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.

This panel convenes four educators, each from different institutions and each with experience managing group projects. Their expertise spans topics including: peer assessment and peer evaluation; entrepreneurship; transdisciplinarity; internationalisation; inclusivity; social values; educational technology and tools; feedback and feed-forward; peer rating; free-riders; as well as blended learning; and post-pandemic online discourse. They reflect on the delight of seeing students collaborate to deliver meaningful projects as well as the challenges posed by disengaged students. They also explore a common theme of discordance inherent to teamwork and systems to support student communities.

This study examines metacognition in program comprehension by purposefully inserting errors in a sequence of code tracing and intent problems. Participants were 39 students reasoning about DeMorgan's Laws from a series of test cases. Student performance suggests a range of faulty attributions and a need to further exemplify good and bad practices to develop proper standards for evaluating the products of cognitive processes.

This study examined the effectiveness of sketching flow diagrams in facilitating CS1 students' problem solving and performance on code writing tasks. Five students received training in design strategies and sketched flow diagrams prior to implementing their solution by writing code. Procedures in solving a sequence of four problems depicted in sketches and captured in keystroke log data were coded and scored for each student. The study results showed that specific and generative depictions of procedures predict the correctness of edits made to solutions.

Throughout the COVID-19 pandemic, institutions have had to shift and adapt rapidly to the ever-changing academic landscape. In response, several new, short-term initiatives were set in motion and new policies were, at least temporarily, adopted to support this rapid change. In computer science, specifically, there were new tools utilized, new modes of delivery employed, and new community engagement practices embraced. As a side effect of all this change, an evolving set of faculty and student expectations emerged.

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward.

Throughout the COVID-19 pandemic, computing instructors have adapted their teaching to meet the needs of students in this ever-changing paradigm. These adaptations include the acquisition of new infrastructure, evolving expectations, revised course development strategies and the adoption of new modes of course delivery. Last year, an ITiCSE Working Group, led by Siegel and Zarb, explored the ways in which higher education computing faculty responded to this dramatic shift in the (educational) world [1]. Based on a survey of computing faculty worldwide, the work explored what the academic landscape might look like beyond the impacts of COVID-19. We will explore these results and discuss lessons learned along with the evolution that has taken place since the survey data was collected. Recognizing that it is unlikely that we return completely to pre-pandemic norms, our goal is to identify practices within computing that have newly formed or improved throughout the pandemic, giving extra weight to those that we might hope to keep as we move into a post-pandemic future.

In this panel, we will explore the use of auto-graded programming assignments to support timely and effective feedback to learners via small, scaffolded programming tasks. Program comprehension involves processing at different levels [5,10]. As students proceed through a program, associative processes take place and are described as information as the current statements activate information from the previous statements and from memory of prior knowledge. The most frequently inferred relations by the students are those that provide a coherent understanding of the state changes and outputs of a program [3] as well as the purpose of a piece of code [9]. The resulting, interconnected representation of the program goes beyond the syntax of tokens and statements. The outcome of successful comprehension is a representation that captures the meaning of each statement as students infer the operations of a statement, in terms of the underlying data and control flow, given its function in the context of solving a problem [4]. Although models of program comprehension generally agree regarding the processes by which a student arrives at a mental representation of a program, it is less clear how student-initiated processes play a role in program comprehension, and how they combine with such passive processes to result in comprehension.

The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in the ways that we communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their teaching styles and use of technology. Computing education traditionally focuses on experiential, in-person activities. The pandemic has mandated that educators reconsider their use of student time and has catalysed overnight innovations in the educational setting. Even in the unlikely event that we return entirely to pre-COVID-19 norms, many new practices have emerged that offer valuable lessons to be carried forward into our post-COVID-19 teaching. This working group will explore what the post-COVID-19 academic landscape might look like, and how we can use lessons learned during this educational shift to improve our subsequent practice. The exploration will strive to identify practices within computing that appear to have been improved through exposure to online tools and technologies, and that should therefore continue to be used in the online space. In the broadest sense, our motivation is to explore what the post-COVID-19 educational landscape will look like for computing education.

In a review of student programming work (through playback of their submissions via online IDE), it was noted that students could be quite close to a successful solution and repeatedly start over, deleting all of the work done to that point in time. The words of Thomas Edison expressed the problem associated with this current branding: "I have not failed. I've just found 10,000 ways that won't work." While this is meant to imply that failure has led towards success, the idea that something "won't work" captures the mindset that students are facing when using the word 'failure' in regards to programming. In fact, iterations of programs move students incrementally closer to their goal. The notion of a binary concept like pass/fail being applied to programming leaves less room for creativity and exploration. Amongst new programming students, after reaching this point of 'passing', student fear of modifying and 'playing' with working programs has been noted. In order to address the concerns of students and help shift student mindsets away from 'failure' towards 'incremental improvement', a rebranding exercise must take place.

The COVID-19 pandemic has seen an unprecedented and agile global shift in the ways that we educate, and in the ways that students consume their learning activities. This necessary paradigm shift has allowed many educators to take a critical look at their teaching styles, and to use technology in innovative ways to create the best experience for their students. Computing is a subject which traditionally, focuses on experiential, in-person activities such as labs, groupwork, and tutorials. COVID-19 has forced educators to reconsider the use of time and catalyzed overnight innovation. Some of these new practices are ones that would be valuable to learn from and carry forward into our post-COVID teaching. This BoF will provide a platform for discussion of what the post-COVID academic landscape might look like, and how we can use lessons learned during this educational shift to improve our practice.

As students transition into higher education, their experience can be quite new and foreign to them - at times acting as a barrier into higher education. This experience, while an individual one, consists of many concerns that are shared amongst these transitioning students. Partly as a result of these concerns, retention rates of first year Computer Science students suffer. These topics were discussed and analysed at length in various studies, panels and working groups to create an initial body of understanding, influenced by a number of institutions across the world. However, the recent and unprecedented COVID-19 pandemic has caused further disruption: it is likely that students transitioning into higher education during the pandemic were faced with unparalleled concerns, anxieties and stresses.

A learning experience comprises several factors and understanding them becomes a crucial first step in developing engaging learning experiences, considering how learning is no longer always a passive "absorb what is told/what you read" exercise. Drawing lessons from designing user experiences, we present a structured and holistic approach to design engaging learning experiences and cater to both learners and teachers, by considering the factors and unique attributes of a learning experience. We explore the role of technology in augmenting learning experiences. Our work attempts to understand the factors/attributes that constitute engaging learning experiences and develop strategies/tools to help design such experiences.

As students transition into higher education, their experience can be quite new and foreign to them. This experience, while an individual one, consists of many concerns that are shared amongst these transitioning students. Partly as a result of these concerns, retention rates of first year Computer Science students suffer. Members of this panel have been involved in multi-year studies across Scotland [2, 5] as well as an international study [3] looking at the student experience as they transition into undergraduate Computer Science. This panel hopes to discuss the transition into CS1 and implications for Computer Science retention rates from varied international perspectives as well as through the lens of online learning. It further hopes to discuss new ways that we might be able to better support first year CS student retention in light of collected transition data, as well as the current state of any implemented recommendations in previous work (you may want to rephrase this).

This paper discusses a study where 249 students from 18 secondary schools around Scotland who were on the verge of applying to study Computing Science at a higher education institution were surveyed on their concerns about the upcoming transition. Preliminary conclusions from this work point to the fact that this transition process is one that seems to evolve with the student as they progress through their education, and as such, should be treated differently at various stages within that process.