Core CL Hauptseminar Winter Semester 2018

NLP for Intelligent Computer-Assisted Language Learning

Last update: December 23, 2018


Intelligent Computer-Assisted Language Learning (ICALL) is a relatively young field of interdisciplinary research exploring the integration of natural language processing in foreign language teaching and learning. The course will introduce conceptual issues and practical system development aspects of ICALL and provide the student with an overview of current research issues. Key questions discussed include the following: Where is the automatic analysis of language relevant for foreign language teaching and learning? What conceptual and practical issues are involved in natural language processing (NLP) of learner language, and what is the current state of the art in detecting errors and providing feedback to learners? How can NLP-supported research on learner corpora inform Second Language Acquisition (SLA) research? What are task and learner models and what roles do they play in ICALL systems? Where is the automatic analysis of native language relevant for foreign language learners and how can it be turned into innovative applications of real-life relevance?

Instructor: Detmar Meurers

Course meets:

Credit Points: 6 CP or 9 CP (with term paper)

Syllabus (this file):

Moodle page:

Nature of course and our expectations: This is a research-oriented Hauptseminar, in which we jointly explore perspectives and approaches on complexity in linguistics, psycholinguistics, and computational linguistics. You are expected to

  1. regularly and actively participate in class, read the papers assigned by any of the presenters and post a meaningful question on Moodle to the “Reading Discussion Forum” on each reading at the latest on the day before it is discussed in class.
  2. explore and present a topic:
  3. if you pursue the 9 CP option, work out a project term paper

Academic conduct and misconduct: Research is driven by discussion and free exchange of ideas, motivations, and perspectives. So you are encouraged to work in groups, discuss, and exchange ideas. At the same time, the foundation of the free exchange of ideas is that everyone is open about where they obtained which information. Concretely, this means you are expected to always make explicit when you’ve worked on something as a team – and keep in mind that being part of a team always means sharing the work.

For text you write, you always have to provide explicit references for any ideas or passages you reuse from somewhere else. Note that this includes text “found” on the web, where you should cite the url of the web site in case no more official publication is available.

Class etiquette: Please do not read or work on materials for other classes in our seminar. All portable electronic devices such as cell phones and laptops should be switched off for the entire length of the flight, oops, class.



    Bodnar, S., Cucchiarini, C., Penning de Vries, B., Strik, H., and van Hout, R. (2017). Learner affect in computerised l2 oral grammar practice with corrective feedback. Computer Assisted Language Learning, 30(3-4):223–246.

    Bodnar, S., Cucchiarini, C., Strik, H., and van Hout, R. (2016). Evaluating the motivational impact of call systems: current practices and future directions. Computer Assisted Language Learning, 29(1):186–212.

    Bull, S. and Kay, J. (2010). Open learner models. In Advances in intelligent tutoring systems, pages 301–322. Springer.

    Caws, C. and Heift, T. (2016). Evaluation in call: Tools, interactions, outcomes. L. Murray & F. Farr Routledge handbook of language learning and technology.

    Chen, X. and Meurers, D. (2017). Challenging learners in their individual zone of proximal development using pedagogic developmental benchmarks of syntactic complexity. In Proceedings of the Joint 6th Workshop on NLP for Computer Assisted Language Learning and 2nd Workshop on NLP for Research on Language Acquisition at NoDaLiDa 2017, Linköping Electronic Conference Proceedings 134, pages 8–17, Gothenburg, Sweden. ACL.

    Chen, X. and Meurers, D. (2019). Linking text readability and learner proficiency using linguistic complexity feature vector distance. Computer-Assisted Language Learning.

    Chinkina, M. and Meurers, D. (2016). Linguistically-aware information retrieval: Providing input enrichment for second language learners. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), pages 188–198, San Diego, CA. ACL.

    Choi, I.-C. (2016). Efficacy of an ICALL tutoring system and process-oriented corrective feedback. Computer Assisted Language Learning, 29(2):334–364.

    Crowther, D., Kim, K. M., and Loewen, S. (2017). The implementation of isla in mall technology: An investigation into the potential effectiveness of duolingo. MSU Working Papers in Second Language Studies, 8(1).

    de Vries, B. P., Cucchiarini, C., Bodnar, S., Strik, H., and van Hout, R. (2015). Spoken grammar practice and feedback in an ASR-based CALL system. Computer Assisted Language Learning, 28(6):550–576.

    de Vries, B. P., Cucchiarini, C., Bodnar, S., Strik, H., and van Hout, R. (2016). Effect of corrective feedback for learning verb second. International Review of Applied Linguistics in Language Teaching, 54(4):347–386.

    D’Mello, S. and Graesser, A. (2012). Autotutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4):23.

    D’mello, S. K., Dowell, N., and Graesser, A. (2011). Does it really matter whether students’ contributions are spoken versus typed in an intelligent tutoring system with natural language? Journal of Experimental Psychology: Applied, 17(1):1.

    Ferreira, A., Moore, J., and Mellish, C. (2007). A study of feedback strategies in foreign language classrooms and tutorials with implications for intelligent computer-assisted language learning systems. International Journal of Artificial Intelligence in Education, 17.

    Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., and Freynik, S. (2014). Technologies for foreign language learning: a review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1):70–105.

