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.

Work-based learning (WBL) programs in Software Engineering provide students with opportunities to apply academic knowledge in the workplace. However, how students navigate the transition between knowledge and skills acquired at university and their application in the workplace remains largely unexplored. We implemented a WBL Software Engineering program in which students spend most of their time in the workplace rather than at university. Reflective student essays were used to examine how our implementation supports the integration and transfer of learning between academic and professional contexts. University modules - such as those on software design patterns and programming - offered directly applicable skills, while foundational knowledge and problem-solving frameworks supported further development. Workplace experiences reinforced understanding, provided context, and enabled practical application. However, skills such as project management, leadership, and context switching were primarily developed in the workplace, revealing gaps between academic team projects and industry needs. Difficulties in transferring some advanced technical skills also point to a disconnect between academic instruction and industry application. Our findings suggest a cyclical relationship between university and workplace learning, with each enhancing the other. We propose key considerations for WBL program design, including curriculum alignment, structured skill development, and integrated delivery. Finally, we underscore the value of reflective essays in observing and supporting skill integration.

Enhancing student engagement in computer science (CS) courses is crucial to improving students' achievement and fostering active participation in computer science education (CSE). Previous studies have highlighted different factors that shape student engagement, including behavioural, cognitive, emotional, and social engagement. Additionally, other factors influence student engagement, such as students' beliefs in the usefulness of learning computer science and their confidence in taking CS classes. Despite existing studies investigating student engagement in CSE, limited studies have explored factors that influence novice student engagement in CS courses. Further, no study has applied machine learning (ML) techniques on the combined self-reported engagement data and student log data to predict CS learning performance. Therefore, this study used ML techniques to explore and identify student engagement factors that affect and predict CS learning outcomes. To achieve this, data was collected using three different sources: self-reported data, system log data, and CS learning performance data. Log data from student behaviour on the system included monthly logs of student access to the CS course page on the LMS during the semester, the total hits of student interactions with the course content throughout the semester, and the total number of task submissions. Our analysis involves 77 novice students who consented and completed a multidimensional self-reported questionnaire during the second semester of 2022 - 2023 at a university in Saudi Arabia. The K-means clustering algorithm was used to understand engagement patterns by classifying students into groups based on their levels of self-reported engagement, log data, and academic performance. Classification algorithms using Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM) were used to predict CS learning performance from student engagement data (self-reported and logs). We evaluated the performance of ML algorithms using metrics including accuracy, precision, recall, and Fl-score. The clustering results showed that students who actively engage with the course content (log data) tend to achieve higher grades, especially those with higher total hits of student interactions with the course content throughout the semester. The classification results showed that the RF model outperforms DT and LGBM, highlighting the significance of student interactions with the course in the first month of the semester as the key indicator influencing CS learning performance. Our findings contribute to a deeper understanding of student engagement in CS education and highlight various sources and factors used to measure and influence student engagement. The study has implications for educators, researchers, and stakeholders who may design effective interventions that would increase engagement to improve student learning outcomes in CS education. Future work will use a larger sample of participants from various educational levels in different countries.

Competencies may be defined as the knowledge, skills, and professional dispositions that an individual is required to demonstrate in order to be considered professionally competent. Competency-based education has long been a feature of professional degree programs, but the discipline of Computing Science has only recently begun to embrace competencies as a means of structuring or evaluating students' learning. Meanwhile, the practice of work-based learning - also well-established in other professional disciplines - has become more prevalent in Computing Science education, with increasing emphasis placed on work-based modes of learning, such as internships and apprenticeships. In this paper, we examine how students enrolled on a degree-level apprenticeship in Software Engineering have developed their professional competencies in the workplace. The paper is based on an analysis of 38 student assignments, wherein apprentices were asked to identify the competencies they have demonstrated, with reference to a portfolio of work. The UK Standard for Professional Engineering Competence and Commitment, which outlines the competencies required for certification as an Incorporated Engineer, provided the necessary framework. Competencies relating to communication and inter-personal skills were among those most often cited by apprentices, with competencies relating to knowledge and understanding and design and development systems also featuring prominently. Competencies relating to responsibility, management, or leadership were less prevalent, with professional commitment proving to be the least commonly cited category of competencies. We provide examples of how apprentices claim to have demonstrated each competency, and discuss the implications of these findings for competency-based learning in Computing Science education.

