Informatics in Education

This study builds on a recent systematic mapping of computing education literature by conducting an in-depth qualitative analysis of selected studies on group work in Project-Based Learning (PjBL), published between 2010 and 2021. We examined how prominent theoretical frameworks are used in this context. We found that frameworks were often applied either as teaching tools or to inform course design, and when used in these ways, authors frequently reported positive pedagogical outcomes. While frameworks like Tuckman’s model were often referenced only superficially, Social Loafing was more commonly explored in depth. Inductive analysis was particularly effective in distinguishing between background mentions and more substantial integration of theory.We recommend a more intentional, theory-driven approach to research and pedagogy to strengthen conceptual clarity and practical impact. Shared community resources and clearer reporting practices could further support deeper theoretical engagement in the field.

This research investigates university students’ success in their first programming course (CS1) in relation to their motivation, mathematical ability, programming self-efficacy, and initial goal setting. To our knowledge, these constructs have not been measured in a single study before in the Finnish context. The selection of the constructs is in line with the statistical model that predicts student performance (“PreSS”) (Quille and Bergin, 2018). The constructs are compared with various demographic and background variables, such as study major, prior programming experience, and average weekly working hours. Some of the main results of this study are as follows: (1) students generally entered with a high interest in programming and high motivation, but these factors did not increase during the course, i.e., interest in programming did not increase. (2) Having prior experience yielded higher initial programming self-efficacy, grade expectations, and spending less time on tasks, but not better grades (although worse neither). While these results can be seen as preliminary (and alarming in some parts), they give rise to future research for investigating possible expectation–performance gaps in CS1 and later CS studies. As our dataset accumulates, we also hope to be able to construct a valid success prediction model.

This longitudinal study investigates the impact of an extra-curricular programming workshop in student interest development in computer science. The workshop was targeted at 12-18-year old youngsters. A survey was sent to all previous participants with a known home address; 31.5% responded the survey (n = 197). This data was then combined with pre-workshop survey data, and analyzed with mixed methods. Positive development of interest was discovered for 57% of the respondents, of which nearly all attributed their interest increase to the workshop at least partly (92%). Qualitative inspection revealed that the workshop provided three anchors that facilitated students' reengagement with programming and development of interest: disciplinary content, a concrete artifact built by students themselves, and tools. Neutral development and interest regress were also discovered, though the impact of the workshop on these interest trajectories remains unclear.