Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System

  • Ines Šarić
  • Ani GrubišićEmail author
  • Ljiljana Šerić
  • Timothy J. Robinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11528)


The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying groups of students who would benefit from the same intervention, in this paper, we study a set of 104 weekly behaviors observed for 26 students in a blended learning environment with AC-ware Tutor, an ontology-based intelligent tutoring system. Online learning behavior in AC-ware Tutor is described using 8 tracking variables: (i) the total number of content pages seen in the learning process; (ii) the total number of concepts seen in the learning process; (iii) the total content proficiency score gained online; (iv) the total time spent online; (v) the total number of student logins to AC-ware Tutor; (vi) the stereotype value after the initial test in AC-ware Tutor, (vii) the final stereotype value in the learning process, and (viii) the mean stereotype variability in the learning process. The previous measures are used in a four-step analysis process that includes the following elements: data preprocessing (Z-score normalization), dimensionality reduction (Principal component analysis), the clustering (K-means), and the analysis of a posttest performance on a content proficiency exam. By using the Euclidean distance in K-means clustering, we identified 4 distinct online learning behavior clusters, which we designate by the following names: Engaged Pre-knowers, Pre-knowers Non-finishers, Hard-workers, and Non-engagers. The posttest proficiency exam scores were compared among the aforementioned clusters using the Mann-Whitney U test.


Intelligent tutoring system Blended learning environment Clustering 



This paper is part of the Adaptive Courseware & Natural Language Tutor project that is supported by the Office of Naval Research Grant No. N00014-15-1-2789.


  1. 1.
    Lin-Siegler, X., Dweck, C.S., Cohen, G.L.: Instructional interventions that motivate classroom learning. J. Educ. Psychol. 108, 295–299 (2016)CrossRefGoogle Scholar
  2. 2.
    Mojarad, S., Essa, A., Mojarad, S., Baker, R.S.: Data-driven learner profiling based on clustering student behaviors: learning consistency, pace and effort. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds.) ITS 2018. LNCS, vol. 10858, pp. 130–139. Springer, Cham (2018). Scholar
  3. 3.
    Bouchet, F., Harley, J.M., Trevors, G.J., Azevedo, R.: Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. J. Educ. Data Min. JEDM. 5, 104–146 (2013)Google Scholar
  4. 4.
    Vellido, A., Castro, F., Nebot, À.: Clustering educational data. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R. (eds.) Handbook of Educational Data Mining, pp. 75–92. CRC Press (2010)Google Scholar
  5. 5.
    Amershi, S., Conati, C.: Combining unsupervised and supervised classification to build user models for exploratory. J. Educ. Data Min. JEDM. 1, 18–71 (2010)Google Scholar
  6. 6.
    Ferguson, R., Clow, D.: Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge - LAK 2015, pp. 51–58. ACM, Poughkeepsie (2015)Google Scholar
  7. 7.
    Rodrigo, M.M.T., Angloa, E.A., Sugaya, J.O., Baker, R.S.J.D.: Use of unsupervised clustering to characterize learner behaviors and affective states while using an intelligent tutoring system. In: International Conference on Computers in Education (2008)Google Scholar
  8. 8.
    Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proceedings of the 3rd International Conference on Learning Analytics and Knowledge - LAK 2013, pp. 170–179. ACM, New York (2013)Google Scholar
  9. 9.
    Grubišić, A.: Adaptive student’s knowledge acquisition model in e-learning systems, Ph.D. thesis, University of Zagreb, Croatia (2012)Google Scholar
  10. 10.
    Grubišić, A., et al.: Knowledge tracking variables in intelligent tutoring systems. In: Proceedings of the 9th International Conference on Computer Supported Education - CSEDU 2017, pp. 513–518. SCITEPRESS, Porto (2017)Google Scholar
  11. 11.
    Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK 2012, pp. 267–270. ACM, New York (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ines Šarić
    • 1
  • Ani Grubišić
    • 1
    Email author
  • Ljiljana Šerić
    • 2
  • Timothy J. Robinson
    • 3
  1. 1.Faculty of ScienceUniversity of SplitSplitCroatia
  2. 2.Faculty of Electrical Engineering, Mechanical Engineering and Naval ArchitectureUniversity of SplitSplitCroatia
  3. 3.Department of StatisticsUniversity of WyomingLaramieUSA

Personalised recommendations