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This article provides an introduction for the special issue of the Journal of Science Education and Technology focused on computational thinking (CT) from a disciplinary perspective. The special issue connects earlier research on what K-12 students can learn and be able to do using CT with the CT skills and habits of mind needed to productively participate in professional CT-integrated STEM fields. In this context, the phrase "disciplinary perspective" simultaneously holds two meanings: it refers to and aims to make connections between established K-12 STEM subject areas (science, technology, engineering, and mathematics) and newer CT-integrated disciplines such as computational sciences. The special issue presents a framework for CT integration and includes articles that illuminate what CT looks like from a disciplinary perspective, the challenges inherent in integrating CT into K-12 STEM education, and new ways of measuring CT aligned more closely with disciplinary practices. The aim of this special issue is to offer research-based and practitioner-grounded insights into recent work in CT integration and provoke new ways of thinking about CT integration from researchers, practitioners, and research-practitioner partnerships. [ABSTRACT FROM AUTHOR]

Background and Context: Learners struggle with conceptual understanding of introductory programming concepts such as variables, expressions, and loops. Objective: We examine whether and how designed activities for conceptual exploration support preliminary engagement with and learning of foundational and often hard-to-grasp programming concepts for students in grades 6–8. Method: Drawing on principles from dynamic mathematics, we developed a suite of non-programming digital and unplugged activities embedded in a curriculum before students engage in Scratch block-based programming. We conducted empirical research in three middle school classrooms in diverse urban US schools and examined student performance through mixed qualitative and quantitative methods. Findings: Learning gains were significant and not predicted by grade, gender or prior academic preparation. Free-choice projects of students showed statistically greater (correct) use of key concepts compared to those not in the study. Implications: Our work demonstrates the promise of novel approaches such as interactive non-programming activities for deeper understanding of programming concepts. [ABSTRACT FROM AUTHOR]

The focus of this research was to create and test an introductory computer science course for middle school. Titled “Foundations for Advancing Computational Thinking” (FACT), the course aims to prepare and motivate middle school learners for future engagement with algorithmic problem solving. FACT was also piloted as a seven-week course on Stanford’s OpenEdX MOOC platform for blended in-class learning. Unique aspects of FACT include balanced pedagogical designs that address the cognitive, interpersonal, and intrapersonal aspects of “deeper learning”; a focus on pedagogical strategies for mediating and assessing for transfer from block-based to text-based programming; curricular materials for remedying misperceptions of computing; and “systems of assessments” (including formative and summative quizzes and tests, directed as well as open-ended programming assignments, and a transfer test) to get a comprehensive picture of students’ deeper computational learning. Empirical investigations, accomplished over two iterations of a design-based research effort with students (aged 11–14 years) in a public school, sought to examine student understanding of algorithmic constructs, and how well students transferred this learning from Scratch to text-based languages. Changes in student perceptions of computing as a discipline were measured. Results and mixed-method analyses revealed that students in both studies (1) achieved substantial learning gains in algorithmic thinking skills, (2) were able to transfer their learning from Scratch to a text-based programming context, and (3) achieved significant growth toward a more mature understanding of computing as a discipline. Factor analyses of prior computing experience, multivariate regression analyses, and qualitative analyses of student projects and artifact-based interviews were conducted to better understand the factors affecting learning outcomes. Prior computing experiences (as measured by a pretest) and math ability were found to be strong predictors of learning outcomes. [ABSTRACT FROM PUBLISHER]

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]