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.

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.

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 F1-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.

We present a study using new computational methods, based on a novel combination of machine learning for inferring admixture hidden Markov models and probabilistic model checking, to uncover interaction styles in a mobile app. These styles are then used to inform a redesign, which is implemented, deployed, and then analysed using the same methods. The data sets are logged user traces, collected over two six-month deployments of each version, involving thousands of users and segmented into different time intervals. The methods do not assume tasks or absolute metrics such as measures of engagement, but uncover the styles through unsupervised inference of clusters and analysis with probabilistic temporal logic. For both versions there was a clear distinction between the styles adopted by users during the first day/week/month of usage, and during the second and third months, a result we had not anticipated.

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.

Girls’ participation in the computer science (CS) field is influenced by several factors, including positive student engagement, confidence in their ability to learn and perform well on CS tasks, and belief in the usefulness of learning CS. While there has been substantial research on student engagement in CS in general, no studies have focused on understanding how the dimensions of student engagement (behavioural, cognitive, emotional, and social) relate to confidence and perceived usefulness in relation to gender, especially for female students in the CS field. To address this gap, a study was conducted using a multidimensional student engagement self-report instrument to collect data from female high school students in CS classes (n = 284). Spearman’s rank correlation results showed a positive correlation between confidence and perceived usefulness with all four dimensions of student engagement. Therefore, increasing student confidence and perceived usefulness can successfully engage students in learning CS. The paper also highlights areas that require further investigation and discusses the challenges and implications of these findings for developing interventions to increase student engagement, confidence, and perceived usefulness in CS. The study significantly contributes to the field by measuring the multidimensional student engagement of Saudi Arabia’s high school pupils in CS classes. The findings are expected to shed light on factors that may motivate female students’ interest in CS and increase their learning.

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. Assessment within on-line environments can vary, based on the technology available and pedagogical approach. However, forms of assessment in these environments must support additional constraints, in that they must scale to support potentially massive cohorts, minimal learner interaction, and range of learner intention. At the same time, there are unique opportunities due to the accessibility of rich learning analytics and learner data. Understanding effective assessment and assessment feedback at scale has broader implications as we cope with growing CS enrolments, and interest in technology. This working group aims to explore assessment within CS MOOCs, as a specific example of the on-line learning environment, identifying engaging and effective assessment exemplars that reflect both the constraints and opportunities of this context. The working group will (1) identify and survey existing literature on formative and summative assessment of Computer Science MOOCS, (2) clarify how assessment may be considered meaningful for students, (3) identify key features of assessment that assist an instructor in evaluating the nature and quality of learning, and (4) identifying case studies that explore both innovative and effective assessments to provide a rich experience for students and also detailed feedback to teaching staff. The outcome of this working group will be a report.

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.

We present a novel class of mixed membership models for combining information from multiple data sources inferring inter‐view and intra‐view statistical associations. An important contemporary application of this work is the meaningful synthesis of data sources corresponding to smartphone application usage, app developers' descriptions and customer feedback. We demonstrate the ability of the model to infer meaningful, interpretable and informative app usage patterns based on the app usage data augmented with rich text data describing the apps. We provide quantitative model evaluations showing the model provides significantly better predictive ability than comparative related existing methods. © 2016 The Authors. Stat Published by John Wiley & Sons Ltd

ScreenLife is a personal tracking application developed at the University of Glasgow. It enables users to view data about how long they spend using digital devices such as laptops, mobile phones and tablets. ScreenLife has two general components: a logger that monitors and records when a device is in use, and an interface that provides users with a visual presentation of the logged data. We implemented ScreenLife loggers for four platforms: iOS, Android, Mac, and Windows (meaning it will work on iPhones, iPads, Android phones, Android tablets, Mac computers, and Windows computers). The visual interface can be viewed using the ScreenLife iOS or Android app, or on a web browser on any platform. We conducted a user trial of ScreenLife in mid 2015. We recruited 21 participants and asked them to install the application on their digital devices. The participants had a combined total of 48 devices. The participants were required to keep the application installed for 28 days or more, but were told they could look at the interface as much or little as they want. The ScreenLife dataset consists of: information collected from the ScreenLife loggers; information about when the users accessed the interface; qualitative data from user interviews conducted at the end of the trial; screenshots of the application.

Evaluation and redesign of user-intensive mobile applications is challenging because users are often heterogeneous, adopting different patterns of activity, at different times. We set out a process of integrating statistical, longitudinal analysis of actual logged behaviours, formal, probabilistic discrete state models of activity patterns, and hypotheses over those models expressed as probabilistic temporal logic properties to inform redesign. We employ formal methods not to the design of the mobile application, but to characterise the different probabilistic patterns of actual use over various time cuts within a population of users. We define the whole process from identifying questions that give us insight into application usage, to event logging, data abstraction from logs, model inference, temporal logic property formulation, visualisation of results, and interpretation in the context of redesign. We illustrate the process through a real-life case study, which results in a new and principled way for selecting content for an extension to the mobile application.

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.

This paper discusses the ethical dimensions of a research project in which we deployed a personal tracking app on the Apple App Store and collected data from users with whom we had little or no direct contact. We describe the in-app functionality we created for supporting consent and withdrawal, our approach to privacy, our navigation of a formal ethical review, and navigation of the Apple approval process. We highlight two key issues for deployment-based research. Firstly, that it involves addressing multiple, sometimes conflicting ethical principles and guidelines. Secondly, that research ethics are not readily separable from design, but the two are enmeshed. As such, we argue that in-action and situational perspectives on research ethics are relevant to deployment-based research, even where the technology is relatively mundane. We also argue that it is desirable to produce and share relevant design knowledge and embed in-action and situational approaches in design activities.

Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to a greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period, resulting in a collection of users' trace logs for multiple sessions. We develop a simple, yet flexible, non‐parametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate that our approach finds interpretable strategies and provides good predictive performance compared with alternative modelling assumptions using a non‐parametric Bayes framework. Copyright © 2015 John Wiley & Sons, Ltd.

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.

We have conducted a video study of households in Scotland with cohabiting students and young professionals. In this paper we unpack five examples of how mobile devices are used by people watching television. In the examples we explore how screens are used together (a) in a physical ecology, (b) in an embodied way, (c) in an orderly way, and (d) with respect to others. We point out that mobile devices are routinely used to access media that is unconnected and unrelated to media on television, for example for sending and receiving messages, browsing social media, and browsing websites. We suggest that mobile devices are not used to directly enhance television programmes, but to enhance leisure time. We suggest that it is important, when considering mobile devices as second screens, not just to treat these as a design topic, but to pay attention to how they are interactionally integrated into the living room.

In using 'app store'-style software repositories to distribute research applications, substantial ethical challenge exists in gaining informed consent from potential participants. Standard 'terms and conditions' pages are commonly used, but we find they fail to communicate relevant information to users. We suggest interrupting use of an application with a visual representation of collected data, rather than merely providing a description at first launch. Data collected, but not uploaded, before this can be used to create personalised examples of what will be shared. We experiment with different ways of presenting this information and allowing opt-out mechanisms, finding that users are more concerned when presented with a visual, personalised representation, and consequently stop using the application sooner. We observe a particular difference in non-English speakers, suggesting that our proposed approach might be especially appropriate for global trials, where not all users will be able to understand researchers' disclosures of data logging intent.