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Computational modeling of scientific systems is a powerful approach for fostering science and computational thinking (CT) proficiencies. However, the role of programming activities for this synergistic learning remains unclear. This paper examines alternative ways to engage with computational models (CM) beyond programming. Students participated in an integrated Science, Engineering, and Computational Modeling unit through one of three distinct instructional versions: Construct a CM, Interpret-and-Evaluate a CM, and Explore-and-Evaluate a simulation. Analyzing 188 student responses to a science+CT embedded assessment task, we investigate how science proficiency and instructional versions related to pseudocode interpretation and debugging performances. We found that students in the Explore-and-Evaluate a simulation outperformed students in the programming-based versions on the CT assessment items. Additionally, science proficiency strongly predicted students’ CT performance, unlike prior programming experience. These results highlight the promise of diverse approaches for fostering CT practices with implications for STEM+C instruction and assessment design. 

K-12 Computer Science (CS) education is developing rapidly but still lacks a comprehensive measure for CS teachers’ pedagogical content knowledge (PCK). We respond to this need by describing the design of a CS-PCK instrument for ‘Algorithms and Programming’ that measures three broad constructs: (a) teachers’ understanding of standards and standards alignment, (b) teachers’ formative assessment practices, and (c) teachers’ self-efficacy for teaching and assessing CS. 

Collaboration is an important learning process. During collaborative learning, students engage in group activities where they converge on goals, solve problems and make joint decisions. To understand the process of collaboration, we focused on how behavior and interaction patterns contribute to the social-relational space of collaboration. We have designed a multilayered conceptual model for the collaboration process and an observation rubric that identifies behaviors and interactions during collaboration that serves as the foundation for machine learning models that can provide behavioral insight into the process of collaboration. This study reports results on several validation studies performed to establish a validation argument for our collaboration conceptual model and collaboration rubric. Through disconfirming evidence, interrater reliability testing, expert reviews, and focus group interviews, we found that our stratified architecture of collaboration and rubric provide valid accounts and descriptions of human behavior and interactions that can be used to substantiate the collaboration process.