A Framework for Parameterized Design of Rule Systems Applied to Algebra

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)


Creating a domain model (expert behavior) is a key component of every tutoring system. Whether the process is manual or semi-automatic, the construction of the rules of expert behavior requires substantial effort. Once finished, the domain model is treated as a fixed entity that does not change based on scope, sequence modifications, or student learning parameters. In this paper, we propose a framework for automatic learning and optimization of the domain model (expressed as condition-action rules) based on designer-provided learning criteria that include aspects of scope, progression sequence, efficiency of learned solutions, and working memory capacity. We present a proof-of-concept implementation based on program synthesis for the domain of linear algebra, and we evaluate this framework through preliminary illustrative scenarios of objective learning criteria.


Intelligent tutoring systems Program synthesis Automated domain modeling Artificial intelligence 


  1. 1.
    Clement, B., Roy, D., Oudeyer, P.Y., Lopes, M.: Multiarmed bandits for intelligent tutoring systems. J. Educ. Data Min. 7(2), 20–48 (2015)Google Scholar
  2. 2.
    Gulwani, S.: Example-based learning in computer-aided stem education. Commun. ACM 57(8), 70–80 (2014)CrossRefGoogle Scholar
  3. 3.
    Jarvis, M.P., Nuzzo-Jones, G., Heffernan, N.T.: Applying machine learning techniques to rule generation in intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 541–553. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Koedinger, K.R., Brunskill, E., Baker, R.S., McLaughlin, E.A., Stamper, J.: New potentials for data-driven intelligent tutoring system development and optimization. AI Mag. 34(3), 27–41 (2013)Google Scholar
  5. 5.
    Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: research issues and challenges. Cogn. Syst. Res. 10(2), 141–160 (2009)CrossRefGoogle Scholar
  6. 6.
    Lazar, T., Bratko, I.: Data-driven program synthesis for hint generation in programming tutors. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 306–311. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  7. 7.
    Li, N., Cohen, W., Koedinger, K.R., Matsuda, N.: A machine learning approach for automatic student model discovery. In: Educational Data Mining 2011 (2010)Google Scholar
  8. 8.
    Li, N., Schreiber, A.J., Cohen, W., Koedinger, K.: Efficient complex skill acquisition through representation learning. Adv. Cogn. Syst. 2, 149–166 (2012)Google Scholar
  9. 9.
    Murray, T.: Authoring intelligent tutoring systems: an analysis of the state of the art. Int. J. Artif. Intell. Educ. 10, 98–129 (1999)Google Scholar
  10. 10.
    Schmid, U., Kitzelmann, E.: Inductive rule learning on the knowledge level. Cogn. Syst. Res. 12(3), 237–248 (2011)CrossRefGoogle Scholar
  11. 11.
    Singh, R., Gulwani, S., Solar-Lezama, A.: Automated feedback generation for introductory programming assignments. In: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of WashingtonSeattleUSA

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