Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments
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.
KeywordsOpen-ended learning environments Coherence analysis Self-regulated learning Temporal analysis
This work has been supported by Institute of Educational Sciences CASL Grant #R305A120186 and the National Science Foundation’s IIS Award #0904387.
- 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.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.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
- 5.Zimmerman, B., Schunk, D. (eds.): Handbook of Self-Regulation of Learning and Performance. Routledge, New York (2011)Google Scholar
- 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
- 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
- 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.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.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
- 16.Segedy, J.R.: Adaptive scaffolds in open-ended computer-based learning environments. Doctoral dissertation, Vanderbilt University (2014)Google Scholar