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1. Integrating Parsons Puzzles within Scratch Enables Efficient Computational Thinking Learning

   Bender, Jeff ; Dziena, Alex ; Zhao, Bingpu ; Kaiser, Gail ( January 2023 , Research and practice in technology enhanced learning)

   A literature review revealed that students learning computational thinking via Scratch often require substantial teacher support. We surveyed grade 6-9 teachers to learn their perceptions of student engagement with computational thinking (CT) and how well their needs are met by existing CT learning systems. The results led us to extend the trend of balancing Scratch’s agency with structure to better serve learners and reduce burden on teachers aiming to learn and teach CT. In this paper, we review architecture and implementation strategies developed to integrate Parsons Programming Puzzles (PPPs) with Scratch, and then analyze their effects on adults, who crucially influence the education of their children. The results from our pilot study suggest PPPs catalyze CT motivation, reduce extraneous cognitive load, and increase learning efficiency without jeopardizing performance on transfer tasks. 

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2. Learning Computational Thinking Efficiently: How Parsons Programming Puzzles within Scratch Might Help

   https://doi.org/10.1145/3511861.3511869  

   Bender, Jeff ; Zhao, Bingpu ; Dziena, Alex ; Kaiser, Gail ( February 2022 , Australasian Computing Education Conference (ACE))

   Using a design thinking approach, we surveyed and interviewed grade 6-9 teachers on their experience with Scratch and Parsons Programming Puzzles (PPP). The results lead us to extend Scratch with gameful PPP functionality focused on individual computational thinking (CT) concepts. In this paper, we vary elements of PPPs presented to 624 adult learners to identify those yielding manageable cognitive load (CL), and maximum CT motivation and learning efficiency, for a general populace. Findings indicate PPPs with feedback and without distractors limit CL, those with feedback produce highest CT motivation, and those with an isolated block palette and without distractors produce highest CT learning efficiency. We analyze study data across nine conditions to offer insight to those developing PPP systems with the aim to advance equitable CT education for all. 

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3. INTEGRATING COMPUTATIONAL THINKING IN HIGH SCHOOL BIOLOGY AND CHEMISTRY CLASSES

   https://doi.org/10.21125/inted.2020.1970  

   Gotwals, R. ( January 2020 , INTED proceedings)

   Computational thinking is identified as one of the “essential skills for 21st-Century students.” [1] Studies of CT in school programs are being funded by many organizations, including the United States National Science Foundation. In this paper, we describe “lessons learned” over the first two years of a research program (PREDICTS: Principles and Resources for Educators to Infuse Computational Thinking in the Sciences) with the goal of developing knowledge of how to integrate CT into introductory high school biology and chemistry classes for all students. Using curricular modules developed by program staff, two in biology and two in chemistry, teachers piloting the program engaged students in CT with computational evidence from authentic tools in order to develop understanding of science concepts. Each module, representing about a week of instruction, addresses science ideas in the prescribed course of study for high school programs. Project researchers have collected survey data on teachers’: (1) beliefs about effective science teaching; (2) beliefs about their effectiveness as a science teacher and their students’ ability to learn science, and; (3) content preparedness. In addition, we observed module implementation, collected and analyzed student artifacts, and interviewed teachers at the conclusion of module implementation. Preliminary results indicated some challenges (access to technology, varying levels of experience among students) and cause for optimism (student and teacher engagement in CT and the computational tools used in the modules). Continuing research efforts are described in this paper, along with descriptions of the curricular modules and the use of observations and “CT check-ins” to assess student engagement in, application of, and learning of CT. 

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4. Learning Computational Thinking Efficiently with Block-based Parsons Puzzles

   Bender, Jeff ; Dziena, Alex ; Kaiser, Gail ( November 2022 , 30th International Conference on Computers in Education (ICCE))

   To investigate learning system elements and progressions that affect computational thinking (CT) learning in block-based environments, we developed a Parsons Programming Puzzle (PPP) module within Scratch with scaffolding customized via a novel Blockly grammar. By varying the presentation and types of feedback encountered between- and within-subjects in a study of 579 adults, we identified features and scaffolding strategies that yield manageable cognitive load (CL), improved CT learning efficiency, and increased motivation, for a general populace. Findings indicate: 1) PPPs with feedback induce lowest CL; 2) an isolated palette, correctness feedback, and fading correctness feedback increase learning efficiency; 3) fading scaffolding can increase CT motivation. We analyze 12 conditions to provide insight to those developing block-based PPP systems with the aim to advance equitable CT education for all. 

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5. Back to Computational Transparency: Co-designing with Teachers to Integrate Computational Thinking in Science Classrooms

   Bain, Connor ; Anton, Gabriella ; Horn, Michael ; Wilensky, Uri ( June 2020 , International Conference of the Learning Sciences)

   Integrating computational thinking (CT) in the science classroom presents the opportunity to simultaneously broaden participation in computing, enhance science content learning, and engage students in authentic scientific practice. However, there is a lot more to learn on how teachers might integrate CT activities within their existing curricula. In this work, we describe a process of co-design with researchers and teachers to develop CT-infused science curricula. Specifically, we present a case study of one veteran physics teacher whose conception of CT during a professional development institute changed over time. We use this case study to explore how CT is perceived in physics instruction, a field that has a long history of computational learning opportunities. We also discuss how a co-design process led to the development of a lens through which to identify fruitful opportunities to integrate CT activities in physics curricula which we term computational transparency–purposefully revealing the inner workings of computational tools that students already use in the classroom. 

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