Collaborative Research: Assisting and Assessing Middle School Science Learning in Formal and Informal Settings
(1) The application of HMM to identify patterns of learning choices indicated that students who received metacognitive support took more effective learning behaviors, which in turn, led to more effective learning. The work also identified several successful and unsuccessful patterns of behavior, which we can subsequently identify and respond to in future designs of the software. The details of this methodology are complex and are available upon request.
(2) A study compared the value of using the FOC versus Powerpoint slides to provide students feedback on the quality of their maps. Preliminary pre- to post-test analyses, summarized in Table 1 show that students show significant gains and large effect sizes for both conditions, but not much difference between conditions. Pending more detailed analysis, one explanation for the lack of a condition effect was that the FOC was used only a few times (and maybe not so well). A major goal of this study was to collect more data for HMM analyses of metacognitive feedback on student progress and learning (both conditions received full Betty treatments with metacognitive support). These analyses are underway.
Table 1: Summary of Pre to Post test gains for different aggregate concepts
(3) A study compared conceptual versus procedural support for learning from simulations. (Recall that one goal of the current grant proposal is to combine the TA support with science, inquiry simulations.) Preliminary results show that all students learned from the simulations. Both kinds of scaffolds helped, but conceptual scaffolds may have been slightly better. However, the two conditions did not differ much in an extended assessment test that was conducted after the study. This work sets the groundwork for creating a TA system that provides metacognitive support for using simulations.
Wagster, J., Kwong, H., Segedy, J., Biswas, G., & Schwartz, D. Bringing CBLEs into Classrooms: Experiences with the Betty's Brain System. The Eighth IEEE International Conference on Advanced Learning Technologies, pp. 252-256, Santander, Cantabria, Spain, July 2008.
Roscoe, R., Wagster, J., & Biswas, G. Using Teachable Agent Feedback to Support Effective Learning by Teaching, The Thirtieth Annual Meeting of the Cognitive Science Society, pp. 2381-2386, Washington, DC, July 2008.
Leelawong, K., & Biswas, G. Designing Learning by Teaching Agents: The Betty's Brain System, International Journal of Artificial Intelligence in Education, vol. 18, no. 3, pp. 181-208, 2008.
Jeong, H., & Biswas, G. Mining Student Behavior Models in Learning-by-Teaching Environments, First International Conference on Educational Data Mining, Montreal, R. S. Baker, T. Barnes, T., I.E. Beck, (eds.), pp. 127-136, Montreal, Quebec, Canada, June 20-21, 2008.
Jeong, H., Gupta, A., Roscoe, R., Wagster, J., Biswas, G., & Schwartz, D. Using Hidden Markov Models to Characterize Student Behavior Patterns in Computer-based Learning-by-Teaching Environments, Intelligent Tutoring Systems: 9th International Conference, Lecture Notes in Computer Science, vol. 5091, B. Woolf, et al. (eds.), Springer, Berlin, Heidelberg, pp. 614-625, 2008., Montreal, Canada.
Schwartz, D., Blair, K.P., Biswas, G. & Leelawong, K. Animations of Thought: Interactivity in the Teachable Agent Paradigm, Learning with Animation: Research and Implications for Design. R. Lowe and W. Schnotz (eds). UK: Cambrige University Press, pp. 114-140, 2007.
Tan, J., Skirvin, N., Biswas, G. & Catley, K. (2007). Providing Guidance and Opportunities for Self-Assessment and Transfer in a Simulation Environment for Discovery Learning, The twenty-ninth Annual Meeting of the Cognitive Science Society, Nashville, Tennessee, (pp. 1539).
Wagster, J., Tan, J., Wu, Y., Biswas, G. & Schwartz, D. (2007). Do Learning by Teaching Environments with Metacognitive Support Help Students Develop Better Learning Behaviors?, The twenty-ninth Annual Meeting of the Cognitive Science Society, Nashville, Tennessee, (pp. 695).
Software systems where students can practice science content they have learnt in classrooms to gain deeper understanding of content, and apply it to problem solving situations. Learn self regulation strategies while using the system.