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Driven by our technologically advanced workplaces and the surge in demand for proficiency in the computing disciplines, it is becoming imperative to provide computational thinking (CT) opportunities to all students. One approach for making computing accessible and relevant to learning and problem-solving in K-12 environments is to integrate it with existing Science, Technology, Engineering, and Math (STEM) curricula. However, novice student learners may face several difficulties in trying to learn STEM and computing concepts simultaneously. To address some of these difficulties, we present a systematic approach to learning STEM and CT by designing and developing domain-specific modeling languages (DSMLs) to aid students in their model building and problem-solving processes. The paper discusses a theoretical framework and the design principles for developing DSMLs, which is implemented as a four-step process. We apply the four-step process in three domains: Physics, Marine Biology, and Earth Science to demonstrate its generality, and then perform case studies to show how the DSMLs impact student learning and model building. We conclude with a discussion of our findings and then present directions for future work. [ABSTRACT FROM AUTHOR]

Synergistic learning combining computational thinking (CT) and STEM has proven to be an effective method for advancing learning and understanding in a number of STEM domains and simultaneously helping students develop important CT concepts and practices. We adopt a design-based approach to develop, evaluate, and refine our Collaborative, Computational STEM (C2STEM) learning environment. The system adopts a novel paradigm that combines visual model building with a domain-specific modeling language (DSML) to scaffold learning of high school physics using a computational modeling approach. In this paper, we discuss the design principles that guided the development of our open-ended learning environment (OELE) using a learning-by-modeling and evidence-centered approach for curriculum and assessment design. Students learn by building models that describe the motion of objects, and their learning is supported by scaffolded tasks and embedded formative assessments that introduce them to physics and CT concepts. We have also developed preparation for future learning (PFL) assessments to study students' abilities to generalize and apply CT and science concepts and practices across problem solving tasks and domains. We use mixed quantitative and qualitative analysis methods to analyze student learning during a semester-long study run in a high school physics classroom. We document some of the lessons learned from this study and discuss directions for future work. [ABSTRACT FROM AUTHOR]