Personalized Multi-relational Matrix Factorization Model for Predicting Student Performance

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 384)

Abstract

Matrix factorization is the most popular approach to solving prediction problems. However, in the recent years multiple relationships amongst the entities have been exploited in order to improvise the state-of-the-art systems leading to a multi relational matrix factorization (MRMF) model. MRMF deals with factorization of multiple relationships existing between the main entities of the target relation and their metadata. A further improvement to MRMF is the Weighted Multi Relational Matrix Factorization (WMRMF) which treats the main relation for the prediction with more importance than the other relations. In this paper, we propose to enhance the prediction accuracy of the existing models by personalizing it based on student knowledge and task difficulty. We enhance the WMRMF model by incorporating the student and task bias for prediction in multi-relational models. Empirically we have shown using over five hundred thousand records from Knowledge Discovery dataset provided by Data Mining and Knowledge Discovery competition that the proposed approach attains a much higher accuracy and lower error(Root Mean Square Error and Mean Absolute Error) compared to the existing models.

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References

  1. 1.
    Krohn-Grimberghe, A., Drumond, L., Freudenthaler, C., Schmidt-Thieme, L.: Multi-relational matrix factorization using bayesian personalized ranking for social network data. In: Proceedings of thefifth ACM international conference on Web search and data mining-WSDM 2012Google Scholar
  2. 2.
    Bekele, R., Menzel, W.: A bayesian approach to predict performance of a student (bapps): a case with ethiopian students. In: Proceedings of the International Conference on Artificial Intelligence and Applications, Vienna, Austria, vol. 27, pp. 189–194 (2005)Google Scholar
  3. 3.
    Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)CrossRefGoogle Scholar
  4. 4.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. In: IEEE Computer Society Press, vol. 42, pp. 30–37 (2009). 38, 40, 41, 45Google Scholar
  6. 6.
    Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.P.: Relation prediction in multi-relational domains using matrix factorization. In: NIPS 2008 Workshop: Structured Input - Structured Output (2008). 56, 57, 60, 61, 64Google Scholar
  7. 7.
    Minaei-Bidgoli, B., Kashy, D.A., Kortemeyer, G., Punch, W.F.: Predicting student performance: an application of data mining methods with an educational web-based system. In: The 33rd IEEE Conference on Frontiers in Education (FIE 2003), pp. 13–18 (2003). 27Google Scholar
  8. 8.
    Nedungadi, P., Remya, M.S.: Predicting students’ performance on intelligent tutoring system personalized clustered BKT (PC-BKT) model. In: Frontiers in Education Conference (FIE). IEEE (2014)Google Scholar
  9. 9.
    Nedungadi, P., Smruthy, T.K.: Enhanced higher order orthogonal iteration algorithm for student performance prediction. In: International Conference on Computer and Communication Technologies (2015). AISC SpringerGoogle Scholar
  10. 10.
    Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the ACM conference on Recommender Systems (RecSys 2008), pp. 251–258. ACM, New York (2008). 40Google Scholar
  11. 11.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), KDD 2008, pp. 650–658. ACM, NewYork (2008). 56, 58, 60Google Scholar
  12. 12.
    Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., Koedinger, K.R.: Algebra 2005–06 Challenge dataset from KDD Cup 2010 Educational Data Mining Challenge (2010)Google Scholar
  13. 13.
    Thai-Nghe, N., Drumond, L., Horvath, T., Nanopoulos, A., Schmidt-Thieme, L.: Matrix and tensor factorization for predicting student performance. In: Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU 2011) (2011a)Google Scholar
  14. 14.
    Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. In: Proceedings of the ACM RecSys 2010 Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), vol. 1, pp. 2811–2819. Elsevier’s Procedia Computer Science (2010c)Google Scholar
  15. 15.
    Thai-Nghe, N., Drumond, L., Horvath, T., Krohn-Grimberghe, A., Nanopoulos, A., Schmidt-Thieme, L.: Factorization techniques for predicting student performance. In: Santos, O.C., Boticario, J.G. (eds.) Educational Recommender Systems and Technologies: Practices and Challenges (ERSAT 2011). IGI Global (2011)Google Scholar
  16. 16.
    Thai-Nghe, N., Drumond, L., Horvath, T., Schmidt-Thieme, L.: Multi-relational factorization models for predicting student performance. In: Proceedings of the KDD 2011 Workshop on Knowledge Discovery in Educational Data (KDDinED 2011) (2011c)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Amrita CREATEAmrita Vishwa VidyapeethamKollamIndia
  2. 2.Dept. of Computer ScienceAmrita Vishwa VidyapeethamKollamIndia

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