The ITiCSE '23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.

This poster focuses on workshops to support students' soft and selfreflection skills during collaborative learning. These workshops intend to help reduce anxiety during group work and to promote inclusive and equitable collaborative learning environments. Unfortunately, single-paced instructional approaches are typically applied in learning environments [3] and do not consider students' needs when learning nor provide soft-skills guidance that encourages equal participation. The workshops offer educator and student support for equitable group work through upskilling students' soft skills, such as leadership and communication, that promote better teamwork. By assisting students in developing and practising soft and self-reflection skills, they might have the opportunity to contribute to solving the activity, enabling them to participate equally. This poster includes an experimental study plan to integrate and evaluate the effects of these workshops.

Teaching robotics is an attractive way of motivating students to learn computer science. However, it is also a challenging topic for students of all ages and only one teacher in a classroom is too little to support approximately 30 students at the same time. Therefore, intelligent tutoring systems might be a meaningful way to support students and teachers. In this paper we describe an approach to support computer science lessons in secondary schools by using a learner model. We are explaining how the three phases of our learner model (data collection - profile construction - profile application) can be implemented for teaching robotics by using different types of implicit and explicit data to generate feedback for the teacher concerning competencies and knowledge of the students on the one hand and by supporting collaboration and group formation amongst the students on the other hand. The model is derived from literature and supported by data from different studies.

Physical computing is receiving much attention in the shape of numerous variations of devices. This trend is apparent in computer science education from kindergarten to university. To improve the implementation of physical computing in secondary school I observe student's process of problem solving when they use physical computing devices. To successfully implement this in schools, it appears to be valuable to investigate problems that occur during the the process and to develop supporting material. These topics are the foci of my doctoral project and will be figured out with results from scientific inquiry. Particularly building on the experiment as an inquiry working technique.

Physical Computing is used to improve computer science competences in school. For this purpose numerous devices and tools for earning knowledge exist, but only with few didactic concepts. Theoretically, there are a lot of overlaps between physical computing and scientific inquiry. The aim of my research is to learn from other sciences to improve physical computing in computer science education. It appears that computer science skills and especially scientific inquiry skills can be improved in this context. Thus we learn more about student's problems in computer science and can find supporting mechanisms.