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Programming can be an emotional experience, particularly for undergraduate students who are new to computer science. While researchers have interviewed novice programmers about their emotional experiences, it can be difficult to pinpoint the specific emotions that occur during a programming session. In this paper, we argue that electrodermal activity (EDA) sensors, which measure the physiological changes that are indicative of an emotional reaction, can provide a valuable new data source to help study student experiences. We conducted a study with 14 undergraduate students in which we collected EDA data while they worked on a programming problem. This data was then used to cue the participants’ recollections of their emotions during a retrospective interview about the programming experience. Using this methodology, we identified 21 distinct events that triggered student emotions, such as feeling anxiety due to a lack of perceived progress on the problem. We also identified common patterns in EDA data across multiple participants, such as a drop in their physiological reaction after developing a plan, corresponding with a calmer emotional state. These findings provide new information about how students experience programming that can inform research and practice, and also contribute initial evidence of the value of EDA data in supporting studies of emotions while programming. 

University introductory computer science courses (CS1) present many challenges. Students enter CS1 with varying backgrounds and many are evaluating their potential for success in the major. Students often negatively self-assess in response to natural programming moments, such as getting a syntax error, but we have a limited understanding of the mechanisms that drive these self-assessments. In this paper, we study the differences in student assessments of themselves and their assessments of others in response to particular programming moments. We analyze survey data from 214 CS1 students, finding that many have a self-critical bias, evaluating themselves more harshly than others. We also found that women have a stronger self-critical bias, and that students tend to be more self-critical when the other is female. These insights can help us reduce the impact of negative self-assessments on student experiences. 

Undergraduate computer science (CS) programs often suffer from high dropout rates. Recent research suggests that self-efficacy -- an individual's belief in their ability to complete a task -- can influence whether students decide to persist in CS. Studies show that students' self-assessments affect their self-efficacy in many domains, and in CS, researchers have found that students frequently assess their programming ability based on their expectations about the programming process. However, we know little about the specific programming experiences that prompt the negative self-assessments that lead to lower self-efficacy. In this paper, we present findings from a survey study with 214 CS1 students from three universities. We used vignette-style questions to describe thirteen programming moments which may prompt negative self-assessments, such as getting syntax errors and spending time planning. We found that many students across all three universities reported that they negatively self-assess at each of the thirteen moments, despite the differences in curriculum and population. Furthermore, those who report more frequent negative self-assessments tend to have lower self-efficacy. Finally, our findings suggest that students' perceptions of professional programming practice may influence their expectations and negative self-assessments. By reducing the frequency that students self-assess negatively while programming, we may be able to improve self-efficacy and decrease dropout rates in CS. 

Student perceptions of programming can impact their experiences in introductory computer science (CS) courses. For example, some students negatively assess their own ability in response to moments that are natural parts of expert practice, such as using online resources or getting syntax errors. Systems that automatically detect these moments from interaction log data could help us study these moments and intervene when the occur. However, while researchers have analyzed programming log data, few systems detect pre-defined moments, particularly those based on student perceptions. We contribute a new approach and system for detecting programming moments that students perceive as important from interaction log data. We conducted retrospective interviews with 41 CS students in which they identified moments that can prompt negative self-assessments. Then we created a qualitative codebook of the behavioral patterns indicative of each moment, and used this knowledge to build an expert system. We evaluated our system with log data collected from an additional 33 CS students. Our results are promising, with F1 scores ranging from 66% to 98%. We believe that this approach can be applied in many domains to understand and detect student perceptions of learning experiences. 

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. 

While many online resources teach basic web development, few are designed to help novices learn the CSS concepts and design patterns experts use to implement complex visual fea- tures. Professional webpages embed these design patterns and could serve as rich learning materials, but their stylesheets are complex and difficult for novices to understand. This paper presents Ply, a CSS inspection tool that helps novices use their visual intuition to make sense of professional webpages. We introduce a new visual relevance testing technique to identify properties that have visual effects on the page, which Ply uses to hide visually irrelevant code and surface unintuitive relation- ships between properties. In user studies, Ply helped novice developers replicate complex web features 50% faster than those using Chrome Developer Tools, and allowed novices to recognize and explain unfamiliar concepts. These results show that visual inspection tools can support learning from complex professional webpages, even for novice developers.