Students' Emotional Reactions to Programming Projects in Introduction to Programming

Lishinski, Alex; Yadav, Aman; Enbody, Richard

doi:10.1145/3105726.3106187

Cited by 82 publications

(38 citation statements)

References 26 publications

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“…They extended the analysis to provide a descriptive model of how computer science majors build selfefficacy perceptions, reported via four narratives that present either positive or negative development in students' self-efficacy [49]. In an interesting quantitative continuation, Lishinski et al [68] built a path model of how students' emotional reactions correlate with their performance on programming projects, with the valence of the emotion dictating the direction of the correlation. Self-efficacy and three categories, happy/proud, frustrated/annoyed, inadequate/disappointed, were based on the work of Kinnunen and Simon.…”

Section: What Types Of Construct Have Beenmentioning

confidence: 99%

“…Programming education is no exception here. Widely reported difficulties that students face in learning programming have prompted many researchers to investigate relationships between attitudes, other non-cognitive factors, and students' motivation [16,81,111], self-efficacy or beliefs about one's skills [24,99,108], study practices [12,18,110], and learning results [17,68,80]. Lishinski and Yadav [67] provide a comprehensive summary of the research literature on students' motivation, attitudes, and other non-cognitive factors.…”

Section: Introductionmentioning

confidence: 99%

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Theories and Models of Emotions, Attitudes, and Self-Efficacy in the Context of Programming Education

Malmi

Sheard

Kinnunen

et al. 2020

Proceedings of the 2020 ACM Conference on International Computing Education Research

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Research into the relationship between learning computing and students' attitudes, beliefs, and emotions often builds on theoretical frameworks from the social sciences in order to understand how these factors influence, for example, students' motivation, study practices, and learning results. In this paper we explore the computing education research literature to identify new theoretical constructs that have emerged from this research. We focus on empirical work in programming education that extends or adapts theories or instruments from the social sciences or that independently develops theories specific to programming. From an initial data set of more than 3800 papers published in the years 2010-2019, we identify 50 papers that present a range of domain-specific theoretical constructs addressing emotions, affect, beliefs, attitudes, and self-efficacy. They include 11 validated instruments and a number of statistical models, but also grounded theories and pedagogical models. We summarize the main results of many of these constructs and provide references for all of them. We also investigate how these constructs have informed further research by analysing over 850 papers that cite these 50 papers. We categorize the ways that theories can inform further research, and give examples of papers in each of these categories. Our findings indicate that among these categories, instruments have been most widely used in further research, thus affirming their value in the field. CCS CONCEPTS• Social and professional topics → Computing education.

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Section: What Types Of Construct Have Beenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Theories and Models of Emotions, Attitudes, and Self-Efficacy in the Context of Programming Education

Malmi

Sheard

Kinnunen

et al. 2020

Proceedings of the 2020 ACM Conference on International Computing Education Research

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“…Improved sensitivity may also help reveal the difference between learning computer science and other STEM disciplines [15]. For example, Lishinski et al (2017) reveal that students have strong emotional reactions to programming exercises, a psychosocial construct that may be unique to CS and that may explain some of the variance in student success [20].…”

Section: Discussionmentioning

confidence: 99%

A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education

McGill

Decker

McKlin³

et al. 2019

Proceedings of the 50th ACM Technical Symposium on Computer Science Education

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A growing body of evidence indicates that there is a deep effect of noncognitive factors on academic achievement and learning. In this study, we analyzed a set of 31 evaluation instruments designed to measure noncognitive constructs (e.g., self-efficacy, confidence, motivation) within computing education. Using the Lee and Shute framework, we assigned each of the 115 unique constructs found in the instruments into one of the four components (Student Engagement, Learning Strategies, School Climate, Social-familial Influences) and their subcomponents to determine which constructs are most frequently measured. We found that the majority of constructs were designed to measure Student Engagement (Affect and Cognition) and School Climate (Teacher Variables). Constructs measuring Learning Strategies and Social-Familial Influences (e.g., homework strategies, peer influences) occur the least. This study may enable further discussions of what noncognitive factors are/are not currently being measured within the computing education community.

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“…The emotions that students experience are identified as a relevant element in the learning of programming, but they are an aspect that has not been widely addressed among the factors that influence the success of this type of course [12][13]. There is a limited number of studies that address the emotional response of students when they learn to program [14][15][16][17]. Out of a group of 29 studies analyzed, only one of them [14] addressed the problem from a gender perspective, therefore, it can be said that this variable is an aspect little considered in the different works presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…There is a limited number of studies that address the emotional response of students when they learn to program [14][15][16][17]. Out of a group of 29 studies analyzed, only one of them [14] addressed the problem from a gender perspective, therefore, it can be said that this variable is an aspect little considered in the different works presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Learning about Programming and Epistemic Emotions: A Gendered Analysis

et al. 2020

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Programming courses often turn into courses with high percentage of desertion and, sometimes, result in a factor that drives students to abandon their careers, even when they are subjects highly relevant in the training of engineers in the areas of computer science, IT, and related careers. These courses demand high cognitive processes, which generate several emotions learning-related that, when taken into account and evaluated, could be used in favor of learning. Programming courses generate negative emotions in female students in a higher proportion than men, which may even lead them to abandon the career, widening the gender gap. In recent years, there has been a growing interest in the role of emotions in academic environments at university level, as well as for knowing the reason for the low participation of women, despite the importance of their role and skills, in computing areas. However, the interest in analyzing the emotions that emerge from students as they learn to program is quite recent. There is not an important number of studies around the emotions of women while they learn to program. The objective of this study is to analyze the behavior -at an emotional level- of students towards different teaching activities, establishing gender level comparisons, and considering the incorporation of elements of collaboration and gamification to identify differences in the emotions originated by these activities.

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Students' Emotional Reactions to Programming Projects in Introduction to Programming

Cited by 82 publications

References 26 publications

Theories and Models of Emotions, Attitudes, and Self-Efficacy in the Context of Programming Education

Theories and Models of Emotions, Attitudes, and Self-Efficacy in the Context of Programming Education

A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education

Learning about Programming and Epistemic Emotions: A Gendered Analysis

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