Females remain underrepresented in computer science (CS), despite numerous studies investigating the causes using different data types and analysis techniques. In recent years, machine learning (ML) has been increasingly used in education, particularly for analysing student engagement using some engagement dimensions. However, no study has yet used ML algorithms to analyse student behavioural, cognitive, emotional, and social engagement survey data. In this paper, we present a study investigating whether these four dimensions of engagement are related to female high school students' beliefs in the usefulness of learning computer science and their confidence in taking CS classes in Saudi Arabia. We also employ ML techniques to identify important indicators that can predict students' confidence and beliefs in the usefulness of learning CS. Additionally, we compare three supervised ML techniques, Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), to determine which algorithms better predict confidence and usefulness in learning CS. Our sample consisted of 284 participants from four schools who completed the multidimensional survey, and we evaluated the ML algorithms using Mean squared error (MSE), Mean absolute error (MAE), and Determination coefficient (R2). Our findings show that each dimension of student engagement positively correlates with the confidence and usefulness of learning computer science, and it is possible to predict them from student engagement indicators. The RF model outperformed DT and LGBM, identifying 'enjoyment in learning new things' and 'interest in topics in CS class' out of 28 indicators as the most important features to predict usefulness. Additionally, from 28 features, 'looking forward to CS class' and 'enjoyment in learning new things' are the most important indicators influencing confidence. Our findings contribute to a broader understanding of student engagement in CS education and highlight various indicators used to measure student engagement. These findings shed light on factors that may motivate and interest female students toward CS learning. Future work will use a larger sample of participants from schools and higher education in different countries.

Increased opportunities for online learning, including growth in Massive Open Online Courses (MOOCS), are changing our education environments, increasing access and flexibility in how students engage with education. However, there are still many questions regarding how we engage with students effectively in these environments, in particular through assessment.

There is a mental health crisis facing universities internationally. A growing body of interdisciplinary research has successfully demonstrated that using sensor and interaction data from students' smartphones can give insight into stress, depression, mood, suicide risk and more. The approach, which is sometimes termed Digital Phenotyping, has potential to transform how mental health and wellbeing can be monitored and understood. The approach could also transform how interventions are designed, delivered and evaluated. To date, little work has addressed the human and ethical side of digital phenotyping, including how students feel about being monitored. In this paper we report findings from in-depth focus groups, prototyping and interviews with students. We find they are positive about mental health technology, but also that there are multi-layered issues to address if digital phenotyping is to become acceptable. Using an acceptability framework, we set out the key design challenges that need to be addressed.

There have been many large-scale investigations of users' mobile app launch behaviour, but all have been conducted on Android, even though recent reports suggest iPhones account for a third of all smartphones in use. We report on the first large-scale analysis of app usage patterns on iPhones. We conduct a reproduction study with a cohort of over 10,000 jailbroken iPhone users, reproducing several studies previously conducted on Android devices. We find some differences, but also significant similarities: e.g. communications apps are the most used on both platforms; similar patterns are apparent of few apps being very popular but there existing a 'long tail' of many apps used by the population; users show similar patterns of 'micro-usage'; almost identical proportions of people use a unique combination of apps. Such similarities add confidence but also specificity about claims of consistency across smartphones. As well as presenting our findings, we discuss issues involved in reproducing studies across platforms.

Many systems have been designed to study social aspects in physical activity tracking. In most, social functions are performed at a distance, such as posting comments and achievements, or via in-app leaderboards. We present an activity tracking app designed instead to encourage face-to-face encounters. Stickers for Steps seeks to recreate the experience of a physical sticker book, where digital 'stickers' are collected in an album, but where stickers are awarded for reaching activity targets. Users will accrue duplicate stickers, which can be swapped with other co-located users over a Bluetooth connection. We explore the usage of our app, reporting on a trial with 33 participants. We find that our app successfully encouraged groups of users to swap duplicates, review progress and to discuss their levels of activity. We provide design recommendations for future activity tracking systems that could incorporate face-to-face interactions.

Numerous studies have tracked people's everyday use of digital devices, but without consideration of how such data might be of personal interest to the user. We have developed a personal tracking application that enables users to automatically monitor their 'screen time' on mobile devices (iOS and Android) and computers (Mac and Windows). The application interface enables users to combine screen time data from multiple devices. We trialled the application for 28+ days with 21 users, collecting log data and interviewing each user. We found that there is interest in personal tracking in this area, but that the study participants were less interested in quantifying their overall screen time than in gaining data about their use of specific devices and applications. We found that personal tracking of device use is desirable for goals including: increasing productivity, disciplining device use, and cutting down on use.

