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

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.