Using Eye-Tracking to Determine the Impact of Prior Knowledge on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)

Abstract

Recent research on self-regulated learning (SRL) includes multi-channel data, such as eye-tracking, to measure the deployment of key cognitive and metacognitive SRL processes during learning with adaptive hypermedia systems. In this study we investigated how 147 college students’ proportional learning gains (PLGs), proportion of time spent on areas of interest (AOIs), and frequency of fixations on AOI-pairs, differed based on their prior knowledge of the overall science content, and of specific content related to sub-goals, as they learned with MetaTutor. Results indicated that students with low prior sub-goal knowledge had significantly higher PLGs, and spent a significantly larger proportion of time fixating on diagrams compared to students with high prior sub-goal knowledge. In addition, students with low prior knowledge had significantly higher frequencies of fixations on some AOI-pairs, compared to students with high prior knowledge. The results have implications for using eye-tracking (and other process data) to understand the behavioral patterns associated with underlying cognitive and metacognitive SRL processes and provide real-time adaptive instruction, to ensure effective learning.

Keywords

Metacognition Self-regulated learning Eye tracking Prior knowledge Adaptive hypermedia-learning environments Process data 

References

  1. 1.
    Azevedo, R., et al.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 427–449. Springer, Amsterdam (2013)CrossRefGoogle Scholar
  2. 2.
    Graesser, A.C.: Evolution of advanced learning technologies in the 21st century. Theor. Into Pract. 52, 93–101 (2013)CrossRefGoogle Scholar
  3. 3.
    Lester, J.C., et al.: Supporting self-regulated science learning in narrative-centered learning environments. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 471–483. Springer, Amsterdam (2013)CrossRefGoogle Scholar
  4. 4.
    Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D.H., Zimmerman, B.J. (eds.) Motivation and Self-Regulated Learning: Theory, Research and Applications, pp. 298–314. Erlbaum, New York (2008)Google Scholar
  5. 5.
    Conati, C., et al.: Understanding attention to adaptive hints in educational games: an eye-tracking study. Int. J. Artif. Intell. Educ. 23, 136–161 (2013)CrossRefGoogle Scholar
  6. 6.
    D’Mello, S.K., et al.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Hum. Comput. Stud. 70, 377–398 (2012)CrossRefGoogle Scholar
  7. 7.
    Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J.M., Azevedo, R., Bouchet, F.: Inferring learning from gaze data during interaction with an environment to support self-regulated learning. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 229–238. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Jaques, N., Conati, C., Harley, J.M., Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 29–38. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  9. 9.
    Taub, M., et al.: Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Comput. Hum. Behav. 39, 356–367 (2014)CrossRefGoogle Scholar
  10. 10.
    Trevors, G., et al.: Note-taking within MetaTutor: interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educ. Technol. Res. Dev. 62, 507–528 (2014)CrossRefGoogle Scholar
  11. 11.
    Pekrun, R., et al.: Measuring emotions in students’ learning and performance: the achievement emotions questionnaire (AEQ). Contemp. Educ. Psychol. 36, 36–48 (2011)CrossRefGoogle Scholar
  12. 12.
    SMI Experiment Center 3.4.165 [Apparatus and Software]. SensoMotoric Instruments, Boston, Massachusetts, USA (2014)Google Scholar
  13. 13.
    Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: Duchowski, A.T. (ed.) Eye-Tracking Research and Application, pp. 71–78. ACM Press, Palm Beach Gardens (2000)Google Scholar
  14. 14.
    Mayer, R.E. (ed.): The Cambridge Handbook of Multimedia Learning, 2nd edn. Cambridge University Press, New York (2014)Google Scholar
  15. 15.
    Calvo, R.A., et al. (eds.): The Oxford Handbook of Affective Computing. Oxford University Press, New York (2015)Google Scholar
  16. 16.
    Azevedo, R.: Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Educ. Psychol. 50, 84–94 (2015)CrossRefGoogle Scholar
  17. 17.
    Baker, R.S.: Educational data mining: an advance for intelligent systems in education. IEEE Intell. Syst. 29, 78–82 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Psychology, Laboratory for the Study of Metacognition and Advanced Learning TechnologiesNorth Carolina State UniversityRaleighUSA

Personalised recommendations