Ambitious Science Teaching

What is Ambitious Science Teaching?


Data Puzzle activities combine authentic scientific data with the research-backed pedagogical practices of Ambitious Science Teaching, the gold standard in three-dimensional learning that supports student sense-making of natural phenomena. Ambitious Science Teaching (AST) is a pedagogical framework made up of four sets of core practices for inquiry-based learning. Why did we incorporate AST into Data Puzzle activities? In a nutshell, AST encourages students to learn science like a scientist, and we couldn't think of a more fitting strategy for engaging students with authentic scientific data. Ambitious Science Teaching was co-developed by Data Puzzle team member and University of Colorado Boulder School of Education faculty member Dr. Melissa Braaten.

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Ambitious Science Teaching was developed by a team of educators and researchers from around the country. The Ambitious Science Teaching book was authored by Mark Windschitl, Jessica Thompson, and Melissa Braaten.

Video: An Overview of Ambitious Science Teaching in Action

The Four Core Practices of Ambitious Science Teaching and Their Role in Data Puzzles

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Eliciting Students' Ideas
The Eliciting Student Ideas practice is used to start the Data Puzzle and to find out what your students already know about the science ideas you will teach. Students are introduced to a scenario through a demo, video, picture, or graph and asked to observe closely what they see or hear is going on. Student noticings/observations and explanations for the scenario are made public through discussion and/or the construction of a whole-class list. These initial student observations, explanations and connections represent resources students use to make sense of ideas presented in the classroom.

Identifying Important Science Ideas
The Identifying Important Science Ideas practice is used to connect student ideas as they relate to the opening scenario to a new scenario, one in which a featured scientist is actively engaged. Students engage with the featured scientist’s research through an interactive reading intended to give students an opportunity to make sense of science ideas in the context of both the research and the opening scenario. This practice culminates with students referencing their understandings as they relate to the reading to create an evidence-based prediction for the investigative question. Students will test their prediction with real data in the “Supporting On-Going Changes in Thinking” practice.

Supporting On-Going Changes in Thinking 
The Supporting On-Going Changes in Thinking practice is used to provide students with an opportunity to test/compare their current understandings against authentic data. Students are tasked with identifying patterns in the data and establishing rules (if present) to describe the relationships between factors represented in the graph. Then, students reflect on the patterns and relationships they’ve identified to evaluate their initial predictions for the investigative question.

Explanatory Model Construction
The Explanatory Model Construction Practice is aimed to have students finalize new understandings/science ideas as they relate to the investigative question. Students construct conceptual models (annotated sketches that include drawings, arrows, and text) to communicate ideas related to the investigative question. Before constructing their models, students should consider the following prompts: “What parts/ideas should be included? How do the parts relate to one another in the system? Do we have any data or evidence to include that supports our thinking?” Upon completion, we recommend having students share their models publicly through gallery walks or small/large group presentations.



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