviewed journal focused on CLIL (Content and Language Integrated Learning), multilingualism, multilingual education, languages for special purposes, interculturality, and CALL (computer-assisted language learning) throughout Latin America and around the world aimed at teachers, researchers, and educational administrators who are interested in researching, implementing, or improving language-learning approaches, techniques, materials, and policies

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

A reflective journal as learning process and contribution to quality and validity in interpretative phenomenological analysis

Using selected, contemporaneous illustrations from the reflective journal of a doctoral student undertaking data analysis for the first time, this article examines the relationship between journaling as a learning process when undertaking computer-assisted qualitative data analysis and establishing quality and validity in interpretative phenomenological analysis. The writing of the journal is shown both to enact some potential validity criteria (e.g. in producing an audit trail) whilst also recording and reflectively prompting the process of learning, interpretation and bracketing, thus evidencing transparency. By using a journal inside the software package and alongside the stages of the interpretative phenomenological analysis, analysis within the software package, it is argued that quality and validity become dynamic, not static constructs. These constructs are intimately linked to the researcher-learning-process and permit a critical stance to be taken

The effect of editing techniques on machine translation-informed academic foreing language writing

[EN] Although the field of machine translation has witnessed huge improvements in recent years, its potentials have not been fully exploited in other interdisciplinary areas such as foreign language teaching. The aim of this paper, therefore, is to report an experiment in which this technology was employed to teach a foreign language to a group of students. This mixed-method study explores the effect of teaching editing techniques in machine translation to a group of Persian EFL university students in an academic writing course. Twenty students took part in a 4-day workshop in which one session was devoted to teaching editing techniques and three remaining sessions to the use of editing techniques, namely, correcting mistakes, removing ambiguities, simplifying structures and combining structures. Each session consisted of a pre-test, a training and a post-test. In addition, in each session, one key writing point, namely, determiners, paraphrasing and collocations were discussed. A questionnaire for candidates’ demographic information and another for learning experiences were administered. The results indicated a statistically significant improvement in the overall gain score. Further analysis showed a significant improvement in the use of determiners in contrast to paraphrasing and collocations. Lack of improvement in data driven learning in paraphrasing and collocation seemed to stem from weakness in vocabulary and grammatical knowledge in both the mother tongue and the target language. Analysis of questionnaire data revealed that the instruction proved to be beneficial since it could be easily implemented in correction and confirmation.  On the whole, it can be concluded that providing the correct type of guidance and feedback on how to use machine translation will indeed have a profound effect on foreign language writing skill.Mirzaeian, VR. (2021). The effect of editing techniques on machine translation-informed academic foreing language writing. The EuroCALL Review. 29(2):33-43. https://doi.org/10.4995/eurocall.2021.12930OJS3343292Acar, A., Geluso, J., and Shiki, T. (2011). How can search engines improve your writing? CALL-EJ, 12(1), 1−10. Retrieved from http://callej.org/journal/12-1/Acar_2011.pdfAnderson, D. D. (1995). Machine translation as a tool in second language learning. CALICO Journal, 13(1), 68−97.BNC Consortium. (2007). The British national corpus, version 3 (BNC XML Edition). Distributed by Oxford University Computing Services on behalf of the BNC Consortium. Retrieved from http://www.natcorp.ox.ac.uk/Boulton, A. (2010). Data‐driven learning: Taking the computer out of the equation. Language Learning, 60(3), 534−572. https://doi.org/10.1111/j.1467-9922.2010.00566.xBueno, J. L. (1992). Traducción automática mediante el ordenador (Automatic Translation Using a Computer). Retrieved from http://eric.ed.gov/?id=ED354715Chambers, A., and O'Sullivan, Í. (2004). Corpus consultation and advanced learners' writing skills in French. ReCALL, 16(1), 158-172. https://doi.org/10.1017/S0958344004001211Chapelle, C. A. (2003). English language learning and technology: Lectures on teaching and research in the age of information and communication. Amsterdam: John Benjamins. https://doi.org/10.1075/lllt.7Chujo, K., Oghigian, K., and Akasegawa, S. (2015). A corpus and grammatical browsing system for remedial EFL learners. In A. Leńko-Szymańska and A. Boulton (Eds.), Multiple affordances of language corpora for data-driven learning (pp. 109-128). Amsterdam: John Benjamins. https://doi.org/10.1075/scl.69.06chuConroy, M. (2010). Internet tools for language learning: University students taking control of their writing. Australasian Journal of Educational Technology, 26(6), 861−882. https://doi.org/10.14742/ajet.1047Crosthwaite, P. (2017) Retesting the limits of data-driven learning: feedback and error correction, Computer Assisted Language Learning, 30(6), 447−473. https://doi.org/10.1080/09588221.2017.1312462Davies, M. (2008). The corpus of contemporary American English (COCA): 520 million words, 1990-present. Retrieved from http://corpus.byu.edu/coca/Egbert, J. (2005). CALL essentials: Principles and practice in CALL