Computing Education
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Item Ethical Considerations and Student Perceptions of Engagement Data in Learning Analytics(2025-01-07) Karimov, Ayaz; Saarela, Mirka; Aliyev, Samir; Baker, RyanThe ethical use of engagement data in online education is a growing concern as institutions increasingly rely on learning analytics. This study explores students' perceptions of engagement data collection and usage by focusing on their attitudes towards privacy and data management. We conducted a survey among students (n=108) who participated in online education to understand their views on data collection practices, privacy concerns, and preferences for data handling. The results demonstrate that while many students are comfortable with their engagement data being used for personal and instructor dashboards, significant concerns remain about privacy, particularly with the collection of facial expressions and chat participation data. Students emphasized the importance of transparency and control over their data and they highlighted the need for clear communication and consent processes. These findings illustrate the necessity for ethical data practices that ensure students feel secure and informed about how their engagement data is utilized.Item Conveying the Ethics of Artificial Intelligence in K–12 and Academia: A Systematic Review of Teaching Methods(2025-01-07) Tschoppe, Nils; Katsarov, Johannes W.; Drews, Paul; Trittin-Ulbrich, HannahArtificial intelligence (AI) and its recent advancements pervade vast areas of education, the workplace, and society. As a driver of technological progress, AI has the potential to transform entire business areas, optimize the way we work and live together, and promote creativity. Concomitantly, it harbors the risk of biased algorithms, discrimination, and misinformation. Accordingly, it is now more important than ever to teach students—as potential future designers and users of AI systems—how to deal responsibly and ethically with AI. To support educators in conveying the responsible and ethical use of AI, we conducted a systematic literature review based on the PRISMA guideline. As a result, we present an overview of established and innovative methods of teaching AI ethics in K–12 and academic settings. We discuss these in terms of their effectiveness and grounding in learning theories and derive implications for theory and practice.Item Conceptions of AI among K-12 Students in Azerbaijan: A Topic Modeling Approach(2025-01-07) Saarela, Mirka; Karimov, Ayaz; Heilala, Ville; Sikström, PietaThis study explores the perceptions of artificial intelligence (AI) among Azerbaijani students by analyzing data collected from 73 participants. Using automatic topic modeling with the recent multilingual sentence transformer to generate word embeddings, along with manual semantic analysis, we identified 12 unique topics that reflect diverse aspects of students' understanding of AI. Key findings include students' recognition of AI's role in facilitating tasks, its applications in daily life, and its autonomous capabilities. However, there are significant knowledge gaps and misconceptions, with some students expressing concerns about AI's potential negative impacts. The analysis highlights the need for explainable AI (XAI) in K-12 education to address these misconceptions and provide a clearer understanding of AI technologies. These insights are crucial for designing educational interventions that prepare students for a future increasingly influenced by AI.Item When Project Conditions Clash: How 3D Development Packs & Team Size Shape the Relationship Between Video Game Programmers’ Experience & Performance(2025-01-07) Schulz, AnkeAs workers’ learning on the job is essential to organizations’ performance, identifying factors that shape it is a key managerial concern. We consider that workers’ task scope – known to influence these outcomes – is shaped by more structural factors than their job assignment. Building on the learning curve framework and U.S. data from the video game industry covering 61,989 observations over 13 years, we analyze the role that project conditions – specifically, 3D development packs and team size – play in shaping the relationship between programmers’ experience and their performance. Our results support our expectation that development packs support, while larger team size deters their learning progress on the job. They highlight the importance for managers to not only think about support technologies and their benefits in isolation, but in combination with other project conditions.Item Introduction to the Minitrack on Computing Education(2025-01-07) Oliveira, Wilk; Dantas, Pasqueline