Abstract
Background and Context: The Inclusive Assessment of Computational Thinking (CT) designed for accessibility and learner variability was studied in over 50 classes in US schools (grades 3-8).
 Objective: The validation studies of IACT sampled thousands of students to establish IACT’s construct and concurrent validity as well as test-retest reliability.
 Method: IACT items for each CT practice were correlated to examine construct validity. The CT pre-measures were correlated with post-measures to examine test-retest reliability. The CT post-measures were correlated with external measures to examine concurrent validity.
 Findings: IACT studies showed moderate evidence of test-retest reliability and concurrent validity and low to moderate evidence of construct validity for an aggregated measure of CT, but weaker validity and reliability evidence for individual CT practices. These findings were similar for students with and without IEPs or 504s.
 Implications: IACT is the first CT tool for grades 3-8 that has been validated in a large-scale study among students with and without IEPs or 504s. While improvements are needed for stronger validity, it is a promising start.
Highlights
Computational Thinking (CT) has been attracting increased attention over the past decade in K–12 education, prompting a call for new models of pedagogy, instruction, and assessment (Shute, Sun, & Asbell-Clarke, 2017; CSTA, 2017; National Academy of Sciences, 2010)
The online logic puzzles that make up IACT were designed to serve as external pre/post assessments in a national, game-based learning study of over 50 upper elementary- and middle-school classes during a study of implicit CT practices demonstrated in Zoombinis gameplay in the 2017-18 school year (Asbell-Clarke, et al, 2020)
2.2 Description of IACT Items To measure foundational CT in grades 3–8, we developed a set of interactive logic puzzles focusing on four fundamental CT practices: Problem Decomposition, Pattern Recognition, Abstraction, and Algorithm Design
Summary
Computational Thinking (CT) has been attracting increased attention over the past decade in K–12 education, prompting a call for new models of pedagogy, instruction, and assessment (Shute, Sun, & Asbell-Clarke, 2017; CSTA, 2017; National Academy of Sciences, 2010). In designing a middle-school curriculum called Foundations for Advancing Computational Thinking (FACT), Grover, Cooper and Pea (2014) used pedagogical strategies to support transfer from block-based to text-based programming, along with formative and summative assessments (including quizzes and tests as well as open-ended programming assignments) related to the acquisition of computational thinking skills. Their findings show that students ages 11–14 using the FACT curriculum experience improved algorithmic learning, understanding of computing, and transfer of skills from the introductory programming environment, Scratch, to a text-based programming context. Building on this research, Lundh, Grover, Jackiw, and Basu (2018) suggest a framing of Variables, Expressions, Loops, and Algorithms (VELA) to prepare young learners for CT
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