K-12 computing education research is a rapidly growing field of research, both driven by and driving the implementation of computing as a school and extra-curricular subject globally. Within discipline-based education research, it is a new and emerging field, drawing on fields such as mathematics and science education research for inspiration and theoretical bases. The urgency around investigating effective teaching and learning in computing in school alongside broadening participation has led to much of the field being focused on empirical research. Less attention has been paid to the underlying philosophical assumptions informing the discipline, which might include a critical examination of the rationale for K-12 computing education, its goals and perspectives, and associated inherent values and beliefs. The goals of this research project are to understand the implicit and hidden values, perspectives and goals underpinning computing education at school. This will be achieved through a critical examination of a wide body of literature leading to the development of a categorization and framework.

The implications of using large language model (LLM) tools for learning to program at secondary school level are largely unknown, and yet there is pressure for teachers to engage with these. To start addressing this gap, we investigated: RQ1: What are secondary educators' views on the potential classroom use of LLM program error message explanations? RQ2: In what ways can a feedback literacy perspective support the analysis of educators' views of potential classroom use of LLM program error message explanations? The responses of eight expert secondary school educators were gathered during a semi-structured, activity-based interview and qualitatively analysed. Fifteen themes were derived from their commentary, of which ten corresponded to enhanced program error message (PEM) guidelines. Yet, all themes correlated to feedback literacy theory, providing a more holistic view. The analysis revealed that educators preferred LLM explanations to guide and develop understanding rather than tell, that students should be supported to make judgements and action LLM-generated feedback. Combining PEM guideline and feedback literacy findings, we suggest augmented IDEs should be designed with educators and students in mind, and teacher professional development (PD) is needed. Research is needed to compare our findings with a wider range of educators and investigate what feedback literacy means for resource design, PD, and classroom practice in secondary and undergraduate contexts.

Computer science (CS) is increasingly being made available to K-12 students around the world. Research in several countries has indicated that female, Black and ethnic minority learners may be less likely to study computing, particularly at university. Culturally responsive computing, drawing on culturally relevant pedagogy, can help us start addressing the diversity issue. Research and initiatives in this area have been evident in the US but less so elsewhere. This work-in-progress paper describes ten reflective prompts or Areas of Opportunity (AOs) for CS educators to use for reviewing and adapting the cultural responsiveness of their K-12 computing activities for their context. The AOs build on guidelines co-created with teachers. We describe each of the AOs, correlate them to literature, suggest what they might look like in practice, and how we are using them in the context of a wider study. We suggest the AOs may be useful for other researchers to investigate in their country's context and welcome discussion about the AOs.

In recent years, significant interest in AI has resulted in many K-12 learning resources being developed for teachers and students. However, a consensus on which concepts and skills should be developed has yet to be reached. In addition, there is limited research on how these resources can be used by teachers. As AI/machine learning is not currently mandated in many CS curricula, supporting teachers is an ongoing challenge. Analysing resources currently available to teachers provides us with a starting point to consider how resources could be designed to better support teaching and learning in this area. In this paper, we surveyed the landscape of resources available and categorised 307 AI/ML teaching resources based on their core learning focus. We devised and employed a learning content analysis framework, SEAME, to determine how resources covered AI at multiple levels: (i) Social & Ethical; (ii) Application; (iii) Model, and; (iv) Engine. We found that most resources focused on the Application level (80%) with many covering the Model level (60%) and fewer at the Social & Ethical (40%) and Engine (27%) levels. We found little consensus about what to teach and how with many resources failing to specify target ages. Similarly, we found few examples of resources with professional development opportunities or appropriate lesson documentation, indicative of the challenges teachers face in teaching about AI. We propose that the SEAME framework provides an innovative starting point for teachers and researchers to review resources and consider what a progression of AI concepts and skills might look like that is comprehensive and simple to use. Likewise, it provides a common language for articulating the learning focus of resources. In order to support teachers encountering a new strand of CS, we suggest that further work in developing age-appropriate curricula and better documentation for AI resources is needed.

Motivation. Historically, the field of computer science (CS) has maintained a low level of representation for women and ethnic minorities. Recent initiatives have focused on social equity and justice within CS, presenting culturally relevant pedagogy (CRP) and culturally responsive teaching (CRT) as approaches that incorporate students’ cultures and identities into learning, with the aim of reducing inequities and improving student engagement and participation. Much of the scholarship on CRP/CRT has been situated in the US, demonstrating a gap in literature for how this pedagogy may be used in the context of the UK. Objective: This paper strives to fill this gap by providing a review of teachers’ current practices that implement CRT into their CS classrooms in England, analysing data from teacher workshops using a sociological lens to inform how culturally relevant computing education can be developed. Method. We collected data from workshop transcripts with 19 K–12 educators of CS and developed themes to describe characteristics of their pedagogical approaches. We used Freire’s and Bourdieu’s sociological nexus to frame the analytic process. Results. We identified four overarching themes: adaptation of materials in terms of content and context; confidence building and maintaining rapport; integration of social justice issues into the curriculum; and reviewing practice utilising a reflexive approach to teaching. Discussion. This study draws on the work of Bourdieu and Friere to develop our understanding of unequal participation in the field of CS. The review of current practices can, to a certain extent, reveal the relationship between culture and computing education, demonstrating which mechanisms of CRP have been adopted by practising teachers.

