Computing education is not widely taught as part of many undergraduate computer science degrees. Learning the principles, philosophies of computing education can be beneficial to students regardless of their career pathway, but particularly to those wishing to enter academia or K-12 education. In this poster paper we describe two models for introducing computing education to undergraduates. By sharing these practices we hope to enable other researchers and practitioners to reflect on possible ways of introducing computing education into their own institutions. Further work will consider students' perspectives.

Philosophy has long shaped understanding of human cognition and the nature of being – from Plato’s realism and theory of forms to contemporary theories of embodied cognition, the role of philosophy has been central in shaping conceptions of humanhood (being human). Today, Artificial Intelligence (AI) challenges these long-standing ideas and raises fundamental questions about the nature of being, intelligence, and authentic human engagement in education, professional, and social settings. The current research study situates this discourse within Computing Science Education (CSE) and Software Engineering (SE) to investigate the meaning of authentic human engagement in these fields and the challenges Generative AI (GenAI) potentially introduces. To this end, the study employs a three-phase methodological approach: first, through theoretical analysis, we explored traditional and contemporary philosophical accounts of humanhood and authenticity; second, based on the previous phase, we proceeded with an empirical investigation based on interpretative phenomenological analysis, which placed practitioners in SE, academics, and students in computer science university departments at the center of an investigation of how they experience and negotiate the meaning of authentic human engagement within these fields and the potential tensions that the use of GenAI tools introduce. Finally, by synthesizing these findings under a philosophical and sociological lens, the study reveals how authentic human engagement is reshaped by the use of GenAI tools, revealing tensions at the intersection of ontology, epistemology, and axiology in CSE and SE. Our findings highlight the limitations of the instrumentalist view of GenAI, illuminating how the integration of these tools into human systems and practices could redefine who we are becoming, how our knowledge is constructed, and how our value system is shaped, leading to a paradigm shift in educational settings and professional practice. This paper makes three key contributions: (1) a conceptual framework for understanding authentic human engagement in CSE and SE and potential tensions in an AI-driven world, (2) a hybrid methodological approach for investigating and understanding complex human phenomena by integrating philosophical and empirical perspectives, and (3) implications for the teaching and learning of computer science. In doing so, we hope to encourage a reconsideration of the way GenAI systems are developed and integrated within education systems, advocating for a transformation in education in order to preserve what is distinctively human in learning.

Equality of access to higher education is a priority for governments the world over. For example, the United Nations Sustainable Development Goal relating to Quality Education includes a target to “ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university” by 2030 (United Nations, 2022). For those from areas of relative socioeconomic deprivation, the cost of studying for a degree qualification represents a barrier to participation. As such, apprenticeships and similar forms of work-based learning are cited as a means of overcoming this barrier, offering students an opportunity to earn a wage while pursuing their degree. The work described here explores how - and to what extent - a work-based degree in Software Engineering has facilitated wider participation in higher education, through a series of in-depth interviews with apprentices from relatively deprived backgrounds. Analysis suggests that economic considerations are chief among the concerns of apprentices from areas of deprivation; however, the relevant factors are evidently intersectional in nature, and often intertwined with a wider range of personal circumstances, including the effects of previous choices about education and work. As such, we believe this work has implications for how we understand the potential for work-based degrees in Software Engineering and Computing Science to widen access to higher education, and for universities to contribute to achieving the United Nations Sustainability Goal of affordable and quality technical, vocational, and tertiary education.

Background and Context. Cognition, in all its forms, is inferential; inferential reasoning underlies processes like decision-making, problem-solving, argumentation and text comprehension. In the Psycholinguistics domain, it is argued that the study of text comprehension - the way that a coherent representation or a mental model of the text is constructed - is a study of inference generation. In programming education, code comprehension has been considered a milestone for students’ progression. An important goal of code comprehension tasks is for the readers to build coherent representations or mental models of the code. Objective-Hypothesis. A common activity during which code comprehension is required is studying a solved programming problem (SPP). There is a threefold comprehension process that takes place during SPP tasks: comprehension at the problem level, comprehension at the program level, and their relationship. These three processes guide the comprehension and the construction of a coherent mental representation of the whole example; drawing from Psycholinguistics, we hypothesize that differences in the way that students construct the mental model of a given SPP may be associated with differences in inference generation during these three processes. Method. Through a mixed research design that brings together a theoretical-conceptual study and an empirical one, an inferential model for SPP is first developed based on scholarly research in Psycholinguistics and Programming Education. The inferential model is then tested and further refined through a comparative case study during which we identify and compare the kind of inferences a proficient, an average, and a struggling first-year undergraduate student made during the comprehension of an SPP task after the end of the first semester. Findings. The results of this study demonstrate that the more advanced students employed specific inferential strategies during the three SPP comprehension processes: Global Backward Explanations and Forward Predictions, Local Backward Explanations and Forward Predictions, Associations and Paraphrasing, all of which guided their SPP comprehension and successful transfer to an isomorphic task. Implications. The paper provides the first evidence towards a) the importance of inference generation during the study of SPP tasks; b) the kind of inferences made during successful SPP comprehension; c) the development of new research directions and theoretical insights and concepts in the area of SPP comprehension; and d) the development of teaching practice that elicits inferential strategic reading comprehension in order to explore and guide students’ understandings before actual problem-solving.

