Spatial skills have been shown to correspond with performance in computer science courses, and individuals further along in their computing journey tend to score higher on spatial skills tests. There is also evidence that these skills can be trained, consequently mitigating gaps in computing performance across demographic groups. However, it is unclear if training spatial skills has a lasting effect throughout a student's computing degree program. To fill this gap, we present longitudinal analyses from two separate institutions that implemented spatial skills training in their entry-level computing course. We perform a survival analysis on the retention data after four to five years and demonstrate that students with higher spatial skills after training are retained at higher rates throughout a degree program than students with lower spatial skills, even when the students initially started with lower spatial skills. However, the patterns of retention of students with high initial spatial skills and those who improve their spatial skills after training differ in our two cases, indicating the potential impact of other factors, such as self-regulation, which are critical for academic success. These patterns underscore that spatial-skill gains, while important, are not a panacea for student success in computing. Complementary supports that bolster self-regulation and related competencies may be required to sustain long-term retention.

The software engineering graduate apprenticeship program at the School of Computing, University of Glasgow, has a significant emphasis on work-based learning (WBL), with approximately 80% of the students' time over four years spent in the workplace. This work-based aspect of the program plays a more prominent role in the final two years, by which time apprentices are undertaking increasingly larger roles in the workplace. In this report, we present how we addressed the challenge of structuring and assessing WBL in these senior years such that there is a balance between professional competency attainment and ranked academic achievement, while providing a fair and flexible structure. Based on a model of WBL presented by Raelin in 1997, we outline our rationale for dividing the assessments into workplace projects, workplace journal, and a portfolio of artefacts. The projects are categorised into different types both for flexibility and to encourage academia-industry collaboration, the workplace journal encourages higher forms of reflection, while the portfolio assessment encourages and assesses the attainment of professional competencies. We present our experience of implementing this structure, an analysis of student feedback, and the adjustments made in response. With an increasing focus on work-ready skills and competency-based education in software engineering education, we expect the theory-informed structure we developed, and our experience of running and adapting it, to serve as an exemplar for developing a WBL program at research-led institutions.

Students' spatial skills are correlated with performance in STEM disciplines, including computer science. Fortunately, these skills can be trained, consequently improving computing course outcomes. The typical spatial skills training in computing education utilizes a workbook that helps students visualize and sketch the rotation of 3-D shapes. However, more methods have been tested in other STEM fields, including video games and spatial-related toys. In this position paper, we discuss these methods from adjacent fields and present a case of how to pilot them with our computer science students. We argue for the need for more flexible, but still impactful, spatial skills training methods to support our students. These methods could also provide a lower barrier to entry to teaching spatial skills, leading to more computing instructors adopting these methods and more computing students having spatial skills training opportunities. Further, broadening our spatial skills training methods can help us better understand the connections between spatial skills and computer science, which is especially obscure given the abstract nature of our field.

There is a growing body of literature on the relationship between spatial skills and computing success and evidence that developing spatial skills can improve computing outcomes, but almost all of this research has been conducted in higher education. To explore the link between spatial and computing skills in young people, we present the results of a spatial skills intervention deployed in maths lessons for 7--8-year-olds. Compared to a control group, pupils who received spatial instruction improved significantly more in tests of computational thinking despite not receiving deliberate computing instruction. We also explore some demographic factors relating to spatial skills and CT scores, finding previously observed demographic distinctions replicated in this population.

No abstract available.

This ITiCSE Working Group (WG) has two goals: first, to increase awareness of computing education research in the African countries, and second, to create and strengthen connections between computing education researchers in African countries and those in the larger computing education research community.

Graduate and Degree Apprenticeships (G/DAs) are relatively new work-based learning (WBL) degree programs in the UK which were established with the aim "to meet key skills needs, enhance productivity, strengthen university and employer partnerships, and offer a new route into work". These programs have been growing in popularity in the UK in recent years. While work-based learning itself is not a novel concept and several countries in the international SIGCSE community offer WBL programs in higher education, formal degree programs remain rare. This work presents an exploration of some of the challenges and opportunities in running such programs in the UK as the programs became formalized. We sent an initial survey to all 61 institutions in the UK that offer G/DAs in computing. 18 institutions responded and we conducted in-depth follow-up interviews with representatives from 10 institutions. We report themes related to the kinds of students and employers participating in these programs, how the design of G/DAs differs from that of traditional degree programs, and differences between these programs that may not be initially apparent. These findings provide insights that can support educators interested in developing similar formal WBL programs.

Background and Context. Margulieux’s Spatial Encoding Strategy theory (SpES) provides a possible reason for the relationship between spatial skills and success in STEM fields, including CS. While there is indirect evidence to suggest that the theory holds, there is little work which explicitly explores the core theory in practice. Furthermore, current work in spatial skills has largely focused on introductory courses, and it is unclear whether advanced students (and then experts) use spatial skills in computing.

Background and Context. The relationship between spatial skills and computing science success has been demonstrated at multiple institutions. ICER has reacted positively to two theories for why this relationship exists, by both Parkinson & Cutts and Margulieux. However, only limited work has been done to validate these theories, and more confirmatory research about the relationship between spatial skills and module grades in CS is necessary.

At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report's recommendations was "Make connections from computing to your students' lives and interests (Make it Matter) but don't assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.

