High failure rates appear to be a norm in introductory programming courses. Many solutions have been proposed to improve the high failure rates. Surprisingly, these solutions have not lead to significant improvements in the performance of students in introductory programming courses. In this study, the relationship between self-efficacy, emotional engagement and the performance of students in introductory programming courses were examined. Enjoyment, interest, and gratification were identified as three factors contributing to emotional engagement in introductory programming courses from a review of existing literature and from focus groups. An online survey of 433 students in introductory programming courses showed that the students’ programming self-efficacy beliefs had a strong positive effect on enjoyment, while gratification and interest had a negative effect on programming performance. These findings have implications for course instructors who design and deliver introductory programming courses.

Coordination is crucial in agile software development projects and a Theory of Coordination in co-located agile software development projects explains coordination in this context. This theory has propositions based on case study research. To improve the generalisability of theory built from case studies, researchers often transition to a theory testing phase involving a large-scale field study using the survey method. Prior to a large-scale field study, the propositions generated during theory building must be converted to testable hypotheses. There is little guidance explaining the complexity of this transition process and the challenges involved. Therefore, this paper explains the operationalisation process of transitioning from research propositions to research hypotheses and illustrates the process using the Theory of Coordination. The paper offers six practical guidelines, identifies seven challenges encountered, and potential solutions for each challenge. This paper contributes to agile software development and theory testing research offering seven recommendations for research practice.

<p>High attrition and failure rates are a common phenomenon in introductory programming courses and are a major concern since course instructors are not able to successfully teach novice programmers the fundamental concepts of computer programming and equip them with skills to code solutions to programming problems. Existing solutions that attempt to minimise the high failure and attrition rates have had little impact on improving the performance of the novice programmers. However, the behaviour of the novice programmer has received little attention from introductory programming course instructors although the literature on learning theory suggests that self-efficacy and engagement are two behavioural factors that affect a student’s performance. This study fills the gap in existing research by examining the effect of programming self-efficacy on the engagement of novice programmers, and the effect of their engagement on their programming performance.  A research model that proposes a link between programming self-efficacy and the indicators of engagement that are specific to the context of introductory programming courses, and a link between the indicators of engagement to the programming performance of the novice programmer was developed. A three-phased mixed methods approach which consists of two survey questionnaires and focus groups was used to validate the research model. Data was collected in New Zealand and in Malaysia with 433 novice programmers participating in the survey questionnaires while 4 focus groups were held to refine and validate the indicators of engagement in introductory programming courses. The findings of the focus groups confirmed that participation, help-seeking, persistence, effort, deep learning, surface learning, trial and error, interest, and enjoyment were indicators of engagement while gratification emerged as a new indicator of engagement in introductory programming courses.  The data from the survey questionnaires were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study found that the programming self-efficacy beliefs of novice programmers had a strong influence on their engagement behaviour with the exception of help-seeking, while effort, enjoyment, deep learning, and surface learning were predictors of programming performance. These findings have implications for introductory programming course instructors and the recommendations emerging from this study include making clear behavioural expectations, designing courses which stimulate and support effective behaviour, and making novice programmers aware of the engagement behaviour that does not lead to better programming performance. This study contributes to the theory of teaching computer programming, and to the practice of designing and delivering introductory programming courses.</p>

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<p>High attrition and failure rates are a common phenomenon in introductory programming courses and are a major concern since course instructors are not able to successfully teach novice programmers the fundamental concepts of computer programming and equip them with skills to code solutions to programming problems. Existing solutions that attempt to minimise the high failure and attrition rates have had little impact on improving the performance of the novice programmers. However, the behaviour of the novice programmer has received little attention from introductory programming course instructors although the literature on learning theory suggests that self-efficacy and engagement are two behavioural factors that affect a student’s performance. This study fills the gap in existing research by examining the effect of programming self-efficacy on the engagement of novice programmers, and the effect of their engagement on their programming performance.  A research model that proposes a link between programming self-efficacy and the indicators of engagement that are specific to the context of introductory programming courses, and a link between the indicators of engagement to the programming performance of the novice programmer was developed. A three-phased mixed methods approach which consists of two survey questionnaires and focus groups was used to validate the research model. Data was collected in New Zealand and in Malaysia with 433 novice programmers participating in the survey questionnaires while 4 focus groups were held to refine and validate the indicators of engagement in introductory programming courses. The findings of the focus groups confirmed that participation, help-seeking, persistence, effort, deep learning, surface learning, trial and error, interest, and enjoyment were indicators of engagement while gratification emerged as a new indicator of engagement in introductory programming courses.  The data from the survey questionnaires were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study found that the programming self-efficacy beliefs of novice programmers had a strong influence on their engagement behaviour with the exception of help-seeking, while effort, enjoyment, deep learning, and surface learning were predictors of programming performance. These findings have implications for introductory programming course instructors and the recommendations emerging from this study include making clear behavioural expectations, designing courses which stimulate and support effective behaviour, and making novice programmers aware of the engagement behaviour that does not lead to better programming performance. This study contributes to the theory of teaching computer programming, and to the practice of designing and delivering introductory programming courses.</p>