Hauptseminar
Summer Semester 2015

Natural Language Processing for Tutoring Systems

Abstract:

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.

Instructors:

Course meets:

Language:

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

  1. regularly and actively participate in class, read the papers assigned by any of the presenters and post a question on Moodle to the“Reading Discussion Forum” on each reading at the latest on the day before it is discussed in class. (10% of grade)
  2. explore and present a topic (30% of grade):
  3. actively participate in and complete the hands-on projects, documenting your work in Moodle (30% of grade)
  4. write and submit a term paper in Moodle (30% of grade). For computational linguistics students, term papers will be written in LaTeX using the standard CL journal template (http://cljournal.org/style.html) with the fullname citation style.

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.

Topics:

Note: The syllabus is subject to change, as we progress through the semester. So check the online version regularly.

References

   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.

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   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.

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   Heift, T. & M. Schulze (2007). Errors and Intelligence in Computer-Assisted Language Learning: Parsers and Pedagogues. Routledge.

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

   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.

   Lu, X. (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics 15(4), 474–496.

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   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.

   Meurers, D. (2015). Learner Corpora and Natural Language Processing. In S. Granger, G. Gilquin & F. Meunier (eds.), The Cambridge Handbook of Learner Corpus Research, Cambridge University Press.

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   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.

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Last update: June 15, 2015