    Graesser, A. C., Chipman, P., Haynes, B. C., and Olney, A. (2005). Autotutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4):612–618.

    Hegelheimer, V. and Heift, T. (2017). Computer-assisted corrective feedback and language learning. In Nassaji, H. and Kartchava, E., editors, Corrective Feedback in Second Language Teaching and Learning, pages 51–65. Routledge.

    Heift, T. (2001). Error-specific and individualized feedback in a web-based language tutoring system: Do they read it? ReCALL, 13(2):129–142.

    Huang, Y.-T., Tseng, Y.-M., Sun, Y. S., and Chen, M. C. (2014). Tedquiz: automatic quiz generation for ted talks video clips to assess listening comprehension. In 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), pages 350–354. IEEE.

    Johnson, A., McCarthy, K., Kopp, K., Perret, C., and McNamara, D. (2017). Adaptive reading and writing instruction in istart and w-pal. In FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, pages 561–566. AAAI Press.

    Johnson, W. L., Ashish, N., Bodnar, S., and Sagae, A. (2010). Expecting the unexpected: warehousing and analyzing data from its field use. In International Conference on Intelligent Tutoring Systems, pages 352–354. Springer.

    Leacock, C., Chodorow, M., Gamon, M., and Tetreault, J. (2014). Automated Grammatical Error Detection for Language Learners, volume 25. Morgan & Claypool Publishers, 2 edition.

    Lichtarge, J., Alberti, C., Kumar, S., Shazeer, N., and Parmar, N. (2018). Weakly supervised grammatical error correction using iterative decoding. arXiv preprint arXiv:1811.01710.

    Litman, D. J. and Silliman, S. (2004). Itspoke: An intelligent tutoring spoken dialogue system. In Demonstration papers at HLT-NAACL 2004, pages 5–8. Association for Computational Linguistics.

    Madnani, N., Burstein, J., Sabatini, J., Biggers, K., and Andreyev, S. (2016). Language Muse: Automated linguistic activity generation for english language learners. Proceedings of ACL-2016 System Demonstrations, pages 79–84.

    McNamara, D. S., Allen, L. K., Crossley, S. A., Dascalu, M., and Perret, C. A. (2017). Natural language processing and learning analytics. Handbook of learning analytics, pages 93–104.

    Meurers, D., Ziai, R., Amaral, L., Boyd, A., Dimitrov, A., Metcalf, V., and Ott, N. (2010). Enhancing authentic web pages for language learners. In Proceedings of the 5th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), pages 10–18, Los Angeles. ACL.

    Ng, H. T., Wu, S. M., Briscoe, T., Hadiwinoto, C., Susanto, R. H., and Bryant, C. (2014). The conll-2014 shared task on grammatical error correction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 1–14, Baltimore, Maryland. Association for Computational Linguistics.

    Nye, B. D., Graesser, A. C., and Hu, X. (2014). Autotutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4):427–469.

    Pennington, M. C. and Rogerson-Revell, P. (2019). Using technology for pronunciation teaching, learning, and assessment. In English Pronunciation Teaching and Research, pages 235–286. Springer.

    Petersen, K. (2010). Implicit Corrective Feedback in Computer-Guided Interaction: Does Mode Matter? PhD thesis, Georgetown University.

    Polio, C. and Yoon, H.-J. (2018). The reliability and validity of automated tools for examining variation in syntactic complexity across genres. International Journal of Applied Linguistics, 28(1):165–188.

    Pujolà, J.-T. (2001). Did call feedback feed back? researching learners’ use of feedback. ReCALL, 13(1):79–98.

    Reynolds, R., Schaf, E., and Meurers, D. (2014). A view of Russian: Visual input enhancement and adaptive feedback. In Proceedings of the third workshop on NLP for computer-assisted language learning, NEALT Proceedings Series 22 / Linköping Electronic Conference Proceedings 107, pages 98–112, Uppsala. ACL.

    Ridgeway, K., Mozer, M. C., and Bowles, A. R. (2016). Forgetting of foreign-language skills: A corpus-based analysis of online tutoring software. Cognitive Science, 41(4):924–949.

    Sagae, A., Johnson, W. L., and Bodnar, S. (2010). Validation of a dialog system for language learners. In Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 241–244. Association for Computational Linguistics.

    Schodde, T., Bergmann, K., and Kopp, S. (2017). Adaptive robot language tutoring based on bayesian knowledge tracing and predictive decision-making. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’17, pages 128–136, New York, NY, USA. ACM.

    Vogt, P., De Haas, M., De Jong, C., Baxter, P., and Krahmer, E. (2017). Child-robot interactions for second language tutoring to preschool children. Frontiers in human neuroscience, 11:73.

    Walmsley, M. (2015). Learner Modelling for Individualised Reading in a Second Language. PhD thesis, The University of Waikato.

    Wang, H., Waple, C. J., and Kawahara, T. (2009). Computer assisted language learning system based on dynamic question generation and error prediction for automatic speech recognition. Speech Communication, 51(10):995–1005.

    Wilske, S. (2015). Form and meaning in dialogue-based computer-assisted language learning. PhD thesis, Universität des Saarlandes, Saarbrücken.

    Xu, J. and Bull, S. (2010). Encouraging advanced second language speakers to recognise their language difficulties: A personalised computer-based approach. Computer Assisted Language Learning, 23(2):111–127.