classrooms. Alexandria, VA: TESOL Publications.Eubanks, J., Yeh, H., and Tseng, H. (2018) Learning Chinese through a twenty-first century writing workshop with the integration of mobile technology in a language immersion elementary school, Computer Assisted Language Learning, 31(4), 346−366. https://doi.org/10.1080/09588221.2017.1399911Flowerdew, L. (2015). Data-driven learning and language learning theories. In A. Leńko-Szymańska and A. Boulton (Eds.), Multiple affordances of language corpora for data-driven learning (pp. 15−36). Amsterdam: John Benjamins. https://doi.org/10.1075/scl.69.02floFranken, M. (2014). The nature and scope of student search strategies in using a web derived corpus for writing. Language Learning Journal, 42(1), 85−102. https://doi.org/10.1080/09571736.2012.678013Garcia, I., and Pena, M. I. (2011). Machine translation-assisted language learning: Writing for beginners. Computer Assisted Language Learning, 24(5), 471−487. https://doi.org/10.1080/09588221.2011.582687Geiller, L. (2014). How EFL students can use Google to correct their 'untreatable' written errors. The EuroCALL Review, 22(2), 26−45. https://doi.org/10.4995/eurocall.2014.3633Geluso, J. (2013). Phraseology and frequency of occurrence on the web: Native speakers' perceptions of Google-informed second language writing. Computer Assisted Language Learning, 26(2), 144−157. https://doi.org/10.1080/09588221.2011.639786Granger, S., Hung, J., and Petch-Tyson, S. (Eds.). (2002). Computer learner corpora, second language acquisition, and foreign language teaching. Amsterdam: John Benjamins. https://doi.org/10.1075/lllt.6Johns, T. (1991). Should you be persuaded: Two samples of data-driven learning materials. English Language Research Journal, 4, 1−16.Kern, R. (2006). Perspectives on technology in learning and teaching languages. TESOL Quarterly, 40(1), 183−210. https://doi.org/10.2307/40264516Kilgarriff, A., and Grefenstette, G. (2003). Introduction to the special issue on the web as corpus. Computational Linguistics, 29(3), 333−347. https://doi.org/10.1162/089120103322711569Lewis, D. (1997) Machine translation in a modern languages curriculum. Computer Assisted Language Learning, 10(3), pp. 255−271. https://doi.org/10.1080/0958822970100305Liu, D., and Jiang, P. (2009). Using a corpus-based lexicogrammatical approach to grammar instruction in EFL and ESL contexts. Modern Language Journal, 93(1), 61−78. https://doi.org/10.1111/j.1540-4781.2009.00828.xMilton, J. (2006). Resource-rich web-based feedback: Helping learners become independent writers. In K. Hyland and F. Hyland (Eds.), Feedback in second language writing: Contexts and issues (pp. 123−137). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139524742.009Nesselhauf, N. (2005). Collocations in a learner corpus. Amsterdam: John Benjamins. https://doi.org/10.1075/scl.14Nino, A. (2008). Evaluating the use of machine translation post-editing in the foreign language class. Computer Assisted Language Learning, 21(1), 29−49. https://doi.org/10.1080/09588220701865482O'Neill, E. (2012). The effect of online translators on L2 writing in French (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign.O'Sullivan, Í., and Chambers, A. (2006). Learners' writing skills in French: Corpus consultation and learner evaluation. Journal of Second Language Writing, 15(1), 49-68. https://doi.org/10.1016/j.jslw.2006.01.002Park, K. (2010). Using a specialized corpus and Google custom search engine (CSE) for enhancing L2 teaching and learning. Retrieved from http://www.personal.psu.edu/xxl13/teaching/au10/588/park_2010.pdfPlonsky, L., and Oswald, F. L. (2014). How big is 'big'? Interpreting effect sizes in L2 research. Language Learning, 64, 878-912. https://doi.org/10.1111/lang.12079Reppen, R. (2010). Using corpora in the language classroom. Cambridge. UK: Cambridge University Press.Richmond, I. M. (1994) Doing it backwards: Using translation software to teach target language grammaticality. Computer Assisted Language Learning, 7(1), pp. 65−78. https://doi.org/10.1080/0958822940070106Shei, C. C. (2002) Teaching MT through pre-editing: Three case studies. Proceedings of the 6th EAMT Workshop on Teaching Machine Translation. Manchester, pp. 89−98.Shei, C.-C. (2008). Discovering the hidden treasure on the Internet: Using Google to uncover the veil of phraseology? Computer Assisted Language Learning, 21(1), 67−85. https://doi.org/10.1080/09588220701865516Smart, J. (2014). The role of guided induction in paper-based data-driven learning. ReCALL, 26(2), 184-201. https://doi.org/10.1017/S0958344014000081Somers, H., Gaspari, F., and Nino, A. (2006). Detecting inappropriate use of free online machine-translation by language students − A special case of plagiarism detection. In 11th Annual Conference of the European Association for Machine Translation-Proceedings (pp. 41−48).Todd, R. W. (2001). Induction from self-selected concordances and self-correction. System, 29, 91-102. https://doi.org/10.1016/S0346-251X(00)00047-6Yang, Y. (2018). New language knowledge construction through indirect feedback in web-based collaborative writing, Computer Assisted Language Learning, 31(4), 459−480. https://doi.org/10.1080/09588221.2017.1414852Yoon, C. (2016). Concordancers and dictionaries as problem-solving tools for ESL academic writing. Language Learning & Technology, 20(1), 209-229. Retrieved from http://llt.msu.edu/issues/february2016/yoon.pdfYoon, H., and Hirvela, A. (2004). ESL student attitudes toward corpus use in L2. Journal of Second Language Writing, 13(4), 257-283. https://doi.org/10.1016/j.jslw.2004.06.002Yoon, H., and Jo, J. W. (2014). Direct and indirect access to corpora: An exploratory case study comparing students' error correction and learning strategy use in L2 writing. Language Learning & Technology, 18(1), 96-117. Retrieved from http://llt.msu.edu/issues/february2014/yoonjo.pd