This working group concerns the adoption of computing education (CE) in undergraduate computer science (CS) programmes. Such adoption requires both arguments sufficient to persuade our departmental colleagues and our education committees, and also curricular outlines to assist our colleagues in delivery. The goal of the group is to develop examples of both arguments and curricular outlines, drawing on any prior experience available.

Argumentation, as the generation and evaluation of arguments, is critical in our ability to reason. Computing education research has long highlighted the relation between reasoning ability and programming skills but, to our knowledge, the relation between argumentative reasoning, and particularly collective argumentative reasoning, and programming have not yet been investigated. The aim of this paper, therefore, is twofold: first, to study empirically the nature of collective argumentative reasoning in programming during problem-solving and secondly, to identify the aspects of argumentation that facilitate or obstruct collective problem-solving. To achieve these aims, through an exploratory research design, our study identifies the argumentative moves and argumentative reasoning schemes employed by expert programmers, MSc students, and first-year undergraduate students (novices) during collective problem-solving by using a protocol analysis of concurrent verbalisations. The study illustrates how collective argumentative reasoning is reflected in the discourses of these groups during problem-solving, and most importantly how argumentative moves and argumentative reasoning schemes interact and impact problem-solving. The three groups exhibited substantial differences: novices engaged in collective monologue, the MSc students engaged in collective but egotistic argumentative dialogue and the experts in collective and altruistic argumentative dialogue. The paper concludes by proposing a turn in educational practices that place argumentative reasoning in the center of both classroom and peer to peer discourse in programming.

Concerns about participation in computer science at all levels of education continue to rise, despite the substantial efforts of research, policy, and world-wide education initiatives. In this paper, which is guided by a systematic literature review, we investigate the issue of inequalities in participation by bringing a theoretical lens from the sociology of education, and particularly, Bourdieu’s theory of social reproduction. By paying particular attention to Bourdieu’s theorising of capital, habitus, and field, we first establish an alignment between Bourdieu’s theory and what is known about inequalities in computer science (CS) participation; we demonstrate how the factors affecting participation constitute capital forms that individuals possess to leverage within the computer science field, while students’ views and dispositions towards computer science and scientists are rooted in their habitus which influences their successful assimilation in computer science fields. Subsequently, by projecting the issue of inequalities in CS participation to Bourdieu’s sociological theorisations, we explain that because most interventions do not consider the issue holistically and not in formal education settings, the reported benefits do not continue in the long-term which reproduces the problem. Most interventions have indeed contributed significantly to the issue, but they have either focused on developing some aspects of computer science capital or on designing activities that, although inclusive in terms of their content and context, attempt to re-construct students’ habitus to “fit” in the already “pathologized” computer science fields. Therefore, we argue that to contribute significantly to the equity and participation issue in computer science, research and interventions should focus on restructuring the computer science field and the rules of participation, as well as on building holistically students’ computer science capital and habitus within computer science fields.

Computing has, for many years, been one of the least demographically diverse STEM fields, particularly in terms of women's participation [2] and those from minoritized racial and ethnic groups. In the case of higher education, one of the most powerful sites of intervention is the classroom. The last decade has seen a proliferation of research exploring new teaching techniques and course sequencing and their effect on the retention of students who have historically been excluded from computing. This research suggests interventions and practices that can affect the inclusiveness of the computer science classroom and potentially improve learning outcomes for all students. But research needs to be translated into practice, and practices need to be taken up in real classrooms. The goal of this working group (WG) is to conduct a systemic "state-of-the-art" review of recent empirical studies of teaching practices that have some explicit test of the impact on women (or other under-represented groups) in computing. The WG will produce an annotated bibliography and a report that distills the research into specific, actionable practices.

Computer programming is one of the most researched subjects within computer science education; within this much attention has been focused on exploring the difficulties and common misconceptions that students experience when learning to program. The study reported here has two aims: firstly, to investigate students' misconceptions around functions by setting up a programming test of advancing difficulty and complexity based on the Bloom and Solo taxonomies, and secondly, to explore the impact that misconceptions have on students' self-efficacy in programming, along with students' self-evaluation and self-efficacy in computer science. Our study revealed seven misconceptions in the area of functions, three of which have never before been reported in the literature, to our knowledge. Additionally, the results suggest that misconceptions do not only prohibit students' progress and learning but have a significant impact on students' self-efficacy in programming.

PRIMM is an approach to teaching programming at K-12 that facilitates the structure of lessons in a purposeful way. PRIMM stands for Predict-Run-Investigate-Modify-Make, and draws on recent research in programming education. In particular the PRIMM approach recognises that starting with existing code and being able to explain what it does gives novice programmers the confidence to write their own programs. Using the PRIMM approach, teachers can devise scaffolded and targeted tasks for students which helps engender understanding, particularly for those who may have previously struggled to understand programming concepts. In this techniques paper, we consider what PRIMM is, and the experiences that teachers have had of using the structure in the classroom. PRIMM materials have been trialled in schools in a study involving around 500 students aged 11-14. From interviews with nine participating teachers we have found that teachers particularly value the collaborative approach taken in PRIMM, the structure given to lessons, and the way that resources can be differentiated. We propose that PRIMM is an approach that could be adopted in all phases of programming education as well as in teacher training.