Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments

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

Abstract

Open-ended computer-based learning environments (OELEs) can be powerful learning tools in that they help students develop effective self-regulated learning (SRL) and problem solving skills. In this study, middle school students used the SimSelf OELE to build causal models to learn about climate science. We study their learning and model building approaches by calculating a suite of behavioral metrics derived using coherence analysis (CA) that are used as features on which to group students by their type of learning behavior. We also analyze changes in these metrics over time, and compare these results to results from other studies with a different OELE to see determine generalizable their findings are across different OELE systems.

Keywords

Open-ended learning environments Coherence analysis Self-regulated learning Temporal analysis 

References

  1. 1.
    Land, S., Hannafin, M., Oliver, K.: Student-centered learning environments: foundations, assumptions and design. In: Jonassen, D., Land, S. (eds.) Theoretical Foundations of Learning Environments, pp. 3–25. Routledge, New York (2012)Google Scholar
  2. 2.
    Segedy, J.R., Biswas, G., Sulcer, B.: A model-based behavior analysis approach for open-ended environments. J. Educ. Technol. Soc. 17(1), 272–282 (2014)Google Scholar
  3. 3.
    Savery, J.R., Duffy, T.M.: Problem based learning: an instructional model and its constructivist framework. Educ. Technol. 35(5), 31–38 (1995)Google Scholar
  4. 4.
    Winters, F., Greene, J., Costich, C.: Self-regulation of learning within computer-based learning environments: a critical synthesis. Educ. Psychol. Rev. 20(4), 429–444 (2008)CrossRefGoogle Scholar
  5. 5.
    Zimmerman, B., Schunk, D. (eds.): Handbook of Self-Regulation of Learning and Performance. Routledge, New York (2011)Google Scholar
  6. 6.
    Butler, D.L., Winne, P.H.: Feedback and self-regulated learning: a theoretical synthesis. Rev. Educ. Res. 65(3), 245–281 (1995)CrossRefGoogle Scholar
  7. 7.
    Segedy, J.R., Kinnebrew, J.S., Biswas, G.: Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments. J. Learn. Anal. 2(1), 13–48 (2015)Google Scholar
  8. 8.
    Sabourin, J., Shores, L., Mott, B., Lester, J.: Understanding and predicting student self-regulated learning strategies in game-based environments. Int. J. Artif. Intell. Educ. 23, 94–114 (2013)CrossRefGoogle Scholar
  9. 9.
    Baker, R.S., Ocumpaugh, J., Gowda, S.M., Kamarainen, A.M., Metcalf, S.J.: Extending log-based affect detection to a multi-user virtual environment for science. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 290–300. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Snow, E.L., Jackson, G.T., McNamara, D.S.: Emergent behaviors in computer-based learning environments: computational signals of catching up. Comput. Hum. Behav. 41, 62–70 (2014)CrossRefGoogle Scholar
  11. 11.
    Luckin, R., Hammerton, L.: Getting to know me: helping learners understand their own learning needs through metacognitive scaffolding. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 759–771. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Segedy, J.R., Kinnebrew, J.S., Biswas, G.: Coherence over time: understanding day-to-day changes in students’ open-ended problem solving behaviors. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds.) AIED 2015. LNCS, vol. 9112, pp. 449–458. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19773-9 CrossRefGoogle Scholar
  13. 13.
    Kinnebrew, J.S., Gauch, B.C., Segedy, J.R., Biswas, G.: Studying student use of self-regulated learning tools in an open-ended learning environment. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds.) AIED 2015. LNCS, vol. 9112, pp. 185–194. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  14. 14.
    Leelawong, K., Biswas, G.: Designing learning by teaching agents: the Betty’s brain system. Int. J. Artif. Intell. Educ. 18(3), 181–208 (2008)Google Scholar
  15. 15.
    Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)MATHGoogle Scholar
  16. 16.
    Segedy, J.R.: Adaptive scaffolds in open-ended computer-based learning environments. Doctoral dissertation, Vanderbilt University (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceInstitute of Software Integrated Systems, Vanderbilt UniversityNashvilleUSA

Personalised recommendations