A freely-available system for browser-based Q&A practice in English, with speech recognition

[EN] A browser-based system to facilitate practice in asking and answering simple questions in English was developed. The user may ask or answer by speaking or typing and the computer’s output is in the form of speech and / or text. The types of questions handled and the permitted vocabulary are limited, though the vocabulary items may be edited freely. The system was well received in a small pilot study among Japanese students. It is freely available for download, and requires no technical expertise to deploy, just the facilities and ability to edit text files and upload to the internet.O'brien, M. (2019). A freely-available system for browser-based Q&A practice in English, with speech recognition. The EuroCALL Review. 27(2):40-52. https://doi.org/10.4995/eurocall.2019.12343OJS4052272Ashwell, T. & Elam, J.R. (2017). How accurately can the Google Web Speech API recognize and transcribe Japanese L2 English learners' oral production?JALT CALL Journal, 13(1), 59-76. https://doi.org/10.29140/jaltcall.v13n1.212Berkner, V. (2016). Revisiting Input and Output Hypotheses in Second Language Learning. Asian Education Studies, 1(1), 19-22. https://doi.org/10.20849/aes.v1i1.18Bibauw, S., François, T., & Desmet, P. (2015). Dialogue-based CALL: an overview of existing research. In F. Helm, L. Bradley, M. Guarda, & S. Thouësny (Eds), Critical CALL - Proceedings of the 2015 EUROCALL Conference, Padova, Italy (pp. 57-64). Dublin: Research-publishing.net. https://doi.org/10.14705/rpnet.2015.000310Bodnar, S., Cucchiarini, C., Penning de Vries, B., Strik, H. & van Hout, R. (2017). Learner affect in computerised L2 oral grammar practice with corrective feedback, Computer Assisted Language Learning, 30(3-4), 223-246. https://doi.org/10.1080/09588221.2017.1302964Daniels, P., & Iwago, K. (2017). The suitability of cloud-based speech recognition engines for language learning. JALT CALL Journal, 13(3), 211-221. https://doi.org/10.29140/jaltcall.v13n3.220DeKeyser, R. (2010). Practice for Second Language Learning: Don't Throw out the Baby with the Bathwater. International Journal of English Studies, 10 (1), 2010, 155-165. https://doi.org/10.6018/ijes/2010/1/114021Donesch-Jezo, E. (2011). The role of output and feedback in second language acquisition - a classroom-based study of grammar acquisition by adult English language learners. Journal of Estonian and Finno-Ugric Linguistics, 2(2), 9-28. https://doi.org/10.12697/jeful.2011.2.2.01Gimeno-Sanz, A. (2016). Moving a step further from "integrative CALL". What's to come? Computer Assisted Language Learning, 29:6, 1102-1115. https://doi.org/10.1080/09588221.2015.1103271Iwanaka, T. & Takatsuka, S. (2007). Roles of Output and Noticing in SLA: Does Exposure to Relevant Input Immediately After Output Promote Vocabulary Learning? Annual Review of English Language Education in Japan, 18, 121-130. Retrieved from https://www.jstage.jst.go.jp/article/arele/18/0/18_KJ00007108492/_pdf/-char/enJarvis, H. & Krashen, S (2014). Is CALL Obsolete? Language Acquisition and Language Learning Revisited in a Digital Age. TESL-EJ, 17(4), 1-6. Retrieved from http://tesl-ej.org/pdf/ej68/a1.pdfJones, C. (Ed.) (2018). Practice in Second Language Learning. Cambridge: Cambridge University