Editorial

Abstract

This issue of Computer Science Education features a diverse collection of work that is concerned with learners ranging from kindergartners to advanced undergraduate university students. The first three papers are showcasing qualitative research in the field. Haroldson and Ballard describe their content analysis of children’s books, Nijenhuis-Voogt et al. present an analysis of semi-structured interview data from secondary teachers, and Xu et al. code pictures drawn by university computer science students. Taken together these papers demonstrate the breadth of research questions amenable to qualitative methods. Haroldson and Ballard present a qualitative study of picture books and graphic novels related to computer science and programming for primary school children. This innovative work seeks to both uncover the computing practices portrayed by characters in these books as well as depict the range of their racial and ethic identities. The authors find that most K-12 CS Framework practices are reasonably well represented in their corpus of childrens’ literature, with the notable exception of explicit presence of Fostering an Inclusive Computing Culture as a component of the storyline. Their results also show an encouraging gender parity among characters in the books, and around one-quarter of the characters were children of color. However, Haroldson and Ballard also note a paucity of adult characters of color, which is a significant impediment to providing children from underrepresented groups with role models that look like they do. Their work is both a research contribution advancing what we know about how computing is portrayed to young learners and a practical contribution for teachers seeking to identify appropriate literature for their classrooms. Nijenhuis-Voogt, Bayram-Jacobs, Meijer, and Barendsen explore context-based approaches for teaching algorithmic reasoning skills in secondary education. Through a series of semi-structured interviews with secondary computing teachers, they aim to characterize various real-world contexts and examples used by teachers while introducing algorithms and the analysis thereof. Furthermore, their interviews delve into teachers’ motivations and concerns when contextualizing their lessons. They find that learners’ perceived authenticity of and personal interest in the context is a key consideration along with the appropriateness of the context for the relevant algorithm. Interestingly, they observe how at times various instructional design characteristics related to context choice may be in conflict with one another, and they describe teachers’ use differentiated contexts for learners in the same classroom in order to address various personal interests. Xu, Ritzhaupt, Umapathy, Ning, and Tsai employ a “draw a picture” strategy to investigate how undergraduate university students depict learning computer science. While this data collection technique has been widely used with younger computer science learners in both formal and informal learning experiences, this work is unique in that it deploys the method with students at various stages of the undergraduate computer COMPUTER SCIENCE EDUCATION 2021, VOL. 31, NO. 1, 1–3 https://doi.org/10.1080/08993408.2021.1889763