Rethinking the classroom science investigation

• Published in: Journal of research in science teaching Vol. 57; no. 7; pp. 1148 - 1174
• Main Authors: Manz, Eve, Lehrer, Richard, Schauble, Leona
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2020; Wiley; Wiley Subscription Services, Inc
• Subjects: Alignment (Education); Classrooms; Concept formation; empirical activity; Evidence; experiment; Innovations; Instructional design; Investigations; Science education; Science Instruction; science investigation; Science Process Skills; science‐as‐practice; Traditions
• ISSN: 0022-4308; 1098-2736
• DOI: 10.1002/tea.21625

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.