TY - JOUR
T1 - Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning
AU - Krist, Christina
AU - Schwarz, Christina V.
AU - Reiser, Brian J.
N1 - Funding Information:
This work was supported by the Division of Research on Learning in Formal and Informal Settings of the National Science Foundation under Grant No. DRL-1020316 to Northwestern University. The opinions expressed herein are our own and not necessarily those of the Foundation.
Funding Information:
This work was supported by the Division of Research on Learning in Formal and Informal Settings of the National Science Foundation under Grant No. DRL-1020316 to Northwestern University. The opinions expressed herein are our own and not necessarily those of the Foundation. This article comes out of the research of the Scientific Practices group at Northwestern University, Michigan State University, University of Wisconsin?Madison, and Wright State University. We are indebted to the nonauthoring members of this group (Leema Berland, Lisa Kenyon, Abraham Lo, Li Ke, May Lee, Joshua Rosenberg, and Jeanette Meager) for their partnership and ongoing conversations that shaped this article and to Keith Esch and Sean Smith for their fruitful feedback and evaluation relevant to the ideas and data presented here. Many thanks to Kelsey Edwards and Dan Voss for their contributions in developing the coding rubrics and to Matt Yan, Gloria Llenos, Lauren Dennis, Caroline Sir, and Kai Kasprick for their efforts in processing and coding the student assessment data. We are grateful to the participating teachers who administered our assessments and to the participating students for sharing their science thinking with us. We also appreciate feedback about this framework from Dr. Jon Stoltzfus, Dr. Melanie Cooper, and her research group. This article was greatly improved by comments from reviewer Rosemary Russ and two additional anonymous reviewers and from editors Douglas Clark and Susan Yoon.
Publisher Copyright:
© 2019, Copyright © 2019 Taylor & Francis Group, LLC.
PY - 2019/3/15
Y1 - 2019/3/15
N2 - Mechanistic reasoning, or reasoning systematically through underlying factors and relationships that give rise to phenomena, is a powerful thinking strategy that allows one to explain and make predictions about phenomena. This article synthesizes and builds on existing frameworks to identify essential characteristics of students’ mechanistic reasoning across scientific content areas. We argue that these characteristics can be represented as epistemic heuristics, or ideas about how to direct one’s intellectual work, that implicitly guide mechanistic reasoning. We use this framework to characterize middle school students’ written explanatory accounts of two phenomena in different science content areas using these heuristics. We demonstrate evidence of heuristics in students’ accounts and show that the use of the heuristics was related to but distinct from science content knowledge. We describe how the heuristics allowed us to characterize and compare the mechanistic sophistication of account construction across science content areas. This framework captures elements of a crosscutting practical epistemology that may support students in directing the construction of mechanistic accounts across content areas over time, and it allows us to characterize that progress.
AB - Mechanistic reasoning, or reasoning systematically through underlying factors and relationships that give rise to phenomena, is a powerful thinking strategy that allows one to explain and make predictions about phenomena. This article synthesizes and builds on existing frameworks to identify essential characteristics of students’ mechanistic reasoning across scientific content areas. We argue that these characteristics can be represented as epistemic heuristics, or ideas about how to direct one’s intellectual work, that implicitly guide mechanistic reasoning. We use this framework to characterize middle school students’ written explanatory accounts of two phenomena in different science content areas using these heuristics. We demonstrate evidence of heuristics in students’ accounts and show that the use of the heuristics was related to but distinct from science content knowledge. We describe how the heuristics allowed us to characterize and compare the mechanistic sophistication of account construction across science content areas. This framework captures elements of a crosscutting practical epistemology that may support students in directing the construction of mechanistic accounts across content areas over time, and it allows us to characterize that progress.
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U2 - 10.1080/10508406.2018.1510404
DO - 10.1080/10508406.2018.1510404
M3 - Article
AN - SCOPUS:85057324089
SN - 1050-8406
VL - 28
SP - 160
EP - 205
JO - Journal of the Learning Sciences
JF - Journal of the Learning Sciences
IS - 2
ER -