KI - Künstliche Intelligenz

, Volume 29, Issue 4, pp 413–418 | Cite as

Learning Feedback in Intelligent Tutoring Systems

Report of the FIT Project, Conducted from December 2011 to March 2015
  • Sebastian Gross
  • Bassam Mokbel
  • Barbara Hammer
  • Niels Pinkwart
Research Project

Abstract

Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme “Autonomous Learning”, the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.

Keywords

Intelligent tutoring systems Example-based feedback Machine learning Prototype-based clustering 

References

  1. 1.
    Anderson JR, Conrad FG, Corbett AT (1989) Skill acquisition and the lisp tutor. Cogn Sci 13(4):467–505CrossRefGoogle Scholar
  2. 2.
    Anderson JR, Corbett AT, Koedinger KR, Pelletier R (1995) Cognitive tutors: lessons learned. J Learn Sci 4:167–207CrossRefGoogle Scholar
  3. 3.
    Brusilovsky P, Yudelson M (2008) From webex to navex: interactive access to annotated program examples. Proc IEEE 96(6):990–999CrossRefGoogle Scholar
  4. 4.
    Coull N, Duncan I, Archibald J, Lund G (2003) Helping novice programmers interpret compiler error messages. In: Proceedings of the 4th annual LTSN-ICS conference. National University of Ireland, Galway, pp 26–28Google Scholar
  5. 5.
    Gross S, Mokbel B, Hammer B, Pinkwart N (2012) Feedback provision strategies in intelligent tutoring systems based on clustered solution spaces. In: Desel J, Haake JM, Spannagel C (eds) Tagungsband der 10. e-learning Fachtagung Informatik (DeLFI), no. P-207 in GI lecture notes in informatics. GI, Bonn, Germany, pp 27–38Google Scholar
  6. 6.
    Gross S, Mokbel B, Hammer B, Pinkwart N (2013) Towards a domain-independent its middleware architecture. In: Chen NS, Huang R, Kinshuk, Li Y, Sampson DG (eds) Proceedings of the 13th IEEE international conference on advanced learning technologies (ICALT). IEEE Computer Society Press, Los Alamitos, pp 408–409Google Scholar
  7. 7.
    Gross S, Mokbel B, Hammer B, Pinkwart N (2013) Towards providing feedback to students in absence of formalized domain models. In: Lane HC, Yacef K, Mostow J, Pavlik P (eds) Proceedings of the 16th international conference on artificial intelligence in education (AIED). Lecture notes in computer science, vol 7926. Springer, Berlin, pp 644–648Google Scholar
  8. 8.
    Gross S, Mokbel B, Hammer B, Pinkwart N (2014) How to select an example? a comparison of selection strategies in example-based learning. In: Trausan-Matu S, Boyer KE, Crosby K, Panourgia M (eds) Proceedings of the 12th international conference on intelligent tutoring systems (ITS), no. 8474 in Lecture Notes in Computer Science. Springer, Berlin, pp 340–347Google Scholar
  9. 9.
    Gross S, Mokbel B, Paassen B, Hammer B, Pinkwart N (2014) Example-based feedback provision using structured solution spaces. Int J Learn Technol 9(3):248–280. doi:10.1504/IJLT.2014.065752 CrossRefGoogle Scholar
  10. 10.
    Hammer B, Hofmann D, Schleif FM, Zhu X (2013) Learning vector quantization for (dis-)similarities. Neurocomputing. doi:10.1016/j.neucom.2013.05.054 (In Press)
  11. 11.
    Holland J, Mitrovic A, Martin B (2009) J-latte: a constraint-based tutor for javaGoogle Scholar
  12. 12.
    Koedinger KR, Anderson JR, Hadley WH, Mark MA (1997) Intelligent tutoring goes to school in the big city. Int J AI Educ 8:30–43Google Scholar
  13. 13.
    Mitrovic A, Mayo M, Suraweera P, Martin B (2001) Constraint-based tutors: a success story. In: Proceedings of the 14th international conference on industrial and engineering applications of artificial intelligence and expert systems. Springer, London, pp 931–940Google Scholar
  14. 14.
    Mokbel B, Gross S, Paassen B, Pinkwart N, Hammer B (2013) Domain-independent proximity measures in intelligent tutoring systems. In: D’Mello SK, Calvo RA, Olney A (eds) Proceedings of the 6th international conference on educational data mining (EDM). Memphis, pp 334–335Google Scholar
  15. 15.
    Mokbel B, Paassen B, Hammer B (2014) Adaptive distance measures for sequential data. In: Verleysen M (ed) 22th European symposium on artificial neural networks. Computational intelligence and machine learning. ESANN, Bruges, pp 265–270. http://i6doc.com
  16. 16.
    Mokbel B, Paassen B, Hammer B (2014) Efficient adaptation of structure metrics in prototype-based classification. In: Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova PD, Magg S, Palm G, Villa AEP (eds) Artificial neural networks and machine learning—ICANN 2014—24th international conference on artificial neural networks. Lecture notes in computer science, vol 8681. Springer, Hamburg, pp 571–578Google Scholar
  17. 17.
    Mokbel B, Paassen B, Schleif FM, Hammer B (2015) Metric learning for sequences in relational LVQ. Neurocomputing (2015). (Accepted/in press)Google Scholar
  18. 18.
    Murray T, Blessing S, Ainsworth S (eds) (2003) Authoring tools for advanced technology learning environments. Kluwer Academic Publishers, DordrechtGoogle Scholar
  19. 19.
    Ohlsson S (1994) Constraint-based student modelling. In: Greer JE, McCalla GI (eds) Student modelling: the key to individualized knowledge-based instruction. Springer, Berlin, pp 167–189CrossRefGoogle Scholar
  20. 20.
    Vanlehn K (2006) The behavior of tutoring systems. Int J Artif Intell Educ 16:227–265Google Scholar
  21. 21.
    Wittwer J, Renkl A (2010) How effective are instructional explanations in example-based learning? a meta-analytic review. Educ Psychol Rev 22(4):393–409. doi:10.1007/s10648-010-9136-5 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sebastian Gross
    • 1
  • Bassam Mokbel
    • 2
  • Barbara Hammer
    • 2
  • Niels Pinkwart
    • 1
  1. 1.Institut für InformatikHumboldt-Universität zu BerlinBerlinGermany
  2. 2.CITEC Cognitive Interaction Technology Center of ExcellenceBielefeld UniversityBielefeldGermany

Personalised recommendations