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1. Board 196: A Framework to Assess Debugging Skills for Computational Thinking in Science and Engineering

   https://doi.org/10.18260/1-2--42591  

   Hylton, Derrick ; Sung, Shannon ; Ding, Xiaotong ; Van_Vleet, Mary ( June 2023 , ASEE Conferences)

   A rubric is presented to assess debugging skills for students particularly in the natural sciences and engineering. The three categories that are assessed for the cognitive processes in debugging skills are identification, isolation, and iteration. These are defined, and the characteristics of each process are listed. We discuss the method used to develop this rubric that was based on intentional errors in a programming assignment given to students in an introductory physics course. The programming in this assignment was in Python and a visual-based programming platform, called iFlow. We believe that visual-based programming will help elicit weaknesses in debugging because it removes students' familiarity with particular programming languages. Our focus on debugging skills came from a survey of students to self-identify barriers in computational work in an introductory physics course that included engineering majors. This skill was the primary self-identified barrier along with abstraction skills, which will be the focus of another work. We also present the results of this survey. The Python assignment (ntext = 9) was used to create the rubric and the iFlow assignment (ngraphic = 11) was used to test the rubric. Scoring was based on a scale of six levels in each category. Although the sample size was too small to establish rigorous scoring reliability, we discussed how the two researchers attained agreement in scoring the assignments after iterative modifications of the rubric and rescoring. For the Python assignment, the average for identification was 2.75/5, for isolation 2.30/5, and for iteration 3.33/5. For the iFlow assignment, the average for identification was 2.63/5, for isolation 2.23/5, and for iterate 3.32/5. A consistent trend from these assignments showed that students' approach to debugging is mainly to identify and iterate without a full understanding of the error (i.e., isolation). The lack of a full understanding of the error implies that students are prone to repeat the error. Thus, the important outcome of debugging is to understand the source of error by systematically investigating different parts of the computational solution. Our preliminary results led to the hypothesis that students with weak debugging skills are mainly due the isolation process. This hypothesis will be tested in a future experiment. Results from such an experiment will be significant to those who are designing intervention strategies to integrate computational thinking in science and engineering curricula. 

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2. Pedagogical Practices that Facilitate the Development of Computational Thinking Skills and Dispositions in Makerspaces

   Hadad, R. ( January 2023 , Annual meeting program American Educational Research Association)

   Focusing on computational thinking (CT) as the process to integrate computer science across subjects, and making as the activity to engage in this process, is a promising way to introduce students to computing. However, there is little guidance on what practices instructors should employ in maker activities to support CT skill/disposition development. This case study takes place in a makerspace program, using the cognitive apprenticeship conceptual framework as analytical framing to answer the question: Within makerspace activities what evidence exists regarding promising practices that support youth development of CT skills and dispositions? Findings inform our understanding of practices instructors can utilize in making activities to assist students in advancing their CT skills/dispositions: tinkering, embodiment, walkthroughs, drawing, and debugging. 

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3. A Debugging Learning Trajectory for Text-Based Programming Learners

   https://doi.org/10.1145/3456565.3460049  

   Du, Hanxiang ; Xing, Wanli ; Zhang, Yuanlin ( June 2021 , Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   Novice programming learners encounter programming errors on a regular basis. Resolving programming errors, which is also known as debugging, is not easy yet important to programming learning. Students with poor debugging ability hardly perform well on programming courses. A debugging learning trajectory which identifies learning goals, learning pathways, and instructional activities will benefit debugging learning activities development. This study aims to develop a debugging learning trajectory for text-based programming learners. This is accomplished through (1) analyzing programming errors in a logic programming learning environment and (2) examining existing literature on debugging analysis. 

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4. Integrating Computational Modeling in K-12 STEM Classrooms

   https://doi.org/10.1145/3287324.3293757  

   Biswas, Gautam ; Hutchins, Nicole ; Lédeczi, Ákos ; Grover, Shuchi ; Basu, Satabdi ( February 2019 , Proceedings of the 50th ACM Technical Symposium on Computer Science Education)

   C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org. 

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5. Demystifying computational thinking

   https://doi.org/10.1016/j_edurev.2017.09.003  

   Shute, V. J. ; Sun, C. ; Asbell-Clarke, J. ( January 2017 , Educational research review)

   This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts. This definition highlights that CT is primarily a way of thinking and acting, which can be exhibited through the use particular skills, which then can become the basis for performance-based assessments of CT skills. Based on the literature, we categorized CT into six main facets: decomposition, abstraction, algorithm design, debugging, iteration, and generalization. This paper shows examples of CT definitions, interventions, assessments, and models across a variety of disciplines, with a call for more extensive research in this area. 

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