Learning Analytics for Monitoring Students Participation Online: Visualizing Navigational Patterns on Learning Management System

  • Leonard K. M. PoonEmail author
  • Siu-Cheung Kong
  • Thomas S. H. Yau
  • Michael Wong
  • Man Ho Ling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10309)


With the increasing use of blended learning approaches in classroom, various kinds of technologies are incorporated to provide digital teaching and learning resources to support students. These resources are often centralized in learning management systems (LMSs), which also store valuable learning data of students. The data could assist teachers in their pedagogical decision making but they are often not well utilized. This paper proposes the use of data mining and visualization techniques as learning analytics to provide a more comprehensive overview of students’ learning online based on log data from LMSs . The focus of this study is the discovery of frequent navigational patterns by sequential pattern mining techniques and the demonstration of how presentation of patterns through hierarchical clustering and sunburst visualization could facilitate the interpretation of patterns. The data in this paper were collected from a blended statistics course for undergraduate students.


Learning analytics Blended learning Sequential pattern mining Hierarchical clustering Navigational pattern LMS Moodle 



The study was funded by Teaching Development Grant (Ref: HKIED7/T&L/12-15) under the Hong Kong University Grants Committee.


  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE (1995)Google Scholar
  2. 2.
    Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanced Learn. 4(5–6), 304–317 (2012)CrossRefGoogle Scholar
  3. 3.
    Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In: MICAI 2008, pp. 765–778 (2008)Google Scholar
  4. 4.
    Gómez-Aguilar, D.A., Hernández-García, A., García-Peñalvo, F.J., Therón, R.: Tap into visual analysis of customization of grouping of activities in elearning. Comput. Hum. Behav. 47, 60–67 (2015)CrossRefGoogle Scholar
  5. 5.
    Graf, S., Liu, T.C.: Analysis of learners’ navigational behaviour and their learning styles in an online course. J. Comput. Assist. Learn. 26(2), 116–131 (2010)CrossRefGoogle Scholar
  6. 6.
    Hirate, Y., Yamana, H.: Generalized sequential pattern mining with item intervals. J. Comput. 1(3), 51–60 (2006)CrossRefGoogle Scholar
  7. 7.
    Kang, J., Liu, M., Qu, W.: Using gameplay data to examine learning behavior patterns in a serious game. Comput. Hum. Behav. 72, 757–770 (2016)CrossRefGoogle Scholar
  8. 8.
    Kerr, D.: Visualizing changes in strategy use across attempts via state diagrams: a case study. Int. J. Comput. Games Technol. 2016, 4 (2016)CrossRefGoogle Scholar
  9. 9.
    Kong, S.C., Song, Y.: An experience of personalized learning hub initiative embedding BYOD for reflective engagement in higher education. Comput. Educ. 88, 227–240 (2015)CrossRefGoogle Scholar
  10. 10.
    Mahajan, R., Sodhi, J., Mahajan, V.: Usage patterns discovery from a web log in an Indian e-learning site: a case study. Educ. Inf. Technol. 21(1), 123–148 (2016)CrossRefGoogle Scholar
  11. 11.
    Mazza, R.: Visualization in educational environments. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D. (eds.) Handbook of Educational Data Mining, pp. 9–26 (2010)Google Scholar
  12. 12.
    Mooney, C.H., Roddick, J.F.: Sequential pattern mining — approaches and algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013)CrossRefzbMATHGoogle Scholar
  13. 13.
    Poon, L.K.M., Kong, S.C., Wong, M., Yau, T.: Mining sequential patterns of students’ access on learning management system. In: The Second International Conference on Data Mining and Big Data (DMBD 2017) (2017)Google Scholar
  14. 14.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE (2001)Google Scholar
  15. 15.
    Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern-growth methods. J. Intell. Inf. Syst. 28, 133–160 (2007)CrossRefGoogle Scholar
  16. 16.
    Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2009)CrossRefGoogle Scholar
  17. 17.
    Psaromiligkos, Y., Orfanidou, M., Kytagias, C., Zafiri, E.: Mining log data for the analysis of learners behaviour in web-based learning management systems. Oper. Res. 11(2), 187–200 (2011)Google Scholar
  18. 18.
    Reimann, P., Hesse, F., Freebody, P., Cierniak, G., Imhof, B., Wasson, B., Hansen, C., Utz, W., Vatrapu, R., Bull, S., et al.: Supporting teachers in capturing and analyzing data in the technology-rich classroom. In: Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012), pp. 33–40. International Society of the Learning Sciences (2012)Google Scholar
  19. 19.
    Rodden, K.: Applying a sunburst visualization to summarize user navigation sequences. IEEE Comput. Graph. Appl. 34(5), 36–40 (2014)CrossRefGoogle Scholar
  20. 20.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor. Newsl. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  21. 21.
    Van Barneveld, A., Arnold, K.E., Campbell, J.P.: Analytics in higher education: establishing a common language. EDUCAUSE Learn. Initiative 1(1), 1–11 (2012)Google Scholar
  22. 22.
    Zhou, M.: Using traces to investigate self-regulatory activities: a study of self-regulation and achievement goal profiles in the context of web search for academic tasks. J. Cogn. Educ. Psychol. 12(3), 287–305 (2013)CrossRefGoogle Scholar
  23. 23.
    Zhou, M., Xu, Y., Nesbit, J.C., Winne, P.H.: Sequential pattern analysis of learning logs: methodology and applications. In: Handbook of Educational Data Mining, pp. 107–121 (2010)Google Scholar
  24. 24.
    Ziebarth, S., Chounta, I.A., Hoppe, H.U.: Resource access patterns in exam preparation activities. In: Design for Teaching and Learning in a Networked World, pp. 497–502. Springer (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Leonard K. M. Poon
    • 1
    Email author
  • Siu-Cheung Kong
    • 1
  • Thomas S. H. Yau
    • 1
  • Michael Wong
    • 1
  • Man Ho Ling
    • 1
  1. 1.Department of Mathematics and Information TechnologyThe Education University of Hong KongTai PoHong Kong SAR, China

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