Background and context. Non-formal learning for K-12 computing education enables young people to learn about computing outside the formal curriculum. Many studies have reported on non-formal initiatives but it is not always clear what children and young people have gained from their participation. Objectives. This study set out to investigate non-formal learning initiatives by means of a systematic literature review. The two research questions addressed by the study are: (1) What has been the focus of recent computing education research about K-12 initiatives for young people and (2) What is the impact of non-formal K-12 computing initiatives? Method. A systematic literature review of computing education research was conducted, focused on non-formal initiatives for young people. Research was included from any country, but must be published in English between January 2015 and April 2021. Searches using key terms were performed across three databases. 88 studies were synthesised from over 400 initial results. Findings. The vast majority of studies reported on immersive multi-day settings such as summer camps run by universities (n= 67), with fewer (n=21) reporting on regular ongoing after-school or weekend clubs. The most popular affective outcomes measured by studies were self-efficacy (n=25) and interest (n=22). Measurement of cognitive outcomes, such as knowledge (n= 13) and skills (n=17), was less prevalent. 22 different topics were identified from the studies, with most studies being programming-heavy. The majority of papers measured the short-term impact of these interventions, and generally there was an inconsistent or incomplete reporting of learner characteristics across the studies. Implications. The lack of papers investigating regular after-school initiatives suggests that the majority of non-formal learners are not being studied or that summer school findings are being wrongly extrapolated to this setting. More rigorous research is needed for regular after-school and short-term non-formal contexts to ensure that this set of learners’ experiences is understood and potentially improved.

We describe the development and trial of a survey based on self-determination theory to investigate the motivations of K-12 teachers to learn ML and AI. Our participants (n=28) were most motivated by personal enjoyment (an intrinsic motivator) and student benefits rather than extrinsic factors, such as external pressure. We will further investigate teacher motivation, as we suggest that this is an important aspect of ML and AI education research.

Motivation. In education, classroom talk is a vital aspect of a lesson, and programming education is no exception. While the role of language and dialogue has been researched in depth in other school subjects, there has been less research in the programming context. Sociocultural theory highlights the importance of language as a mediator for learning, alongside other tools. Objectives. Drawing on sociocultural theory and models of dialogic education, the purpose of the study was to investigate the ways in which programming teachers use classroom talk to support learning, and to propose a model to frame our understanding of this element of programming lessons. Method. The qualitative study used phenomenological methodology to investigate and interpret teachers’ ‘lived experiences’ of classroom talk. Interviews were conducted with 20 primary and secondary computing teachers about the content and effect of classroom talk in programming lessons. The context of the study was PRIMM, a lesson structure which highlights the importance of talk around a shared programming artefact. Results. Analysis of data revealed four main themes: how talk occurs in the classroom setting, how questioning is used to facilitate talk, how students are encouraged to explain, and why teachers feel it is important for students to use correct vocabulary. Discussion. Building on research into models of dialogue in education and our findings we suggest a model to frame talk in the programming classroom. We discuss the contribution of PRIMM to our understanding of talk in programming lessons. More research is needed to validate the proposed model and to investigate the impact of classroom talk on learning outcomes in programming.

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.

The Computer-Science Student-Centered Instructional Continuum (CS-SCIC) is a new framework to support PreK-12 instructors in their lesson design. Educators are faced with choices when building lessons; there is a tension between direct instruction, constructivism and constructionism and difficulty in providing differentiated instruction. Theoretically aligned to Vygotsky's zone of proximal development, CS-SCIC places research-based instructional strategies on a simple learning continuum. Teachers use the continuum to discuss, review and design learning events. Used internationally, initial qualitative feedback from teachers who attended pilot CS-SIC workshops was emphatically positive. Future work includes more feedback from academia and formal research, including pre and post-professional development workshop surveys.

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.

The recent introduction of computer science (CS) education into schools in many countries has led to a surge in interest in programming tools and approaches which make CS concepts and tasks engaging, motivating and accessible to all. There is renewed interest in supporting learning through physical computing, which has been shown to be motivational whilst offering opportunities for collaboration and creativity. Within this context the BBC recently led a collaborative venture in the UK to develop a portable and low-cost programmable device. The consortium funded and produced one million devices, enough for every 11-12 year-old in the UK. In this paper, we report on what we believe to be the first study to investigate the usability and affordances of the BBC micro:bit. We interviewed 15 teachers and 54 pupils in schools in England about their experiences with the device who were, in general, enthusiastic about the potential of the BBC micro:bit. We describe pupils' experiences in terms of usability, creativity, the tangibility of the device and their learning of programming, and analyse their experiences in the context of previously reported benefits of physical computing.