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Cluster based feedback provision strategies in intelligent tutoring systems

Published: 14 June 2012 Publication History

Abstract

In this paper, we propose the use of machine learning techniques operating on sets of student solutions in order to automatically infer structure on these spaces. Feedback opportunities can then be derived from the clustered data. A validation of the approach based on data from a programming course confirmed the feasibility of the approach.

References

[1]
Ahtiainen, A., Surakka, S., Rahikainen, M.: Plaggie: Gnu-licensed source code plagiarism detection engine for java exercises. In: Proceedings of the 6th Baltic Sea Conference on Computing Education Research: Koli Calling 2006, Baltic Sea 2006, pp. 141-142. ACM, New York (2006)
[2]
Frey, B. J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972-976 (2007)
[3]
Goodlad, S., Hirst, B.: Peer tutoring: a guide to learning by teaching. Kogan Page (1989)
[4]
Lynch, C., Ashley, K. D., Pinkwart, N., Aleven, V.: Concepts, structures, and goals: Redefining ill-definedness. International Journal of Artificial Intelligence in Education 19(3), 253-266 (2010)

Cited By

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  • (2019)Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual WorldProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303832(130-134)Online publication date: 4-Mar-2019
  • (2016)Semi-supervised verified feedback generationProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950363(739-750)Online publication date: 1-Nov-2016
  • (2014)Did you let a robot check my homework?ACM Inroads10.1145/2614512.26145225:2(33-35)Online publication date: 1-Jun-2014
  • Show More Cited By

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Published In

cover image Guide Proceedings
ITS'12: Proceedings of the 11th international conference on Intelligent Tutoring Systems
June 2012
721 pages
ISBN:9783642309496
  • Editors:
  • Stefano A. Cerri,
  • William J. Clancey,
  • Giorgos Papadourakis,
  • Kitty Panourgia

Sponsors

  • NSF: National Science Foundation
  • IAIED: International AI-ED Society
  • JSAI: Japanese Society for Artifical Intelligence
  • The Association for the Advancement of Artificial Intelligence (AAAI)
  • JSISE: Japanese Society for Information and Systems in Education

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 June 2012

Author Tags

  1. ill-defined domains
  2. intelligent tutoring systems
  3. machine learning

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Cited By

View all
  • (2019)Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual WorldProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303832(130-134)Online publication date: 4-Mar-2019
  • (2016)Semi-supervised verified feedback generationProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950363(739-750)Online publication date: 1-Nov-2016
  • (2014)Did you let a robot check my homework?ACM Inroads10.1145/2614512.26145225:2(33-35)Online publication date: 1-Jun-2014
  • (2014)CodewebsProceedings of the 23rd international conference on World wide web10.1145/2566486.2568023(491-502)Online publication date: 7-Apr-2014
  • (2012)How to quantitatively compare data dissimilarities for unsupervised machine learning?Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition10.1007/978-3-642-33212-8_1(1-13)Online publication date: 17-Sep-2012

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