Vesa Lappalainen

Coding camps bring together individuals from diverse backgrounds to tackle given challenges within a limited timeframe. Such camps create a rich learning environment for various skills, some of which are directly associated with the camp, and some of which are a result of working as a team during the camp. Unfortunately, coding camps often remain isolated from the broader educational curriculum or other bigger context, which downplays the opportunities they can offer to students. In this paper, we present the vision of the European initiative OSCAR, which aims at connecting coding camps to the educational and professional context faced by the learners. In addition, we sketch a supporting platform and its features for connected coding camps.

Quantum computing is a highly abstract scientific discipline, which, however, is expected to have great practical relevance in future information technology. This forces educators to seek new methods to teach quantum computing for students with diverse backgrounds and with no prior knowledge of quantum physics. We have developed an online course built around an interactive quantum circuit simulator designed to enable easy creation and maintenance of course material with ranging difficulty. The immediate feedback and automatically evaluated tasks lower the entry barrier to quantum computing for all students, regardless of their background.

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

This paper reports on a practice used for providing additional support to beginner programmers. This practice emphasizes low social barriers to learning, differentiated instruction, and revision. Altogether we try to avoid defensiveness or stigmatization among those who face difficulties. Student feedback indicates their acceptance of this approach while teaching assistants report that helping students in need of additional support improve their teaching skills. Further, we have observed indications of improved student performance. We describe the practice and suggest a particular educational constructionism, i.e., how students construct their social identities within a particular school setting, as an explanation for our positive experiences. As a conclusion, we stress the importance of support implemented through differentiated instruction and informed by constructionism as a CS1 research topic.