Machine Learning (ML) is a challenging topic to teach due to its broad scope and multi-step nature. We developed a no-code practice-based tool in Python for teaching ML as a dashboard called MLDASH. Students are recommended ML tasks to solve and increase their competence levels. MLDASH helps learners understand ML concepts through practice and application, following a no-code approach and making ML learning accessible to a broader audience. To our knowledge, this study is the first to propose a dashboard that utilizes Learning Analytics for teaching ML. Our experimentation with students shows that MLDASH facilitates personalized learning and adapts to different practice styles.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K-12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking.

Agile methodologies like Scrum are vital in software engineering (SE) education but can be complex for students. Especially, planning backlog items during a sprint turns out to be challenging. Our research develops and assesses a web-based educational tool, ScrumQuest, aimed at aiding first-year SE students in mastering Scrum practices with an emphasis on effective planning. Addressing a previous gap in Scrum educational tools, ScrumQuest also includes a unique and novel feature that provides automated feedback on sprint backlog activities to enhance student guidance. Evaluation involving 21 first-year SE university students indicated a positive experience with ScrumQuest, highlighting enhanced engagement and improved usage of sprint backlogs. Students found ScrumQuest easier to use than GitHub Projects, which served as a baseline in the study. In addition to developing and evaluating a novel Scrum educational tool, we contribute to design-science research by providing validated functional requirements within this context. We explain our research and demonstrate ScrumQuest's practical relevance in Software Engineering education.