Abstract
During inquiry learning with virtual labs students are invited to construct mathematical models that capture key features of the underlying structures. However, students typically fail to construct complete models. In order to identify ways to support learners without restricting them, we look at the literature of Productive Failure and Invention activities (often termed PS-I, Problem Solving before Instruction). PS-I activities are designed to facilitate specific cognitive mechanisms that aid learning. This paper seeks to (1) evaluate in what ways PS-I activities compare to inquiry learning, (2) whether students in inquiry learning report similar processes to PS-I, and (3) whether these are associated with better learning. We begin by synthesizing the two approaches in order to highlight their similarities. Following, we coded self-reported post-activity reflections by 139 students who worked with two virtual labs. Students reported processes that are typical to PS-I and, out of these, prior knowledge activation was associated with constructing more complete models. Based on this, we suggest ways to support students in learning from their inquiry.
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References
Chase, C., Marks, J., Bernett, D., Aleven, V.: The Design of an exploratory learning environment to support invention. In: AIED Workshops (2015)
Chen, Z., Klahr, D.: All other things being equal: acquisition and transfer of the control of variables strategy. Child Dev. 70(5), 1098–1120 (1999). https://doi.org/10.1111/1467-8624.00081
Chowrira, S.G., Smith, K.M., Dubois, P.J., Roll, I.: DIY productive failure: boosting performance in a large undergraduate biology course. NPJ Sci. Learn. 4, 1 (2019). https://doi.org/10.1038/s41539-019-0040-6
Farrar, D.E., Glauber, R.R.: Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Stat. 49(1), 92 (1967). https://doi.org/10.2307/1937887
Ford, K.M., Bradshaw, J.M., Adams-Webber, J.R., Agnew, N.M.: Knowledge acquisition as a constructive modeling activity. Int. J. Intell. Syst. 8(1), 9–32 (1993). https://doi.org/10.1002/int.4550080103
Glaser, R., Schauble, L., Raghavan, K., Zeitz, C.: Scientific reasoning across different domains. In: De Corte, E., Linn, M.C., Mandl, H., Verschaffel, L. (eds.) Computer-Based Learning Environments and Problem Solving, pp. 345–371. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-642-77228-3_16
Hmelo-Silver, C.E., Duncan, R.G., Chinn, C.A.: Scaffolding and achievement in problem-based and inquiry learning: a response to Kirschner, Sweller, and Clark (2006). Educ. Psychol. 42(2), 99–107 (2007). https://doi.org/10.1080/00461520701263368
Holmes, N.G., Day, J., Park, A.H.K., Bonn, D.A., Roll, I.: Making the failure more productive: scaffolding the invention process to improve inquiry behaviors and outcomes in invention activities. Instr. Sci. 42(4), 523–538 (2014). https://doi.org/10.1007/s11251-013-9300-7
de Jong, T.: Scaffolds for scientific discovery learning. In: Handling Complexity in Learning Environments: Theory and Research, pp. 107–128 (2006)
de Jong, T., van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Rev. Educ. Res. 68(2), 179 (1998). https://doi.org/10.2307/1170753
Kapur, M., Bielaczyc, K.: Designing for productive failure. J. Learn. Sci. 21(1), 45–83 (2012). https://doi.org/10.1080/10508406.2011.591717
Lazonder, A.W., Hagemans, M.G., de Jong, T.: Offering and discovering domain information in simulation-based inquiry learning. Learn. Instr. 20(6), 511–520 (2010). https://doi.org/10.1016/j.learninstruc.2009.08.001
Loibl, K., Rummel, N.: The impact of guidance during problem-solving prior to instruction on students’ inventions and learning outcomes. Instr. Sci. 42(3), 305–326 (2014). https://doi.org/10.1007/s11251-013-9282-5
Loibl, K., Roll, I., Rummel, N.: Towards a theory of when and how problem solving followed by instruction supports learning. Educ. Psychol. Rev. 29(4), 693–715 (2017). https://doi.org/10.1007/s10648-016-9379-x
McHugh, M.L.: Interrater reliability: the kappa statistic. Biochem. Med. 22, 276–282 (2012). https://doi.org/10.11613/bm.2012.031
Pedaste, M.: Phases of inquiry-based learning: definitions and the inquiry cycle. Educ. Res. Rev. 14, 47–61 (2015). https://doi.org/10.1016/j.edurev.2015.02.003
Perez, S., et al.: Control of variables strategy across phases of inquiry in virtual labs. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 271–275. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_50
Roll, I., Holmes, N.G., Day, J., Bonn, D.: Evaluating metacognitive scaffolding in guided invention activities. Instr. Sci. 40(4), 691–710 (2012). https://doi.org/10.1007/s11251-012-9208-7
Schwartz, D.L., Martin, T.: Inventing to prepare for future learning: the hidden efficiency of encouraging original student production in statistics instruction. Cogn. Instr. 22(2), 129–184 (2004). https://doi.org/10.1207/s1532690xci2202_1
Schwartz, D.L., Chase, C.C., Oppezzo, M.A., Chin, D.B.: Practicing versus inventing with contrasting cases: the effects of telling first on learning and transfer. J. Educ. Psychol. 103(4), 759–775 (2011). https://doi.org/10.1037/a0025140
VanLehn, K.: Toward a theory of impasse-driven learning. In: Mandl, H., Lesgold, A. (eds.) Learning Issues for Intelligent Tutoring Systems, pp. 19–41. Springer, New York (1988). https://doi.org/10.1007/978-1-4684-6350-7_2
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Brand, C., Massey-Allard, J., Perez, S., Rummel, N., Roll, I. (2019). What Inquiry with Virtual Labs Can Learn from Productive Failure: A Theory-Driven Study of Students’ Reflections. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_6
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