Advertisement

Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems

  • Carlotta SchattenEmail author
  • Lars Schmidt-Thieme
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 739)

Abstract

Intelligent Tutoring Systems often profit of intelligent components, which allow to personalize the proposed contents’ characteristics and sequence. Adaptive sequencing, in particular, requires either a detrimental data collection for users or extensive domain information provided by experts of the educational area. In this paper we propose an efficient domain independent method to model student progress that can be later used to sequence tasks in large commercial systems. The developed method is based on the integration of domain independent Matrix Factorization Performance Prediction with Kalman Filters state modeling abilities. Our solution not only reduces the prediction error, but also possesses a more computationally efficient model update. Finally, we give hints about a potential interpretability of student’s state computed by Matrix Factorization, that, because of its implicit modeling, did not allow human experts, to monitor user’s knowledge acquisition.

Keywords

Progress modeling Kalman filerts Matrix factorization Performance prediction Sequencing 

Notes

Acknowledgement

This research has been co-funded by the Seventh Framework Programme of the European Commission, through project iTalk2Learn (\(\#\)318051). www.iTalk2Learn.eu. This paper is an extended version of [21] presented at the 8th International Conference on Computer Supported Education.

References

  1. 1.
    Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley.com, Chichester (2009). Wiley.com
  2. 2.
    Corbett, A., Anderson, J.: Knowledge tracing: modeling the acquisition of procedural knowledge. UMAI 4, 253–278 (1994)Google Scholar
  3. 3.
    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). doi: 10.1007/978-3-540-69132-7_44 CrossRefGoogle Scholar
  4. 4.
    Janning, R., Schatten, C., Lars, S.-T.: Feature analysis for affect recognition supporting task sequencing. In: ECTEL (2014)Google Scholar
  5. 5.
    Janning, R., Schatten, C., Schmidt-Thieme, L.: Multimodal affect recognition for adaptive intelligent tutoring systems. In: FFMI EDM (2014)Google Scholar
  6. 6.
    Janning, R., Schatten, C., Schmidt-Thieme, L.: Perceived task-difficulty recognition from log-file information for the use in adaptive intelligent tutoring systems. Int. J. Artif. Intell. Educ. 26, 855–876 (2016)CrossRefGoogle Scholar
  7. 7.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35–45 (1960)Google Scholar
  8. 8.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  9. 9.
    Li, B., Zhu, X., Li, R., Zhang, C., Xue, X., Wu, X.: Cross-domain collaborative filtering over time. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 2293–2298. AAAI Press (2011)Google Scholar
  10. 10.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender systems in technology enhanced learning. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 387–415. Springer, New York (2011). doi: 10.1007/978-0-387-85820-3_12 CrossRefGoogle Scholar
  11. 11.
    Nielsen, J.: Usability Engineering. Elsevier, San Diego (1994)zbMATHGoogle Scholar
  12. 12.
    Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a Bayesian networks implementation of knowledge tracing. In: Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255–266. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13470-8_24 CrossRefGoogle Scholar
  13. 13.
    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). doi: 10.1007/978-3-642-22362-4_21 CrossRefGoogle Scholar
  14. 14.
    Pavlik, P., Cen, H., Koedinger, K.: Performance factors analysis-a new alternative to knowledge tracing. In: AIED (2009)Google Scholar
  15. 15.
    Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: RecSys (2009)Google Scholar
  16. 16.
    Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 251–258. ACM (2008)Google Scholar
  17. 17.
    Schatten, C., Janning, R., Schmidt-Thieme, L.: Vygotsky based sequencing without domain information: a matrix factorization approach. In: Zvacek, S., Restivo, M.T., Uhomoibhi, J., Helfert, M. (eds.) CSEDU 2014. CCIS, vol. 510, pp. 35–51. Springer, Cham (2015). doi: 10.1007/978-3-319-25768-6_3 CrossRefGoogle Scholar
  18. 18.
    Schatten, C., Janning, R., Schmidt-Thieme, L.: Integration and evaluation of a machine learning sequencer in large commercial ITS. In: AAAI 2015. Springer (2015)Google Scholar
  19. 19.
    Schatten, C., Mavrikis, M., Janning, R., Schmidt-Thieme, L.: Matrix factorization feasibility for sequencing and adaptive support in ITS. In: EDM (2014)Google Scholar
  20. 20.
    Schatten, C., Schmidt-Thieme, L.: Adaptive content sequencing without domain information. In: CSEDU (2014)Google Scholar
  21. 21.
    Schatten, C., Schmidt-Thieme, L.: Student progress modeling with skills deficiency aware Kalman filters. In: CSEDU (2016)Google Scholar
  22. 22.
    Schatten, C., Wistuba, M., Schmidt-Thieme, L., Gutirrez-Santos, S.: Minimal invasive integration of learning analytics services in ITS. In: ICALT (2014)Google Scholar
  23. 23.
    Schilling, N., Wistuba, M., Drumond, L., Schmidt-Thieme, L.: Joint model choice and hyperparameter optimization with factorized multilayer perceptrons. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 72–79. IEEE (2015)Google Scholar
  24. 24.
    Thai-Nghe, N., Drumond, L., Horvath, T., Krohn-Grimberghe, A., Nanopoulos, A., Schmidt-Thieme, L.: Factorization techniques for predicting student performance. In: Educational Recommender Systems and Technologies: Practices and Challenges. IGI Global (2011)Google Scholar
  25. 25.
    Thai-Nghe, N., Drumond, L., Horvath, T., Schmidt-Thieme, L.: Using factorization machines for student modeling. In: UMAP Workshops (2012)Google Scholar
  26. 26.
    Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)CrossRefGoogle Scholar
  27. 27.
    Vinagre, J., Jorge, A.M., Gama, J.: Fast incremental matrix factorization for recommendation with positive-only feedback. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 459–470. Springer, Cham (2014). doi: 10.1007/978-3-319-08786-3_41 Google Scholar
  28. 28.
    Voss, L., Schatten, C., Schmidt-Thieme, L.: A transfer learning approach for applying matrix factorization to small its datasets. In: EDM 2015 (2015)Google Scholar
  29. 29.
    Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes. HUP, Cambridge (1978)Google Scholar
  30. 30.
    Wang, Y., Heffernan, N.T.: The student skill model. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 399–404. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-30950-2_51 CrossRefGoogle Scholar
  31. 31.
    Wistuba, M., Schilling, N., Schmidt-Thieme, L.: Sequential model-free hyperparameter tuning. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 1033–1038. IEEE (2015)Google Scholar
  32. 32.
    Xiong, L., Chen, X., Huang, T.-K., Schneider, J.G., Carbonell, J.G.: Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: SDM, vol. 10, pp. 211–222. SIAM (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Information Systems and Machine Learning LabUniversity of HildesheimHildesheimGermany

Personalised recommendations