The final issue of the 33rd volume of Computer Science Education focuses primarily on computing education for learners before they reach secondary school. The first five articles explore existing literature through two review articles and three empirical studies with learners ranging from eight to thirteen years of age. This constellation of papers sheds new insights on how young learners are experiencing their first days of programming, how curriculum shapes what skills are emphasized early on, and how these experiences can be used to promote transfer in a range of contexts at a later point. We hope that you will enjoy reading these articles as a set while reflecting on the similarities and differences between them.
Kallia and Cutts present the first contribution in this issue, which details the development of a grounded cognition model for conceptual development in computing education for young learners. This conceptual review article synthesizes a wide variety of prior research on grounded cognition and uses it to propose the Enacted Instrumented Formal Framework for Early Learning in Computing. In particular, this new theoretical model provides new ways for thinking about the use of manipulatives in teaching computing to early learners which could serve as a foundation for future work on learning trajectories, unplugged activity design principles, and teaching techniques to promote transfer.
The second piece, by Yauney, Bartholomew, and Rich, is also a review article exploring early experiences with computing. This article explores the results of a systematic review of evidence around Hour of Code (HoC) activities from 64 different peer-reviewed papers. They found that papers clustered into six general themes: advice for how to conduct an HoC event, experience reports about HoC efforts, reusable HoC activities for others to use, research studies, reports on use of the online HoC platform, and literature reviews. While the results help demonstrate the broad interest in Hour of Code and its general success in introducing diverse learners to basic ideas of coding through engaging puzzles, Yauney and coauthors identify a paucity of evidence establishing the extent to which HoC activities result in substantive knowledge gains or attitude changes.
Kale, Yuan, and Roy continue our discussion of computing education with young learners in their study of Code.org’s lesson plans targeted at third graders (learners of approximately 8 years of age). They apply content analysis techniques to lessons to uncover the necessary core computational thinking skills and supporting skills like reflection, resilience, and digital citizenship. Their analysis shows a high degree of emphasis on the pattern recognition and algorithm automation skills, but surprisingly little coverage of decomposition and abstraction skills. They then apply social network analysis techniques to explore relationships between pairs of skills in how they are presented together. More generally, this approach is a unique case study that could be used to identify both strengths and gaps in other large-scale public curriculum materials.
The fourth article in this issue by Espinal, Vieira, and Guerrero-Bequis investigates the ability of children to transfer their programming skills from the MakeCode block-based environment to Scratch and Python. Their multiple-case study approach with 27 students is interesting both because of the multiple target languages and the fact that learners did not speak English as a first language. This unique context contributes a new perspective to the evidence base around knowledge transfer from block-based languages. While almost all learners were able to complete near transfer tasks in Scratch, fewer than 25% were able to complete far transfer tasks in Python and difficulties with English playing a significant role in adding to the complexity.
Zdawczyk and Varma extend the discussion of novice programming for younger learners with a study of 87 young women in grades 5–8 (approximately 10–13 year olds). In particular, they deploy multiple-survey instruments to investigate differences in student perceptions of stereotypes, sense of belonging, and self-efficacy and make comparisons about how those factors differ related to the programming language used in the intervention (Scratch vs Python). Their results provide additional evidence about the complex inter-relation of belonging, interest, and self-efficacy for all students, but they also identify the unique role of block-based tools like Scratch in promoting higher interest and self-efficacy for young learners than text-based counterparts.
The final article in this issue, authored by Cheers and Lin, turns our attention away from younger learners, and instead focuses on a new automated technique for identifying instances of probable code plagiarism in undergraduate computing courses. They describe their novel approach using density-based clustering over similarity scores generated from several other existing code plagiarism detection tools. Evaluation data using historical and simulated codebases showed a promising ability to identify suspicious groups of assignments while maintaining a low error rate and that the new technique outperforms use of any one existing tool alone. As the authors note, the ultimate goal is to help guide instructors in their manual review of potential issues of misconduct rather than to eliminate the need for human judgment entirely.
Lastly, it is also our distinct pleasure to welcome Dr Elenor O’Rourke to the Computer Science Education editorial board. She brings a wealth of mixed methods research expertise in computing education to the team, and we look forward to working with her as our newest associate editor.
Eleanor O’Rourke is an Assistant Professor in Computer Science and the Learning Sciences at Northwestern University, where she co-directs the interdisciplinary Delta Lab. Her research explores the design of novel computational systems to support motivation and learning in STEM domains, with a particular focus on computer science education. Recent projects include studying student beliefs about programming ability in introductory computer science courses, designing game mechanics that encourage students to practice effective problem-solving skills, and developing web inspection tools that allow novice developers to learn directly from authentic professional websites. Her interventions have been used by over 100,000 students online, adopted by companies, and integrated into classrooms. Her work has been recognized through an NSF CAREER Award, a Google Systems Research Award, and best paper awards and nominations at SIGCSE, UIST, and CHI.