Providing active learning experiences is particularly challenging when students are learning higher-order skills such the experimental process, where it is critical to provide opportunities to learn by doing, but hard to tune activities to each student’s current skill level or provide the necessary feedback. While virtual learning environments can help, the definition of inquiry in such environments covers a wide range. In building educational activities, particularly on the computer, designers implicitly or explicitly manipulate three axes: scaffolding, feedback, and constraint. In this chapter, I show how thinking deliberately about these axes can improve student learning. I argue that of those three, constraint has been underappreciated. I base this argument on data from several studies involving thousands of students at hundreds of high schools and colleges. The research shows examples of explicitly manipulating constraint in both questions and simulation-based activities, and suggests that choosing the right level of constraint can increase student learning and instructor’s ability to assess student understanding of complex skills. From these data along with theoretical considerations I suggest an Intermediate Constraint Hypothesis stating that focusing on level of constraint in student exercises and assessments is a practical and powerful way to maximize student learning.
- Teaching strategy
- Biology education
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While I alone take the blame for the intermediate constraint hypothesis and other potentially naïve ideas introduced here, I built the ideas and narrative upon research and discussions shared with many collaborators. At SimBiotic Software, thanks to Susan Maruca, Kerry Kim, and all my other colleagues who have participated in our research activities. The Understanding Experimental Design project involved many collaborators including Denise Pope, now at Univ. Massachusetts Amherst, Joel Abraham at California State Univ, Fullerton, Jody Clarke-Midura at Utah State, Daniel Wendell, Ling Hsao, and others from the lab of Eric Klopfer at Massachusetts Institute of Technology, and many people at SimBio including Susan Maruca, Kerry Kim, Jennifer Palacio, Jenna Conversano, and numerous content and software developers. We also received help from a great advisory board of Ross Nehm, Kathryn Perez, and Ryan Baker. The GraphSmarts project was led by Stephanie Gardner at Purdue University and involved several researchers in her lab, most prominently Elizabeth Suazo-Flores. Also participating were Joel Abraham, and many of the same research team at SimBio. Thanks to Isobel Buck for making the figures prettier. Thanks to the many instructors and students who agreed to allow us access to their classes and data. Finally, thanks to the organizers of this volume for inviting my contribution. This material is based upon work supported in part by the National Science Foundation under Grants No. 1227245 and 1726180. Thanks to all of those parties for the ideas, data, and funding upon which this chapter is built.
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Meir, E. (2022). Strategies for Targeting the Learning of Complex Skills Like Experimentation to Different Student Levels: The Intermediate Constraint Hypothesis. In: Pelaez, N.J., Gardner, S.M., Anderson, T.R. (eds) Trends in Teaching Experimentation in the Life Sciences. Contributions from Biology Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-98592-9_24
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Online ISBN: 978-3-030-98592-9