Summer Semester 2015
Natural Language Processing for Tutoring Systems
In this Hauptseminar we will explore how Natural Language Processing tools are used to obtain interactivity and adaptivity in Tutoring Systems. The course is designed to include both an overview of the relevant issues and hands-on practical aspects (which also motivates the fact that it meets for six hours a week). As background for the discussion of NLP in the tutoring system context, the course will include sessions introducing Tutoring Systems and their components: a) the domain module containing the knowledge of the area being taught, b) the user module, which includes a student model that tracks the student’s activity and progression; and c) the pedagogical module, which includes the teaching strategies. The course will cover those three parts for a language learning context: the computational linguistic modeling of learner language, the learner modeling, and modeling of language learning tasks. The course will include group projects designing, implementing and presenting components of a tutoring system, which will be completed in a final project.
Credits: 15 CP in MA ISCL
Moodle page: https://moodle02.zdv.uni-tuebingen.de/course/view.php?id=1130
Syllabus (this file):
NB: Be sure to check Moodle for the latest version of the syllabus.
Nature of course and our expectations: This Hauptseminar intends to provide an overview of the concepts and issues involved in research in this domain. Participants are expected to
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.
Note: The syllabus is subject to change, as we progress through the semester. So check the online version regularly.
Ai, H. & X. Lu (2013). A corpus-based comparison of syntactic complexity in NNS and NS university students’ writing. In A. Díaz-Negrillo, N. Ballier & P. Thompson (eds.), Automatic Treatment and Analysis of Learner Corpus Data, John Benjamins, pp. 249–264.
Amaral, L. & D. Meurers (2008). From Recording Linguistic Competence to Supporting Inferences about Language Acquisition in Context: Extending the Conceptualization of Student Models for Intelligent Computer-Assisted Language Learning. Computer-Assisted Language Learning 21(4), 323–338. URL http://purl.org/dm/papers/amaral-meurers-call08.html.
Amaral, L. & D. Meurers (2011). On Using Intelligent Computer-Assisted Language Learning in Real-Life Foreign Language Teaching and Learning. ReCALL 23(1), 4–24. URL http://dx.doi.org/10.1017/S0958344010000261.
Bull, S., P. Brna & H. Pain (1995). Extending the Scope of the Student Model. User Modeling and User-Adapted interaction 5, 45–65. URL http://www.eee.bham.ac.uk/bull/papers-pdf/UMUAI-95.pdf.
Chapelle, C. (1998). Multimedia CALL: Lessons to be Learned from Research on Instructed SLA. Language Learning & Technology 2(1), 21–39. URL http://llt.msu.edu/vol2num1/pdf/article1.pdf.
Chapelle, C. & Y.-R. Chung (2010). The promise of NLP and speech processing technologies in language assessment. Language Testing 27(3), 301–315. URL http://ltj.sagepub.com/content/early/2010/06/17/0265532210364405.abstract.
Díaz Negrillo, A. & J. Fernández Domínguez (2006). Error Tagging Systems for Learner Corpora. Revista Española de Lingüística Aplicada (RESLA) 19, 83–102. URL http://purl.org/net/DiazNegrillo.FernandezDominguez-06.pdf.
Díaz Negrillo, A., D. Meurers, S. Valera & H. Wunsch (2010). Towards interlanguage POS annotation for effective learner corpora in SLA and FLT. Language Forum 36(1–2), 139–154. URL http://purl.org/dm/papers/diaz-negrillo-et-al-09.html.
Ferreira, A., J. Moore & C. Mellish (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.
Flor, M., Y. Futagi, M. Lopez & M. Mulholland (2014). Patterns of misspellings in L2 and L1 English: a view from the ETS Spelling Corpus. In A.-K. Helland Gujord (ed.), Proceedings of the Learner Corpus Research Conference (LCR 2013). University of Bergen, Bergen Language and Linguistic Studies (BeLLS). To appear.
Fortmann, C. & M. Forst (2004). An LFG grammar checker for CALL. In R. Delmonte (ed.), InSTIL/ICALL 2004 Symposium on Computer Assisted Learning, NLP and speech technologies in advanced language learning systems. Venice, Italy: International Speech Communication Association (ISCA). URL http://purl.org/net/Fortmann.Forst-04.pdf.
Gamon, M., M. Chodorow, C. Leacock & J. Tetreault (2013). Using learner corpora for automatic error detection and correction. In A. Díaz-Negrillo, N. Ballier & P. Thompson (eds.), Automatic Treatment and Analysis of Learner Corpus Data, John Benjamins, pp. 127–150.
Golonka, E. M., A. R. Bowles, V. M. Frank, D. L. Richardson & S. Freynik (2014). Technologies for foreign language learning: a review of technology types and their effectiveness. Computer Assisted Language Learning 27(1), 70–105. URL http://dx.doi.org/10.1080/09588221.2012.700315.
Heift, T. (2008). Modeling Learner Variability in CALL. Computer-Assisted Language Learning 21(4), 305–321. URL http://www.informaworld.com/smpp/content~content=a903255013~db=all.