press. https://doi.org/10.1017/9781316443118Këpuska, V., Bohouta,G. (2017). Comparing Speech Recognition Systems (Microsoft API, Google API and CMU Sphinx). Int. Journal of Engineering Research and Application, 7(3), 20-24. https://doi.org/10.9790/9622-0703022024Krashen, S.D. (1982). Principles and Practice in Second Language Acquisition. London: Pergamon. Retrieved from http://www.sdkrashen.com/content/books/principles_and_practice.pdfKrashen, S.D. & Terrell, T.D. (1983). The Natural Approach: Language Acquisition in the Classroom. San Francisco: Alemany Press. Retrieved from http://sdkrashen.com/content/books/the_natural_approach.pdfKrashen, S. (1998). Comprehensible Output. System, 26, 175-182. https://doi.org/10.1016/S0346-251X(98)00002-5Lyster, R., & Sato, M. (2013). Skill Acquisition Theory and the role of practice in L2 development. In M.G. Mayo, J. Gutierrez-Mangado, & M.M. Adrian (Eds.), Contemporary Approaches to Second Language Acquisition (pp. 71-92). Amsterdam: John Benjamins. https://doi.org/10.1075/aals.9.07ch4Mennim, P. (2007). Long-term effects of noticing on oral output. Language Teaching Research, 11(3), 265-280. https://doi.org/10.1177/1362168807077551Ponniah, R. J. and Krashen, S. (2008). The Expanded Output Hypothesis.The International Journal of Foreign Language Teaching, 4(2), 2-3. Retrieved from https://www.academia.edu/738658/The_expanded_output_hypothesisPonniah, R. J. (2010). Insights into Second Language Acquisition Theory and Different Approaches to Language Teaching. Journal on Educational Psychology, 3(4), 14-18. https://doi.org/10.26634/jpsy.3.4.1130O'Brien, M. (1997). A computer program to provide practice in questions and answers for learners of English. Computer Assisted Language Learning, 10(3), 299-305. https://doi.org/10.1080/0958822970100307O'Brien, M. (2017). A freely-available authoring system for browser-based CALL with speech recognition. EUROCALL Review, 25(1), 16-25. https://doi.org/10.4995/eurocall.2017.6830Swain, M. (1985). Communicative competence: some roles of comprehensible input and comprehensible output in its development. In S.M. Gass and C.G. Madden (Eds.) Input in second language acquisition (pp. 235-253). Rowley, MA: Newbury House.Swain, M. & Lapkin, S. (1995). Problems in Output and the Cognitive Processes they Generate: A Step Towards Second Language Learning. Applied Linguistics, 16(3), 371-91. https://doi.org/10.1093/applin/16.3.371Sydorenko, T., Smits, T., Evanini, K, & Ramanarayanan, K. (2018). Simulated speaking environments for language learning: insights from three cases. Computer Assisted Language Learning, 32(1-2), 17-48. https://doi.org/10.1080/09588221.2018.1466811van 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. https://doi.org/10.1080/09588221.2016.1167090Warschauer, M. (1996). Computer Assisted Language Learning: An Introduction. In S. Fotos (Ed.), Multimedia language teaching (pp. 3-20). Tokyo: Logos International. Retrieved from http://www.ict4lt.org/en/warschauer.htmXu, Y. & Seneff, S. (2009). Speech-Based Interactive Games for Language Learning: Reading, Translation, and Question-Answering. Computational Linguistics and Chinese Language Processing, 14(2), 133-160. Retrieved from https://www.semanticscholar.org/paper/Speech-Based-Interactive-Games-for-Language-and-Xu-Seneff/29c0398871d6b34e9a220e134f90f5cec6dfb86

