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Tools for Science Inquiry Learning: Tool Affordances, Experimentation Strategies, and Conceptual Understanding

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Abstract

Manipulative environments play a fundamental role in inquiry-based science learning, yet how they impact learning is not fully understood. In a series of two studies, we develop the argument that manipulative environments (MEs) influence the kind of inquiry behaviors students engage in, and that this influence realizes through the affordances of MEs, independent of whether they are physical or virtual. In particular, we examine how MEs shape college students’ experimentation strategies and conceptual understanding. In study 1, students engaged in two consecutive inquiry tasks, first on mass and spring systems and then on electric circuits. They either used virtual or physical MEs. We found that the use of experimentation strategies was strongly related to conceptual understanding across tasks, but that students engaged differently in those strategies depending on what ME they used. More students engaged in productive strategies using the virtual ME for electric circuits, and vice versa using the physical ME for mass and spring systems. In study 2, we isolated the affordance of measurement uncertainty by comparing two versions of the same virtual ME for electric circuits—one with and one without noise—and found that the conditions differed in terms of productive experimentation strategies. These findings indicate that measures of inquiry processes may resolve apparent ambiguities and inconsistencies between studies on MEs that are based on learning outcomes alone.

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Notes

  1. Alternative explanations are that combinations of ME provide multiple representations, which leads to increased learning benefits, irrespective of affordances. The current state of research cannot exclude these alternative explanations (Lazonder and Ehrenhard 2014).

  2. In Lazonder and Ehrenhard (2014), the researcher ensured that each experiments students designed was unconfounded.

  3. The regression equation was marginally significant, F(4, 29)= 2.2, p = .09, with an adjusted R 2=.13.

  4. M is the mean and SD the standard deviation of the sample

  5. The regression equation was significant, F(3, 61)= 8.4, p<.001, with an adjusted R 2=.26.

  6. The average silhouette score was .37. Nevertheless, the clusters are reasonably coherent in terms of experimentation strategies.

  7. Specifically, we controlled for pre-test scores, medium, their interaction, and the number of circuits built.

  8. The correlations are based on a total of 32 participants, excluding participants from POE and participants with perfect pre-test scores.

  9. We will discuss why POE did not show any effect in the section on limitations of the studies.

  10. Random noise was designed as Gaussian noise centered on the theoretically correct current value, and spanning a maximum range of about 10% of the current value, up to a maximum of about 0.6Amp.

  11. Copyright 2017 Qualtrics. http://www.qualtrics.com.

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Authors and Affiliations

  1. Graduate School of Education, Stanford University, Stanford, CA, 94305, USA

    Engin Bumbacher, Shima Salehi, Carl Wieman & Paulo Blikstein

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  1. Engin Bumbacher
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  2. Shima Salehi
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Correspondence to Engin Bumbacher.

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Bumbacher, E., Salehi, S., Wieman, C. et al. Tools for Science Inquiry Learning: Tool Affordances, Experimentation Strategies, and Conceptual Understanding. J Sci Educ Technol 27, 215–235 (2018). https://doi.org/10.1007/s10956-017-9719-8

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