We have developed a mobile application called Pass The Ball that enables users to track, reflect on, and discuss physical activity with others. We followed an iterative design process, trialling a first version of the app with 20 people and a second version with 31. The trials were conducted in the wild, on users' own devices. The second version of the app enforced a turn-taking system that meant only one member of a group of users could track their activity at any one time. This constrained tracking at the individual level, but more successfully led users to communicate and interact with each other. We discuss the second trial with reference to two concepts: social-relatedness and individual-competence. We discuss six key lessons from the trial, and identify two high-level design implications: attend to "practices" of tracking; and look within and beyond "collaboration" and "competition" in the design of activity trackers.

This paper characterises the use of activity trackers as "lived informatics". This characterisation is contrasted with other discussions of personal informatics and the quantified self. The paper reports an interview study with activity tracker users. The study found: people do not logically organise, but interweave various activity trackers, sometimes with ostensibly the same functionality; that tracking is often social and collaborative rather than personal; that there are different styles of tracking, including goal driven tracking and documentary tracking; and that tracking information is often used and interpreted with reference to daily or short term goals and decision making. We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people's outlook on their future.

The recent rise in large scale trials of mobile software using 'app stores' has moved current researcher practice beyond available ethical guidelines. By surveying this recent and growing body of literature, as well as established professional principles adopted in psychology, we propose a set of ethical guidelines for large scale HCI user trials. These guidelines come in two parts: a set of general principles and a framework into which individual app store-based trials can be assessed and ethical concerns exposed. We categorise existing literature using our scheme, and explain how researchers could use our framework to classify their future user trials to determine ethical responsibility, and the steps required to meet these obligations.

With the launch of 'app stores' on several mobile platforms and the great uptake of smartphones among the general population, researchers have begun utilising these distribution channels to deploy research software to large numbers of users. Previous Research In The Large workshops have sought to establish base-line practice in this area. We have seen the use of app stores as being successful as a methodology for gathering large amounts of data, leading to design implications, but we have yet to explore the full potential for this data's use and interpretation. How is it possible to leverage the practices of large-scale research, beyond the current approaches, to more directly inform future designs? We propose that the time is right to re-energise discussions on large-scale research, looking further than the basic methodological issues and assessing the potential for informing the design of new mobile software.

User trials of mobile applications have followed a steady march out of the lab, and progressively further ''into the wild', recently involving ''app store'-style releases of software to the general public. Yet from our experiences on these mass participation systems and a survey of the literature, we identify a number of reported difficulties. We propose a hybrid methodology that aims to address these, by combining a global software release with a concurrent local trial. A phone-based game, created to explore the uptake and use of ad hoc peer-to-peer networking, was evaluated using this new hybrid trial method, combining a small-scale local trial (11 users) with a ''mass participation' trial (over 10,000 users). Our hybrid method offers many benefits, allowing locally observed findings to be verified, patterns in globally collected data to be explained and addresses ethical issues raised by the mass participation approach. We note trends in the local trial that did not appear in the larger scale deployment, and which would therefore have led to misleading results were the application trialed using ''traditional' methods alone. Based on this study and previous experience, we provide a set of guidelines to researchers working in this area.

Recent advances in mobile device technology have opened up new possibilities in enhancing the experience of spectators at stadium-based sporting events. In creating novel applications for use in such settings, designers must be aware of the current practices of spectators and of features of the environment at such events that novel applications may seek to exploit. This work forms an early part of the Designing the Augmented Stadium project. Data sets have been collected from spectators, logging the results of Bluetooth scans alongside GPS location. This paper presents an information visualization tool that can be used in the analysis and exploration of this data, to provide insight into the activities of spectators, the relationship between an individual spectator and the crowd as a whole and the suitability of stadium environments for applications based on infrastructure such as mobile ad hoc networks (MANETs) and wireless mesh networking. Various visualization tools are described and example cases are illustrated, using several real-world data sets recorded at football matches.

Visualisation systems consisting of a set of components through which data and interaction commands flow have been explored by a number of researchers. Such hybrid and multistage algorithms can be used to reduce overall computation time, and to provide views of the data that show intermediate results and the outputs of complementary algorithms. In this paper we present work on expanding the range and variety of such components, with two new techniques for analysing and controlling the performance of visualisation processes. While the techniques presented are quite different, they are unified within HIVE: a visualisation system based upon a data-flow model and visual programming. Embodied within this system is a framework for weaving together our visualisation components to better afford insight into data and also deepen understanding of the process of the data's visualisation. We describe the new components and offer short case studies of their application. We demonstrate that both analysts and visualisation designers can benefit from a rich set of components and integrated tools for profiling performance.