Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.
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.
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 subjects 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 practitionergrounded insights into recent work in CT integration and provoke new ways of thinking about CT integration from researchers, practitioners, and research-practitioner partnerships.
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