Premium
Rethinking the classroom science investigation
Author(s) -
Manz Eve,
Lehrer Richard,
Schauble Leona
Publication year - 2020
Publication title -
journal of research in science teaching
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.067
H-Index - 131
eISSN - 1098-2736
pISSN - 0022-4308
DOI - 10.1002/tea.21625
Subject(s) - operationalization , argumentation theory , next generation science standards , science education , scientific literacy , empirical evidence , educational research , empirical research , nature of science , interpretation (philosophy) , engineering ethics , philosophy of science , epistemology , mathematics education , sociology , management science , computer science , psychology , philosophy , engineering , economics , programming language
There is now a significant research literature devoted to reconceptualizing scientific activities, such as modeling, explanation, and argumentation, to realize a vision of science‐as‐practice in classrooms. As yet, however, not all scientific practices have received equal attention. Planning and Carrying out Investigations is one of the eight scientific practices identified in the Next Generation Science Standards, and there is a long line of research from both psychological and science education traditions that addresses topics about investigation, such as the generation and interpretation of evidence. However, investigation has not been subject to concerted reconceptualization within recent research and instructional design efforts focused on science‐as‐practice. In this article, we propose a framework that centers the investigation as a key locus for constructing alignments among phenomena, data, and explanatory models and makes visible the work that scientists engage in as they develop and stabilize alignments. We argue that these alignments are currently under‐theorized and under‐utilized in instructional environments. We explore four opportunities that we argue are both accessible to students from a young age and can support conceptual innovation. These are (a) developing empirical systems, (b) getting a grip on empirical systems, (c) determining, defining and operationalizing data as “evidence,” and (d) making sense of what the results of empirical systems do and do not help us understand.