Special Issue on Language Learning : Introduction

This special issue of the Journal of Artificial Intelligence in Education is based on the workshop SCIAL'93 (Cognitive Science, Computer Science and Language Learning) held in October 1993 in Clermont-Ferrand, France (Chanier, Renié & Fouqueré, 1993). The workshop gathered researchers who consider the development of interactive language learning environments as a field of multidisciplinary collaboration. Researchers belonged to several disciplines from the domain of cognitive science: linguistics (in its broad meaning, including theoretical and applied linguistics as well as language teaching), computational linguistics, computer science, psycholinguistics. The workshop was directed by Thierry Chanier, University of Clermont 2, France, who is also the editor of this special issue. New versions of selected papers focusing on Intelligent Computer Assisted Language Learning (ICALL) more specifically have been assembled here so as to introduce JAIED readers to the current research interests in ICALL. The workshop version of the papers had been selected by its multidisciplinary program committee, and then new versions have been reviewed in the normal JAIED manner

Journal information

The Latin American Journal of Content & Language Integrated Learning (LACLIL) is a no-fee, open-access, peer-reviewed journal focused on CLIL (Content and Language Integrated Learning), multilingualism, multilingual education, languages for special purposes, interculturality, and CALL (computer-assisted language learning) throughout Latin America and around the world aimed at teachers, researchers, and educational administrators who are interested in researching, implementing, or improving language-learning approaches, techniques, materials, and policies

Journal information

The Latin American Journal of Content & Language Integrated Learning (LACLIL) is a no-fee, open-access, peerreviewed journal focused on CLIL (Content and Language Integrated Learning), multilingualism, multilingual education, languages for special purposes, interculturality, and CALL (computer-assisted language learning) throughout Latin America and around the world aimed at teachers, researchers, and educational administrators who are interested in researching, implementing, or improving language-learning approaches, techniques, materials, and policie