Exploring Students’ Affective States During Learning with External Representations

  • Beate GrawemeyerEmail author
  • Manolis Mavrikis
  • Claudia Mazziotti
  • Alice Hansen
  • Anouschka van Leeuwen
  • Nikol Rummel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331)


We conducted a user study that explored the relationship between students’ usage of multiple external representations and their affective states during fractions learning. We use the affective states of the student as a proxy indicator for the ease of reasoning with the representation. Extending existing literature that highlights the advantages of learning with multiple external representations, our results indicate that low-performing students have difficulties in reasoning with representations that do not fully accommodate the fraction as a part-whole concept. In contrast, high-performing students were at ease with a range of representations, including the ones that vaguely involved the fraction as part-whole concept.


  1. 1.
    Bazaldua, D.L., de Baker, R.S.J., Pedro, M.O.S.: Comparing expert and metric-based assessments of association rule interestingness. In: Proceedings of EDM (2014)Google Scholar
  2. 2.
    Cox, R.: Representation interpretation versus representation construction: an ILEbased study using switchERII. In: Proceedings of AIED, pp. 434–441Google Scholar
  3. 3.
    D’Mello, S.K., Lehman, B., Pekrun, R., Graesser, A.C.: Confusion can be beneficial for learning. Learn. Instr. 29(1), 153–170 (2014)CrossRefGoogle Scholar
  4. 4.
    Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., Rummel, N.: Affective learning: Improving engagement and enhancing learning with affect-aware feedback. User Model. User-Adap. Inter. - Special Issue on Impact of Learner Modeling (2017)Google Scholar
  5. 5.
    Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the R-extension package arulesViz (2011). R project moduleGoogle 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(3), 855–876 (2016)CrossRefGoogle Scholar
  7. 7.
    Rau, M.A., Aleven, V., Rummel, N.: Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. In: Proceedings of AIED, pp. 441–448 (2009)Google Scholar
  8. 8.
    Stenning, K.: Seeing Reason: Image and Language in Learning to Think. Oxford University Press, Oxford (2002)CrossRefGoogle Scholar
  9. 9.
    Suthers, D.D.: Towards a systematic study of representational guidance for collaborative learning discourse. J. Univ. Comput. Sci. 7(3), 254–277 (2001)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Beate Grawemeyer
    • 1
    Email author
  • Manolis Mavrikis
    • 2
  • Claudia Mazziotti
    • 3
  • Alice Hansen
    • 2
  • Anouschka van Leeuwen
    • 4
  • Nikol Rummel
    • 3
  1. 1.BBK Knowledge LabBirkbeck, University of LondonLondonUK
  2. 2.UCL Knowledge Lab, Institute of EducationUniversity College LondonLondonUK
  3. 3.Institute of Educational ResearchRuhr-Universität BochumBochumGermany
  4. 4.Faculty of Social and Behavioral ScienceUtrecht UniversityUtrechtNetherlands

Personalised recommendations