Heift, T. & P. McFetridge (1999). Exploiting the Student Model to Emphasize Language Teaching Pedagogy. In Natural Language Processing. Computer-Mediated Language Assessment and Evaluation in Natural Language Processing, ACL/IALL. pp. 55–62.
Heift, T. & D. Nicholson (2001). Web Delivery of Adaptive and Interactive Language Tutoring. International Journal of Artificial Intelligence in Education 12(4), 310–325. URL http://aied.inf.ed.ac.uk/members01/archive/vol_12/heift/paper.pdf.
Jones, A., S. Bull & G. Castellano (2013). Teacher Perspectives on the Potential for Scaffolding with an Open Learner Model and a Robotic Tutor. In G. Biswas, R. Azevedo, V. Shute & S. Bull (eds.), Proceedings of Workshop on Scaffolding in Open-Ended Learning Environments.
L’Haire, S. & A. V. Faltin (2003). Error diagnosis in the FreeText project. CALICO Journal 20(3), 481–495. URL https://www.calico.org/a-290-ErrorDiagnosisintheFreeTextProject.html.
Meurers, D. (2012). Natural Language Processing and Language Learning. In C. A. Chapelle (ed.), Encyclopedia of Applied Linguistics, Oxford: Wiley, pp. 4193–4205. URL http://purl.org/dm/papers/meurers-12.html.
Michaud, L. N. & K. F. McCoy (1998). Planning Tutorial Text in a System for Teaching English as a Second Language to Deaf Learners. In Proceedings of Integrating Artificial Intelligence and Assistive Technology, an AAAI ‘98 Workshop. Madison, Wisconsin. URL http://www.eecis.udel.edu/~mccoy/publications/1998/MichMcCo98.pdf.
Michaud, L. N. & K. F. McCoy (2004). Empirical Derivation of a Sequence of User Stereotypes for Language Learning. User Modeling and User-Adapted Interaction 14(4), 317–350. URL http://www.springerlink.com/content/lp86123772372646/.
Michaud, L. N., K. F. McCoy & L. A. Stark (2001). Modeling the Acquisition of English: An Intelligent CALL Approach. In Proceedings of The 8th International Conference on User Modeling. Sonthofen, Germany, pp. 14–25. URL http://www.eecis.udel.edu/research/icicle/pubs/MiMcSt01.ps.
Nagata, N. (2002). BANZAI: An Application of Natural Language Processing to Web based Language Learning. CALICO Journal 19(3), 583–599. URL http://www.usfca.edu/japanese/CALICO02.pdf.
Nagata, N. (2009). Robo-Sensei’s NLP-Based Error Detection and Feedback Generation. CALICO Journal 26(3), 562–579. URL https://www.calico.org/a-762-RoboSenseis\%20NLPBased\%20Error\%20Detection\%20and\%20Feedback\%20Generation.html.
Petersen, K. (2010). Implicit Corrective Feedback in Computer-Guided Interaction: Does Mode Matter? Ph.D. thesis, Georgetown University. URL http://purl.org/net/Petersen-10.pdf.
Quixal, M. (2012). Language Learning Tasks and Automatic Analysis of Learner Language. Connecting FLTL and NLP in the design of ICALL materials supporting effective use in real-life instruction. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona and Eberhard-Karls-Universität Tübingen.
Ragheb, M. & M. Dickinson (2012). Defining Syntax for Learner Language Annotation. In Proceedings of COLING 2012. Mumbai, India, pp. 965–974. URL http://cl.indiana.edu/~md7/papers/ragheb-dickinson12.html.
Reznicek, M., A. Lüdeling & H. Hirschmann (2013). Competing Target Hypotheses in the Falko Corpus: A Flexible Multi-Layer Corpus Architecture. In A. Díaz-Negrillo, N. Ballier & P. Thompson (eds.), Automatic Treatment and Analysis of Learner Corpus Data, John Benjamins, vol. 59, pp. 101–123.
Vandeventer Faltin, A. (2003). Syntactic error diagnosis in the context of computer assisted language learning. Thèse de doctorat, Université de Genève, Genève. URL http://doc.rero.ch/getfile.py?docid=215&name=VandeventerA-these&format=pdf&version=1.
Weischedel, R. M. & N. K. Sondheimer (1983). Meta-rules as a Basis for Processing Ill-formed Input. Computational Linguistics 9(3-4), 161–177. URL http://aclweb.org/anthology/J83-3003.
Wilske, S. (2014). Form and meaning in dialogue-based computer-assisted language learning. Ph.D. thesis, Universität des Saarlandes, Saarbrücken. URL http://purl.org/icall/wilske-thesis.
Wilske, S. & M. Wolska (2011). Meaning versus Form in Computer-assisted Task-based Language Learning: A Case Study on the German Dative. Journal for Language Technology and Computational Linguistics 26(1), 23–37. URL http://media.dwds.de/jlcl/2011_Heft1/3.pdf.
Last update: June 15, 2015