Three decades of active research on the teaching of introductory programming has had limited effect on classroom practice. Although relevant research exists across several disciplines including education and cognitive science, disciplinary differences have made this material inaccessible to many computing educators. Furthermore, computer science instructors have not had access to a comprehensive survey of research in this area. This paper collects and classifies this literature, identifies important work and mediates it to computing educators and professional bodies.We identify research that gives well-supported advice to computing academics teaching introductory programming. Limitations and areas of incomplete coverage of existing research efforts are also identified. The analysis applies publication and research quality metrics developed by a previous ITiCSE working group [74].

Three decades of active research on the teaching of introductory programming has had limited effect on classroom practice. Although relevant research exists across several disciplines including education and cognitive science, disciplinary differences have made this material inaccessible to many computing educators. Furthermore, computer science instructors have not had access to a comprehensive survey of research in this area. This paper collects and classifies this literature, identifies important work and mediates it to computing educators and professional bodies.We identify research that gives well-supported advice to computing academics teaching introductory programming. Limitations and areas of incomplete coverage of existing research efforts are also identified. The analysis applies publication and research quality metrics developed by a previous ITiCSE working group [74].

Abstract. Numerous specialized ad hoc routing protocols are currently proposed for use, or being implemented. Few of them have been subjected to formal verification. This paper evaluates two model checking tools, SPIN and UPPAAL, using the verification of the Lightweight Underlay Network Ad hoc Routing protocol (LUNAR) as a case study. Insights are reported in terms of identifying important modeling considerations and the types of ad hoc protocol properties that can realistically be verified.

Computing related content is introduced in school curricula all over the world, placing new requirements on school teachers and their knowledge. Little attention has been paid to fostering the skills and attitudes required to teach the new content. This involves not only traditional computing topics, such as algorithms or programming, but also the role of technology in society as well as questions related to ethics, safety and integrity. As technology develops at a fast rate, so does the content to be taught. Learning computing content through isolated in-service training initiatives is by no means enough, but rather, teachers need to develop confidence to independently and continuously explore what is new, what is relevant and how to include digital competence in their teaching. Teachers' self-efficacy is hence of crucial importance. In a previous article [13] we described the development of a self-efficacy scale for teachers, focusing on digital competence as described in EU's framework DigComp 2.0. In this paper, we extend that work by analysing 530 teachers' responses collected in Autumn 2017 during a series of workshops and other professional development events. Our goal was to collect baseline data, painting a picture of teachers' current self-efficacy levels in order to facilitate follow-up studies. In addition, our results also point out challenging areas, consequently providing important insight into what topics and themes should be emphasized in professional development initiatives.

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.

How might the content and outcomes of tertiary education programmes be described and analysed in order to understand how they are structured and function? To address this question we develop a framework for modelling graduate competencies linked to tertiary degree programmes in the computing disciplines. While the focus of our work is computing the framework is applicable to education more broadly. The work presented here draws upon the pioneering curricular document for information technology (IT2017), curricular competency frameworks, other related documents such as the software engineering competency model (SWECOM), the Skills Framework for the Information Age (SFIA), current research in competency models, and elicitation workshop results from recent computing conferences. The aim is to inform the ongoing Computing Curricula (CC2020) project, an endeavour supported by the Association for Computing Machinery (ACM) and the IEEE Computer Society. We develop the Competency Learning Framework (CoLeaF), providing an internationally relevant tool for describing competencies. We argue that

After four decades of research on a broad range of topics, computing education has now emerged as a mature research community, with its own journals, conferences, and monographs. Despite this success, the computing education research community still lacks a commonly recognized core literature. A core literature can help a research community to develop a common orientation and make it easier for new researchers to enter the community. This paper proposes an approach to constructing and maintaining a core literature for computing education research. It includes a model for classifying research contributions and a methodology for determining whether they should be included in the core. The model and methodology have been applied to produce an initial list of core papers. An annotated list of these papers is given in appendix A.

Abstract-Previous research in STEM education demonstrates that students are engaged in a continuous process of identity development, trying to integrate their educational experiences with their perception of who they are, and who they wish to become. It appears increasingly apparent from this body of research that students are not well supported in this process by the education they currently receive.The goal of this paper is to analyse a specific aspect of the student experience, participation, in order to gain a better understanding of how computer science (CS) and information technology (IT) students engage with CS prior to and during their studies.Drawing on student interview data we describe and discuss students' qualitatively different ways of experiencing participation in CS and IT. The notion of participation applied here is inspired by Wenger's notion of participation in his social theory of learning. A phenomenographic analysis identifies a spectrum of qualitatively distinct ways in which the students experience participation in CS and IT, ranging from "using", to participation as "continuous development", and "creating new knowledge".