The purpose of this study was to investigate how computational modeling promotes systems thinking for English Learners (ELs) in fifth-grade science instruction. Individual student interviews were conducted with nine ELs about computational models of landfill bottle systems they had developed as part of a physical science unit. We found evidence of student engagement in four systems thinking practices. Students used data produced by their models to investigate the landfill bottle system as a whole (Practice 1). Students identified agents and their relationships in the system (Practice 2). Students thought in levels, shuttling between the agent and aggregate levels (Practice 3). However, while students could think in levels to develop their models, they struggled to engage in this practice when presented with novel scenarios (e.g., open vs. closed system). Finally, students communicated information about the system using multiple modalities and less-than-perfect English (Practice 4). Overall, these findings suggest that integrating computational modeling into standards-aligned science instruction can provide a rich context for fostering systems thinking among linguistically diverse elementary students.
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Using computational thinking for data practices in high school science.
When conducting a science investigation in biology, chemistry, physics or earth science, students often need to obtain, organize, clean, and analyze the data in order to draw conclusions about a particular phenomenon. It can be difficult to develop lesson plans that provide detailed or explicit instructions about what students need to think about and do to develop a firm conceptual understanding, particularly regarding data analysis. This article demonstrates how computational thinking principles and data practices can be merged to develop more effective science investigation lesson plans. The data practices of creating, collecting, manipulating, visualizing, and analyzing data are merged with the computational thinking practices of decomposition, pattern recognition, abstraction, algorithmic thinking, and automation to create questions for teachers and students that help them think through the underlying processes that happen with data during high school science investigations. The questions can either be used to elaborate lesson plans or embedded into lesson plans for students to consider how they are using computational thinking during their data practices in science.
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- Award ID(s):
- 1842090
- NSF-PAR ID:
- 10171527
- Date Published:
- Journal Name:
- The science teacher
- Volume:
- 87
- Issue:
- 6
- ISSN:
- 1943-4871
- Page Range / eLocation ID:
- 30-36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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