Towards a Digital Assessment: Artificial Intelligence Assisted Error Analysis in ESL

Manuel Macías Borrego

doi:10.55544/ijrah.3.4.10

Authors

Manuel Macías Borrego Professor, Department of Philology and Didactics, Faculty of Education, Universidad Autónoma de Madrid, SPAIN. Professor, Department of English Didactics, Faculty of Education, Universidad Internacional de la Rioja, SPAIN. https://orcid.org/0000-0002-5376-8860

DOI:

https://doi.org/10.55544/ijrah.3.4.10

Keywords:

Artificial Intelligence, English didactics, English language teaching, Writing assessment

Abstract

The study we present here aims to explore the possibilities that new Artificial Intelligence tools offer teachers to design assessments to improve the written proficiency of students of English as a Foreign Language (the participants in this study have predominantly Spanish as their L1) in a University English Language Course with CEFR B2 objective. The group we are going to monitor is, as far as the Spanish university system is concerned, on average: more than sixty students, with diverse backgrounds and unequal proficiency in English. In such conditions, the teacher must be very attentive to meet the needs of all students/learners and, at the same time, keep track of successes and failures in the designed study plans. One of the most notable reasons for subject/class failure and dropout, in a scenario such as the one described, is the performance and time dedication to written competence (Cabrera, 2014 & López Urdaneta, 2011). Consequently, we will explore whether the union of all the theoretical baggage that underpins the linguistic and pedagogical tradition of Error Analysis, one of the most notable tools for enhancing the writing competence of English as a Foreign Language, and new intelligent technologies can provide new perspectives and strategies to effectively help learners of English as a Foreign Language to produce more appropriate written texts (more natural outputs) and, at the same time, to check whether an AI-assisted Error Analysis-based assessment produces better results in error avoidance and rule application in the collected writing samples.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Alderson, J. (2005). Diagnosing Foreign Language Proficiency. The interface between learning and assessment. London: Continuum.

Al-Fequi, A.I.M. (2012). Management of the electronic educational situations designed by motivation and its effect on student achievement and supporting the trend to use AI and expert systems by education technology students. Electronic Education Technology – Current Trends and Issues (13), 1, 2012, 187-215.

Arabski, J. (2006). Cross-linguistic influences in the second language lexicon. Toronto: Multilingual Matters.

Barnes-Hawkins, C. (2016). English language learners’ perspectives of the communicative language approach. Doctor of Education, Walden University. https://search.proquest.com/docview/1810440594?accountid=178282

Brown, H. D. (1994). Principles of Language Learning and Teaching. Upper Saddle River, NJ: Prentice Hall.

Cabrera, A., Elejalde, J. & Vine, A. (2014). Análisis de errores asistido por ordenador basado en un corpus de aprendiente de español como lengua extranjera. Revista signos: estudios de lingüística 86, 385-411.

Cabrera, P. (2014). Spanish Interference in EFL Writing Skills: A case of Ecuadorian Senior High Schools. English Language Teaching, 7 (7), 40-48.

Chaudhry, A. & Kazim, E. (2021). Artificial Intelligence in Education (AIEd): a high-level academic industry note 2021. Ai and Ethics, 157-165.

Chauhan, C., Parida, V. & Dhir, A. (2022). Linking economy and digitalisation technologies: a systematic literature review of past achievements and future promises. Technological Forecasting and Social Change, 177, 1-18.

Corder, S. (1967). The significance of learners’ errors. In J. C. Richards (Ed. Error analysis: perspectives on second language analysis (pp. 19-27). London: Longman.

Corder, S. (1993). A role of the mother tongue. In S. Gass, & L. Selinker (Eds.). Language transfer in language learning (pp. 18-31). Amsterdam: John Benjamins.

Clerk, D., Rutherford, M. (2000). Language as a confounding variable in the diagnosis of misconceptions. International Journal of Science Education, 22(7), 703-717

Daniel, A. (2018). Object Recognition Using Deep Learning. International Journal of Engineering and Technical Research (7), 352-359.

Dickson, B. (2017). How Artificial Intelligence is Shaping the Future of Education. PC Magazine, 105−115. http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=125789751&site=ehost-live

French, R. (2000). The Turing Test: The fifty years. Trends in Cognitive Sciences (4) 115-122.

Gadd, A. (2016). Minimising same error repetition and maximising progress in SLA: an integrated method. The IAFOR Journal of Language Learning (2), 1, 2016.

Gass, S. M & Selinker, L. (2008). Second Language Acquisition: an introductory course. New York: Routledge.

Göçen, A. (2020). Artificial Intelligence in Education and Schools. Research on Education and Media, 12 (1), 13-21.

Goker, U. (2021). Reflective coaching: Training for development of instructional skills and sense of efficacy of pre-service EFL teachers. Journal of Language and Linguistics Studies, 17 (1), 423 – 447.

Grewal, S. D. (2014). A critical conceptual analysis of definitions and artificial intelligence as applicable to computer engineering. IOSR Journal of Computer Engineering, 16 (2), 9-13.