Spatial skills are cognitive skills relating to the mental consolidation of spatial structures and operations. Rotating shapes in one’s head, identifying patterns from obscured environments and parsing 3D structures from 2D representations are some tasks which require spatial skills. It is fairly easy to see how spatial skills – literally, skills to do with space – are associated with success in STEM domains: 3D modelling in engineering, understanding molecular structure in chemistry and conceptualising kinematics in physics. This connection is less clear for CS, where concepts of space are more abstract and can be thought unrelated to problems that programmers typically face [5]. And yet, spatial skills are correlated with CS success and training spatial skills improves CS outcomes. We are left asking: “What does space look like in CS?”

Four years ago, the authors' institution conducted a spatial skills test with entry level computing students. At the time of writing, these students have just graduated and some agreed to have their academic record over the course of their programme examined. In particular, we wished to discover whether a spatial skills test taken in the students' first year of study was indicative of their final GPA, whether the correlation between grades and spatial ability in their first year rose or declined over the intervening years and to expose the courses in later years of study which most strongly correlated with spatial ability.

There is an intriguing connection between spatial skills and CS: those with better spatial skills tend to do better in many CS related tasks. Since spatial skills are malleable, it is tempting to simply introduce spatial skills training courses to students who are struggling and expect positive outcomes. While improved outcomes are being observed, it would be preemptive to introduce such schemes widely without better understanding the relationship. We do not know why spatial skills are important in CS, so while one might take the gains observed at face value, we stand to lose valuable insights into not only the abstract cognition involved in spatial skills which appears to be of value across STEM, but also reflective and nuanced understanding of how people engage with CS education.

Spatial skills have been associated with STEM success for decades. Research has shown that training spatial skills can have a positive impact on outcomes in STEM domains such as engineering, mathematics and physics; however -- despite some promising leads -- evidence for the same relationship with computing is limited. This research describes a spatial skills intervention delivered to around 60 students in introductory computing courses who tested with relatively low spatial skills, mirroring a well established intervention developed and used by Sorby in engineering for over 20 years. This study has shown correlation between spatial skills and computing assessment marks which was observed both before and after training took place, suggesting that as the students' spatial skills are improved via training, so too is their computing assessment. Students who took part in the intervention also showed a significant increase in class rankings over their peers. The authors consider this to be a good indication that spatial skills training for low spatial skills scorers starting a computing degree is of value.

Goal-directed problem-solving labs can lead a student to believe that the most important achievement in a first programming course is to get programs working. This is counter to research indicating that code comprehension is an important developmental step for novice programmers. We observed this in our own CS-0 introductory programming course, and furthermore, that students weren't making the connection between code comprehension in labs and a final examination that required solutions to pencil-and-paper comprehension and writing exercises, where sound understanding of programming concepts is essential. Realising these deficiencies late in our course, we put on three 3-hour optional revision evenings just days before the exam. Based on a mastery learning philosophy, students were expected to work through a bank of around 200 pencil-and-paper exercises. By comparison with a machine-based hackathon, we called this a Thinkathon. Students completed a pre and post questionnaire about their experience of the Thinkathon. While we find that Thinkathon attendance positively influences final grades, we believe our reflection on the overall experience is of greater value. We report that: respected methods for developing code comprehension may not be enough on their own; novices must exercise their developing skills away from machines; and there are social learning outcomes in programming courses, currently implicit, that we should make explicit.

Work-based learning (WBL) is a delivery model that attempts to address the isolation of theory and practice by integrating them into a single programme. The concern is that through lack of experience and understanding, both universities and industry may devise 'Frankenstein' curricula, harming individuals rather than helping them. This poster introduces a small project to support curricula development by proposing universities act as both the learning provider and workplace for apprentice Research Software Engineers (RSEs).

Vast numbers of publications in computing education begin with the premise that programming is hard to learn and hard to teach. Many papers note that failure rates in computing courses, and particularly in introductory programming courses, are higher than their institutions would like. Two highly distinct research projects have established that average success rates in introductory programming courses world-wide are in the region of 67%. However, there is little published work comparing pass rates in computing courses with those in other STEM disciplines. As institutions continually ask computing educators to justify the atypical failure rates in their courses, a thoroughly researched comparison of this sort could prove useful in demonstrating whether the phenomenon is real, and, if so, whether it extends somewhat beyond the boundaries of individual institutions. This working group will gather information on pass rates in computing courses, particularly introductory programming courses, and in courses at comparable levels in other STEM disciplines. Members of the group will be required to gather the information from their own institutions, and further data will be gathered by way of a broad survey. The data will be analysed to see whether global patterns can be established, and the group will survey the literature to gather and summarise postulated explanations for any difference between pass rates in computing and in other STEM disciplines.

The relationship between spatial skills training and computer science learning is unclear. Reported experiments provide tantalising, though not convincing, evidence that training a programming student's spatial skills may accelerate the development of their programming skills. Given the well-documented challenge of learning to program, such acceleration would be welcomed. Despite the experimental results, no attempt has been made to develop a model of how a linkage between spatial skills and computer science ability might operate, hampering the development of a sound research programme to investigate the issue further. This paper surveys the literature on spatial skills and investigates the various underlying cognitive skills involved. It poses a theoretical model for the relationship between computer science ability and spatial skills, exploring ways in which the cognitive processes involved in each overlap, and hence may influence one another. An experiment shows that spatial skills typically increase as the level of academic achievement in computer science increases. Overall, this work provides a substantial foundation for, and encouragement to develop, a major research programme investigating precisely how spatial skills training influences computer science learning, and hence whether computer science education could be significantly improved.