On the Discovery of Educational Patterns using Biclustering

  • Rui HenriquesEmail author
  • Anna Carolina Finamore
  • Marco Antonio Casanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11528)


The world-wide drive for academic excellence is placing new requirements on educational data analysis, triggering the need to find less-trivial educational patterns in non-identically distributed data with noise, missing values and non-constant relations. Biclustering, the discovery of a subset of objects (whether students, teachers, researchers, courses and degrees) correlated on a subset of attributes (performance indicators), has unique properties of interest thus being positioned to satisfy the aforementioned needs. Despite its relevance, the potentialities of applying biclustering in the educational domain remain unexplored. This work proposes a structured view on how to apply biclustering to comprehensively explore educational data, with a focus on how to guarantee actionable, robust and statistically significant results. The gathered results from student performance data confirm the relevance of biclustering educational data.


Biclustering Pattern mining Educational data mining 



This work is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) under project iLU DSAIPA/DS/0111/2018 and INESC-ID pluriannual UID/CEC/50021/2019.


  1. 1.
    Antunes, C.: Acquiring background knowledge for intelligent tutoring systems. In: EDM (2008)Google Scholar
  2. 2.
    Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) Learning Analytics, pp. 61–75. Springer, New York (2014)Google Scholar
  3. 3.
    Barracosa, J., Antunes, C.: Anticipating teachers performance. In: KDD IW on Knowledge Discovery in Educational Data, pp. 77–82 (2011)Google Scholar
  4. 4.
    Buldu, A., Üçgün, K.: Data mining application on students data. Procedia - Soc. Behav. Sci. 2(2), 5251–5259 (2010)CrossRefGoogle Scholar
  5. 5.
    Chandra, E., Nandhini, K.: Knowledge mining from student data. Eur. J. Sci. Res. 47(1), 156–163 (2010)Google Scholar
  6. 6.
    Charrad, M., Ben Ahmed, M.: Simultaneous clustering: a survey. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 370–375. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Dutt, A., Aghabozrgi, S., Ismail, M.B., Mahroeian, H.: Clustering algorithms applied in educational data mining. IJ Info. Electron. Eng. 5(2), 112 (2015)Google Scholar
  8. 8.
    Dutt, A., Ismail, M.A., Herawan, T.: A systematic review on educational data mining. IEEE Access 5, 15991–16005 (2017)CrossRefGoogle Scholar
  9. 9.
    Eren, K., Deveci, M., Küçüktunç, O., Çatalyürek, Ü.: A comparative analysis of biclustering algorithms for gene expression data. Brief. Bioinf. 14(3), 279–292 (2013)CrossRefGoogle Scholar
  10. 10.
    Gottin, V., Jiménez, H., Finamore, A.C., Casanova, M.A., Furtado, A.L., Nunes, B.P.: An analysis of degree curricula through mining student records. In: ICALT, pp. 276–280. IEEE (2017)Google Scholar
  11. 11.
    Henriques, R., Antunes, C., Madeira, S.C.: A structured view on pattern mining-based biclustering. Pattern Recognit. 48(12), 3941–3958 (2015)CrossRefGoogle Scholar
  12. 12.
    Henriques, R., Ferreira, F.L., Madeira, S.C.: BicPAMS: software for biological data analysis with pattern-based biclustering. BMC Bioinform. 18(1), 82 (2017)CrossRefGoogle Scholar
  13. 13.
    Henriques, R., Madeira, S.C.: BicPAM: pattern-based biclustering for biomedical data analysis. Algorithms Mol. Biol. 9(1), 27 (2014)CrossRefGoogle Scholar
  14. 14.
    Henriques, R., Madeira, S.C.: BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge. Algorithms Mol. Biol. 11(1), 23 (2016)CrossRefGoogle Scholar
  15. 15.
    Henriques, R., Madeira, S.C.: BSig: evaluating the statistical significance of biclustering solutions. Data Min. Knowl. Discov. 32(1), 124–161 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 1(1), 24–45 (2004)CrossRefGoogle Scholar
  17. 17.
    Olaniyi, A.S., Abiola, H.M., Taofeekat Tosin, S.I., Kayode, Babatunde, A.N.: Knowledge discovery from educational database using apriori algorithm. CS&Telec. 51(1) (2017)Google Scholar
  18. 18.
    Trivedi, S., Pardos, Z., Sárkozy, G., Heffernan, N.: Co-clustering by bipartite spectral graph partitioning for out-of-tutor prediction. In: Proceedings of the 5th International Conference on Educational Data Mining, Chania, Greece, 19–21 June 2012, pp. 33–40 (2012)Google Scholar
  19. 19.
    Trivedi, S., Pardos, Z., Sárkozy, G., Heffernan, N.: Spectral clustering in educational data mining. In: EDM (2010)Google Scholar
  20. 20.
    Vale, A., Madeira, S.C., Antunes, C.: Mining coherent evolution patterns in education through biclustering. In: Educational Data Mining (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INESC-ID and Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.PUC-RioRio de JaneiroBrazil

Personalised recommendations