Haupin, R. (2016). Improving receptive oral language skills of English language learners to enhance achievement in reading Recovery. Doctor of Education, Widener University. https://search.proquest.com/docview/1803233737?accountid=178282

Johansson, S. & Hofland, K. (1994). Towards an English-Norwegian parallel corpus. In: Fries, U. (Ed.) Creating and using English language corpora. Rodopi: Rodopi Editions.

Jodai, H. (2012). Error Analysis in a new context: a comparative study of errors in General Linguistics and CALL. The Iranian EFL Journal, 332-345.

Kasirzadeh, A. & Gabriel I. (2021). In conversation with Artificial Intelligence: aligning language models with human values. https://arxiv.org/pdf/2209.00731.pdf

Kessler, G. (2018). Technology and the future of language teaching. Foreign languages annals 51(1), 205-218.

Kessler, G. (2021). Current realities and future challenges for CALL teacher preparation. CALICO Journal 38 (3), 1-20.

Kumar, A. (2021). A quick guide to Error Analysis for Machine Learning Classification Models. Analytics Vidhya, 1 (1). Link: https://www.analyticsvidhya.com/blog/2021/08/a-quick-guide-to-error-analysis-for-machine-learning-classification-models/

Lemos, S. (2019). Self-adaptative Artificial Intelligence. IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing System (SEAMS).

Lolli, F. M.A. (2013). Semantics and pragmatics in actual software applications and in web search engines: exploringinnovations: https://arxiv.org/ftp/arxiv/papers/1307/1307.0087.pdf

López Urdaneta, J. L. (2011). Spanish-English Writing Structure Interferences in Second Language Learners. Gist Education and Learning Research Journal (5), 158-179.

Magid, S. et al. (2022). Texture-based Error Analysis for Image super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2118-2127.

Mahmood. R. K. (2021). Introducing Pragmatic Intelligence as a Prerequisite for Pragmatic Competence in Communication. Journal of University of Human Development, 7 (4), 55-59-

McLaughlin, B. (1987). Theories of second language learning. London: Routledge.

Markauskaite, L. et al. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?. Computers and Education: Artificial Intelligence (3), 1-16.

McLaughlin, B. (1987). Theories of second language learning. London: Routledge.

Murad, A. I. & Mahmood, A. H, (2018). Approaching the language of the Second Language Learner: Interlanguage and the Models Before. English Language Teaching (11), 95-108.

Nemser, W. (1971). Approximative system of foreign language. Internationational Review of Applied Linguistics (IRAL), 9 (2), 115-123.

Popovic, M. & Ney, H. (2022). Towards automatic Error Analysis of machine translation output. Computational Linguistics 37 (4), 657-688.

Rabah, A. M. T. (2020). Using Artificial Intelligence for Developing English Language Teaching/Learning: An Analytical Study from University Students’ Perspective. International Journal of English Linguistics (10), 6, 40 – 53.

Rapaport, W. (2005). Meinongian Semantics and Artificial Intelligence. Humana.Mente: Journal of Philosophical Studies, 6, 1, 1-22.

Rao, C. S. (2018). The use of English Language Research. Journal for research and professionals of English Language Learning, 8 (2), 1-8.

Richards, J. C. & Renandya, W. A. (2002). Methodology in Language Teaching: An Anthology of Current Practice. Cambridge: Cambridge University Press.

Rutherford, W. E. & Selinker, L. (2022). Rediscovering interlanguage. London: Longman.

Steels, L. (2022). Foundations for Meaning and Understanding in Human-centric AI. Venice International University: Venice. https://zenodo.org/communities/muhai/

Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10, 209-231. Https://doi.org/10.1515/iral.1972.10.1-4.209

Selinker, L. & Gass, S. M. (2008). Second Language Acquisition: an introductory course. New York: Routledge.

Shin, M. (2018). How to use artificial intelligence in the English language learning classroom. Indian Journal of Public Health Research and Development, (9), 557-568.

Shin, M., Kim, J, & Kim, M. (2021). Human Learning from Artificial Intelligence: Evidence from Human Go Players decisions after AlphaGo. Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 1795-1801.

Silva, C. (2018). Artificial Intelligence can predict human’s learning and attention. Psychol Behav Sci Int, 9 (3), 1-13 & 62-68.

Stefan, A.D.P & Sharon, K. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12 (22), 62-68.

Torben, S. (2022). Artificial Intelligence in Foreign Language Learning and Teaching: A CALL for Intelligent Practice. Anglistik: International Journal of English Studies, 33 (1), 165-184.

Viberg, O. (2022). Introduction to ‘Machine Learning and Human Learning’. In: M. E. Auer et al. (Eds.), Learning with technologies and Technologies in Learning. (pp.7-23). New York: Springer.

Wobst, J. & Lueg, R. (2022). Avoiding algorithm errors in computer-aided text analysis: selecting text analysis software. Proceedings of the Academy of Management, 1556-1560.

Yunus, M.M. & Rajendran, T. (2021). A systematic literature review on the use of Mobile Assisted Language Learning (MALL) for enhancing speaking skills among ESL and EFL learners. International Journal of Academic Research in Progressive Education and Development (10) 586-609.

Zhang, D. (2020). The progress that natural language processing has made towards human-level Ai. Journal of Artificial Intelligence Practice, 3, 38-47.