Topological Order Discovery via Deep Knowledge Tracing

  • Jiani ZhangEmail author
  • Irwin King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)


The goal of discovering topological order of skills is to generate a sequence of skills satisfying all prerequisite requirements. Very few previous studies have examined this task from knowledge tracing perspective. In this paper, we introduce a new task of discovering topological order of skills using students’ exercise performance and explore the utility of Deep Knowledge Tracing (DKT) to solve this task. The learned topological results can be used to improve students’ learning efficiency by providing students with personalized learning paths and predicting students’ future exercise performance. Experimental results demonstrate that our method is effective to generate reasonable topological order of skills.


Knowledge tracing Topological order Recurrent neural networks 



The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14208815 of the General Research Fund), and 2015 Microsoft Research Asia Collaborative Research Program (Project No. FY16-RES-THEME-005).


  1. 1.
    Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas,L., Sohl-Dickstein, J.: Deep knowledge tracing. In: NIPS (2015)Google Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Interact. 4(4), 253–278 (1994)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
  8. 8.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 17351780 (1997)CrossRefGoogle Scholar
  9. 9.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85117 (2015)CrossRefGoogle Scholar
  10. 10.
    Khajah, M., Lindsey, R.V., Mozer, M.C.: How deep is knowledge tracing? arXiv preprint arXiv:1604.02416 (2016)
  11. 11.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, pp. 593–605. IEEE (1989)Google Scholar
  12. 12.
    Kahn, A.B.: Topological sorting of large networks. Commun. ACM 5(11), 558–562 (1962)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications, Shenzhen Research InstituteThe Chinese University of Hong KongShenzhenChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong

Personalised recommendations