Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing

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


Bayesian Knowledge Tracing (BKT) is one of the most widely adopted student modeling methods in Intelligent Tutoring Systems (ITSs). Conventional BKT mainly leverages sequences of observations (e.g. correct, incorrect) from student-system interaction log files to infer student latent knowledge states (e.g. unlearned, learned). However, the model does not take into account the instructional interventions that generate those observations. On the other hand, we hypothesized that various types of instructional interventions can impact student’s latent states differently. Therefore, we proposed a new student model called Intervention-Bayesian Knowledge Tracing (Intervention-BKT). Our results showed the new model outperforms conventional BKT and two factor analysis based alternatives: Additive Factor Model (AFM) and Instructional Factor Model (IFM); moreover, the learned parameters of Intervention-BKT can recommend adaptive pedagogical policies.


Knowledge tracing Hidden Markov Model Input Output Hidden Markov Model Student modeling Instructional intervention 


  1. 1.
    Beck, J.: Difficulties in inferring student knowledge from observations (and why you should care). In: Educational Data Mining: Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education, pp. 21–30 (2007)Google Scholar
  2. 2.
    Baker, R.S.J., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406–415. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Beck, J.E., Chang, K., Mostow, J., Corbett, A.T.: Does help help? introducing the bayesian evaluation and assessment methodology. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 383–394. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Cen, H., Koedinger, K.R., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Chi, M., Koedinger, K. R., Gordon, G. J., Jordon, P., VanLahn, K.: Instructional factors analysis: A cognitive model for multiple instructional interventions (2011)Google Scholar
  6. 6.
    Corbett, A.T., Anderson, J.R.: Modeling the acquisition of procedural knowledge. UMUAI 4(4), 253–278 (1994)Google Scholar
  7. 7.
    Gong, Y., Beck, J.E., Heffernan, N.T.: Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 35–44. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Pardos, Z.A., Heffernan, N.T.: KT-IDEM: introducing item difficulty to the knowledge tracing model. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 243–254. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a bayesian networks implementation of knowledge tracing. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255–266. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    VanLehn, K., Jordan, P.W., Litman, D.: Developing pedagogically effective tutorial dialogue tactics: experiments and a testbed. In: SLaTE, pp. 17–20 (2007)Google Scholar
  11. 11.
    Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 171–180. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Chi, M., VanLehn, K., Litman, D., Jordan, P.: An evaluation of pedagogical tutorial tactics for a natural language tutoring system: a reinforcement learning approach. int. j. artif. intell. educ. 21(1–2), 83–113 (2011)Google Scholar
  13. 13.
    Eddy, S.R.: Hidden markov models. Curr. Opin. Struct. Biol. 6(3), 361–365 (1996)CrossRefGoogle Scholar
  14. 14.
    Marcel, S., Bernier, O., Viallet, J.E., Collobert, D.: Hand gesture recognition using input-output hidden markov models. In: fg, p. 456. IEEE, March 2000Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

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