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Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students' learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to support synergistic learning remains an important challenge. Relatively, little is known about how a student's conceptual understanding develops in such learning environments and the difficulties they face when learning with such integrated curricula. In this paper, we present a research study with CTSiM (Computational Thinking in Simulation and Modeling)-computational thinking-based learning environment for K-12 science, where students build and simulate computational models to study and gain an understanding of science processes. We investigate a set of core challenges (both computational and science domain related) that middle school students face when working with CTSiM, how these challenges evolve across different modeling activities, and the kinds of support provided by human observers that help students overcome these challenges. We identify four broad categories and 14 subcategories of challenges and show that the human-provided scaffolds help reduce the number of challenges students face over time. Finally, we discuss our plans to modify the CTSiM interfaces and embed scaffolding tools into CTSiM to help students overcome their various programming, modeling, and science-related challenges and thus gain a deeper understanding of the science concepts.

ABSTRACT:Researchers have long recognized the potential benefits of open-ended computerbased learning environments (OELEs) to help students develop self-regulated learning (SRL) behaviours. However, measuring self-regulation in these environments is a difficult task. In this paper, we present our work in developing and evaluating coherence analysis (CA), a novel approach to interpreting students' learning behaviours in OELEs. CA focuses on the learner's ability to seek out, interpret, and apply information encountered while working in the OELE. By characterizing behaviours in this manner, CA provides insight into students' open-ended problem-solving strategies as well as the extent to which they understand the nuances of their current learning task. To validate our approach, we applied CA to data from a recent classroom study with Betty's Brain. Results demonstrated relationships between CA-derived metrics, prior skill levels, task performance, and learning. Taken together, these results provide insight into students' SRL processes and suggest targets for adaptive scaffolds to support students' development of science understanding and open-ended problem-solving skills.

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