Morrison and Lauren Margulieux, for their insightful and detailed feedback of my thesis. I am honored to have Professor Matti Tedre as my opponent. Thanks for the CNPq and IFMS for their financial support.

Context. Code quality is a key issue in software development. The ability to develop high quality software is therefore a key learning goal of computing programs. However, there are no universally accepted measures to assess the quality of code and current standards are considered weak. Furthermore, there are many facets to code quality. Defining and explaining the concept of code quality is therefore a challenge faced by many educators. Objectives. In this working group, we investigated code quality as perceived by students, educators, and professional developers, in particular, the differences in their views of code quality and which quality aspects they consider as more or less important. Furthermore, we investigated their sources for information about code quality and its assessment. Methods. We interviewed 34 students, educators and professional developers regarding their perceptions of code quality. For the interviews they brought along code from their own experience to discuss and exemplify code quality.Results. There was no common definition of code quality among * Working group co-leaders:

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Computing education researchers and educators use a wide range of approaches for measuring students' prior knowledge in programming. Such measurement can help adapt the learning goals and assessment tools for groups of learners at different skills levels and backgrounds. There seems to be no consensus on if and how prior programming knowledge should be measured. Traditional background surveys are often ad-hoc or non-standard, which do not allow comparison of results between different course contexts, levels, and learner groups. Moreover, surveys may yield inaccurate information and may not be useful due to lack of detail. In contrast, tests can provide much higher detail and accuracy than surveys about student knowledge or skills, but large-scale tests are typically very time-consuming or impractical to arrange. To bridge the gap between ad-hoc surveys and standardized tests, we propose and evaluate a novel self-evaluation instrument for measuring prior programming knowledge in introductory programming courses. This instrument investigates in higher detail typical course concepts in programming education considering the different levels of proficiency. Based on a sample of two thousand introductory programming course students, our analysis shows that the instrument is internally consistent, correlates with traditional background information metrics and identifies students of varying programming backgrounds. CCS CONCEPTS• Social and professional topics → Computer science education; Model curricula; Student assessment.

Cognitive load (CL) on a learner's working memory has emerged as an influential concept in computing education and beyond. CL is commonly divided in at least two components, intrinsic load (IL) and extraneous load (EL). We seek progress on two questions: (1) How can CL components be measured in the programming domain?(2) How should CL measurement deal with the "third component" of germane load (GL)? We replicate two studies: Morrison and colleagues' [49] evaluation of a questionnaire for self-assessing CL in programming, which is an adaptation of a generic instrument; and Jiang and Kalyuga's [24] study, which found support for a twocomponent measure of CL in language learning, with GL redundant. We crowd-sourced CL data using Morrison's questions at the end of a video tutorial on programming for beginners. A confirmatory factor analysis found strong support for a three-factor model, with factors matching the items intended to capture IL, EL, and GL, respectively. A two-factor model with IL-targeting and GL-targeting items combined gave a poorer fit. Our findings strengthen the claims of discriminant validity and internal reliability for Morrison's CL questionnaire for programming; construct validity for GL remains open, however. We affirm the need for further research on the twocomponent theory of CL and the sensitivity of CL self-assessments to contextual factors. CCS CONCEPTS• Social and professional topics → Computing education.