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1. How Do Students Talk About Intelligence?: An Investigation of Motivation, Self-Efficacy, and Mindsets in Computer Science

   https://doi.org/10.1145/3291279.3339413  

   Gorson, Jamie; O'Rourke, Eleanor (January 2019, Proceedings of the 2019 ACM Conference on International Computing Education Research)

   Undergraduate programs in computer science (CS) face high dropout rates, and many students struggle while learning to program. Studies show that perceived programming ability is a significant factor in students' decision to major in CS. Fortunately, psychology research shows that promoting the growth mindset, or the belief that intelligence grows with effort, can improve student persistence and performance. However, mindset interventions have been less successful in CS than in other domains. We conducted a small-scale interview study to explore how CS students talk about their intelligence, mindsets, and programming behaviors. We found that students' mindsets rarely aligned with definitions in the literature; some present mindsets that combine fixed and growth attributes, while others behave in ways that do not align with their mindsets. We also found that students frequently evaluate their self-efficacy by appraising their programming intelligence, using surprising criteria like typing speed and ease of debugging to measure ability. We conducted a survey study with 103 students to explore these self-assessment criteria further, and found that students use varying and conflicting criteria to evaluate intelligence in CS. We believe the criteria that students choose may interact with mindsets and impact their motivation and approach to programming, which could help explain the limited success of mindset interventions in CS. 

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2. Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn’t Work

   https://doi.org/10.1177/09567976211028984  

   Yeager, David S.; Carroll, Jamie M.; Buontempo, Jenny; Cimpian, Andrei; Woody, Spencer; Crosnoe, Robert; Muller, Chandra; Murray, Jared; Mhatre, Pratik; Kersting, Nicole; et al (January 2022, Psychological Science)

   A growth-mindset intervention teaches the belief that intellectual abilities can be developed. Where does the intervention work best? Prior research examined school-level moderators using data from the National Study of Learning Mindsets (NSLM), which delivered a short growth-mindset intervention during the first year of high school. In the present research, we used data from the NSLM to examine moderation by teachers’ mindsets and answer a new question: Can students independently implement their growth mindsets in virtually any classroom culture, or must students’ growth mindsets be supported by their teacher’s own growth mindsets (i.e., the mindset-plus-supportive-context hypothesis)? The present analysis (9,167 student records matched with 223 math teachers) supported the latter hypothesis. This result stood up to potentially confounding teacher factors and to a conservative Bayesian analysis. Thus, sustaining growth-mindset effects may require contextual supports that allow the proffered beliefs to take root and flourish. 

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3. Examining Engineering Students’ Shift in Mindsets Over the Course of a Semester: A Longitudinal Study

   Verdin, D.; Perez, C. L.; Krinsky, S.; Allen, E. L. (June 2023, 2023 ASEE Annual Conference & Exposition)

   Students (like all people) have elements of both growth and fixed mindsets. We studied shifts in both types of student mindsets over three one-semester courses. We found no significant change in students’ growth mindset at the beginning of the semester compared to the end of the semester. However, students’ fixed mindsets showed a statistically significant increase from the beginning of the semester to the end of the semester. Two multilevel models were used to understand why students’ fixed mindsets may have increased 1) personal sourcesmastery goal, performance goal, and internal recognition, and 2) situational sourcesclassroom goal orientations and external recognition. Students’ endorsement of a performance goal orientation, which focuses on demonstrating competence and managing others’ perception of their abilities, increased their fixed mindset views at the end of the semester. In the model focused on situational sources, we found that students’ fixed mindset increased when they perceived their classroom environment endorsed a performance-approach goal structure and by receiving external recognition. When comparing both models, students’ fixed mindset increase was largely explained by classroom environmental sources. Specifically, students’ fixed mindsets increased when they perceived that their classroom environment valued a demonstration of competence (i.e., classroom performance-approach). Being recognized as an engineer by peers and instructors also increased students’ fixed views of their abilities. Conversely, one situational source was found to decrease students’ fixed mindset views, i.e., a classroom environment that promotes mastery goals. Our study points to an apparent and crucial role engineering classroom environments have in promoting certain mindsets. The study concludes with one pedagogical strategy that may help mitigate the inadvertent promotion of a fixed mindset, e.g., a mastery learning pedagogical intervention. 

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4. Turning bugs into learning opportunities: Understanding debugging processes, perspectives, and pedagogies.

   Kafai, Y. B.; Biswas, G.; Hutchins, N.; Snyder, C.; Brennan, K.; Haduoan, P.; DesPortes, K.; Fong, M.; Flood, V.J.; Walker-van Aalst, O.; et al (January 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of theLearning Sciences (ICLS) 2020)

   Gresalfi, M.; Horn, I. (Ed.)

   The design of most learning environments focuses on supporting students in making, constructing, and putting together projects on and off the screen, with much less attention paid to the many issues—problems, bugs, or traps—that students invariably encounter along the way. In this symposium, we present different theoretical and disciplinary perspectives on understanding how learners engage in debugging applications on and off screen, examine learners’ mindsets about debugging from middle school to college students and teachers, and present pedagogical approaches that promote strategies for debugging problems, even having learners themselves design problems for others. We contend that learning to identify and fix problems—debug, troubleshoot, or get unstuck—in completing projects provides a productive space in which to explore multiple theoretical perspectives that can contribute to our understanding of learning and teaching critical strategies for dealing with challenges in learning activities and environments. 

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5. How to Teach Debugging? The Next Million-Dollar Question in Microelectronics Education

   Mehraban, Haniye; Hu, John (June 2024, ASEE)

   With the passage of the Chips and Science Act, semiconductor workforce development has become front and center for US universities. Among the many skills needed for undergraduates to enter the semiconductor industry, debugging is an important skill that is rarely taught. As the transistor count and complexity of today’s chips grow, thanks to Moore’s Law, fewer new chips can work perfectly for the first time. Hence, much engineering effort is put into debugging, a process that identifies and fixes any discrepancies between the expected and measured chip behavior. This paper first investigates the need and the economic incentives of debugging in the semiconductor industry. It was estimated that a typical semiconductor project spent 35 to 50 percent of its time in debugging. The need for silicon debugging has led to a new profession called validation engineers. Debugging has also gained the nickname of the Schedule Killer, highlighting its impact on the project schedule and the company’s bottom line. Next, the paper summarizes existing cognitive models of troubleshooting. Early models often failed to capture the role of experience, which was essential for circuit and hardware debugging. Jonassen et al. proposed a troubleshooting learning architecture that includes the contribution of past experiences. This cognitive framework has been successfully applied in computer science and physics education, leading to some of the latest pedagogy innovations, such as collaborative pair debugging. This paper also investigates multiple emotions associated with debugging, such as frustration, fear, and anxiety. These emotions may lead to disengagement and avoidance of the subjects. Debugging may also be related to other non-cognitive factors, such as mindsets. The positive effect of teaching self-theory and a growth mindset has been observed in different age groups. However, studies also found that domain-specific aptitudes were more helpful in changing student’s performance in the subject matter. The takeaway message from this paper is that a genuinely effective debugging education intervention must be holistic and domain-specific. Holistic means that the intervention should address both cognitive and affective components. Domain specificity means that any growth mindset message should be contextually situated within the subject matter materials. How to design such an intervention will be the next million-dollar question, as it not only fills the gap of collegiate debug education in microelectronics but also serves as a critical missing piece toward developing a globally competent semiconductor workforce for generations to come. 

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