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1. Investigating the Relationship Between Programming Experience and Debugging Behaviors in an Introductory Computer Science Course

   Pinto, Juan D.; Liu, Qianhui; Paquette, Luc; Zhang, Yingbin; Fan, Aysa X. (October 2023, Springer Nature Switzerland)

   Debugging is a challenging task for novice programmers in computer science courses and calls for specific investigation and support. Although the debugging process has been explored with qualitative methods and log data analyses, the detailed code changes that describe the evolution of debugging behaviors as students gain more experience remain relatively unexplored. In this study, we elicited “constituents” of the debugging process based on experts’ interpretation of students’ debugging behaviors in an introductory computer science (CS1) course. Epistemic Network Analysis (ENA) was used to study episodes where students fixed syntax/checkstyle errors or test errors. We compared epistemic networks between students with different prior programming experience and investigated how the networks evolved as students gained more experience throughout the semester. The ENA revealed that novices and experienced students put different emphasis on fixing checkstyle or syntax errors and highlighted interesting constituent co-occurrences that we investigated through further descriptive and statistical analyses. 

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2. Connecting affordances of physical and virtual laboratory modes to engineering epistemic practices

   https://doi.org/10.1007/s12528-024-09403-7  

   Gavitte, Samuel B; Koretsky, Milo D; Nason, Jeffrey A (June 2024, Journal of Computing in Higher Education)

   Laboratory activities are central to undergraduate student learning in science and engineering. With advancements in computer technology, many laboratory activities have shifted from providing students experiments in a physical mode to providing them in a virtual mode. Further, physical and virtual modes can be combined to address a single topic, as the modes have complementary affordances. In this paper, we report on the design and implementation of a physical and virtual laboratory on the topic of jar testing, a common process for drinking water treatment. The assignment for each laboratory mode was designed to leverage the mode’s affordances. Correspondingly, we hypothesized each would elicit a different subset of engineering epistemic practices. In a naturalistic, qualitative study design based on laboratory mode (physical or virtual) and laboratory order (virtual first or physical first), we collected process, product, and reflection data of students’ laboratory activity. Taking an orientation that learning is participation in valued disciplinary practice, data were coded and used to characterize how students engaged with each laboratory mode. Results showed that the virtual laboratory elicited more conceptual epistemic practices and the physical laboratory more material epistemic practices, aligning with the affordances of each mode. When students completed the laboratory in the virtual mode first, students demonstrated greater engagement in epistemic practices and more positive perceptions of their learning experience in the virtual mode than when they completed the physical mode first. In contrast, engagement in the physical mode was mostly unaffected by the laboratory order. 

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3. Embodied mixed reality with passive haptics in STEM education: randomized control study with chemistry titration

   https://doi.org/10.3389/frvir.2023.1047833  

   Johnson-Glenberg, Mina C.; Yu, Christine S.; Liu, Frank; Amador, Charles; Bao, Yueming; Yu, Shufan; LiKamWa, Robert (July 2023, Frontiers in Virtual Reality)

   Researchers, educators, and multimedia designers need to better understand how mixing physical tangible objects with virtual experiences affects learning and science identity. In this novel study, a 3D-printed tangible that is an accurate facsimile of the sort of expensive glassware that chemists use in real laboratories is tethered to a laptop with a digitized lesson. Interactive educational content is increasingly being placed online, it is important to understand the educational boundary conditions associated with passive haptics and 3D-printed manipulables. Cost-effective printed objects would be particularly welcome in rural and low Socio-Economic (SES) classrooms. A Mixed Reality (MR) experience was created that used a physical 3D-printed haptic burette to control a computer-based chemistry titration experiment. This randomized control trial study with 136 college students had two conditions: 1) low-embodied control (using keyboard arrows), and 2) high-embodied experimental (physically turning a valve/stopcock on the 3D-printed burette). Although both groups displayed similar significant gains on the declarative knowledge test, deeper analyses revealed nuanced Aptitude by Treatment Interactions (ATIs). These interactionsfavored the high-embodied experimental group that used the MR devicefor both titration-specific posttest knowledge questions and for science efficacy and science identity. Those students with higher prior science knowledge displayed higher titration knowledge scores after using the experimental 3D-printed haptic device. A multi-modal linguistic and gesture analysis revealed that during recall the experimental participants used the stopcock-turning gesture significantly more often, and their recalls created a significantly different Epistemic Network Analysis (ENA). ENA is a type of 2D projection of the recall data, stronger connections were seen in the high embodied group mainly centering on the key hand-turning gesture. Instructors and designers should consider the multi-modal and multi-dimensional nature of the user interface, and how the addition of another sensory-based learning signal (haptics) might differentially affect lower prior knowledge students. One hypothesis is that haptically manipulating novel devices during learning may create more cognitive load. For low prior knowledge students, it may be advantageous for them to begin learning content on a more ubiquitous interface (e.g., keyboard) before moving them to more novel, multi-modal MR devices/interfaces. 

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4. A study of predictors for debugging quality among preservice, early childhood teachers

   Belland, B. R.; Kim, C.; Lee, E.; Baabdullah, A.; Zhang, A. Y. (April 2020, Annual meeting program American Educational Research Association)

   While there has been much progress in the meaningful integration of computer science within K-12 classrooms, there is a need to promote more equitable participation and to improve teacher preparation. One area in which this is needed is in early childhood education. In this paper, we investigated predictors of debugging quality among 19 pre-service early childhood teachers as they engaged in debugging supported by scaffolding. Bayesian regression indicated the following variables predicted debugging quality: debugging process score, English domain identification, performance approach goals, and sentiment analysis scores applied to what students wrote in response to scaffold prompts. 

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5. An Analysis of Middle Grade Teachers' Debugging Pedagogical Content Knowledge

   https://doi.org/10.1145/3502718.3524770  

   Tsan, Jennifer; Weintrop, David; Franklin, Diana (July 2022, The 27th ACM Conference on Innovation and Technology in Computer Science Education)

   There is an increasing need for knowledgeable K-12 computer science (CS) teachers. It is necessary to inform teachers how to debug and help their students debug programs. Research has shown that debugging is difficult for novices because the process requires different skills from creating programs and instructing students how to debug can help them acquire these skills. To this end, we developed a CS professional development for middle grade teachers (grades 5th-8th/ages 10-13) that includes lessons on debugging. The teachers completed debugging activities that involved finding bugs in Scratch programs and explaining how they would help their students in debugging. We qualitatively analyzed their responses and found that teachers successfully identified the problem but they struggled to locate it in the code. In considering how they would help students who had such a bug, the teachers often focused on helping the student find a solution for the bug rather than on identifying the problem or its source. Finally, teachers’ ability to identify bugs and the pedagogical strategies to engage students in this process differed based on CS teaching experience and prior CS knowledge. This work contributes to our understanding of teachers’ debugging abilities and advances our knowledge on how to support teachers in teaching their students how to debug their programs. 

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