Studies on how the skills needed to carry out usercentred design (UCD) activities can be taught and learnt have been limited, although the impact of UCD is widely recognised in professional settings. Conducting UCD activities in educational contexts within the constraints of university courses remains a challenge, specifically when students are in their first year and are just beginning to engage with the complexities of software development. In this paper, we argue for the importance of early exposure to a variety of UCD activities, with the help of scaffolding and decomposition, through the step-by-step UCD Sprint process. We explore the integration of the UCD Sprint process for 7 weeks in a 12-week, first-semester, firstyear Computer Science undergraduate course, focusing on how students experience exposure to a wide range of UCD activities. Data is gathered using two questionnaires: the background questionnaire completed by 215 students at the start of the course, gathering data on demographic information, and the perceived experience questionnaire completed by 57 students, gathering data on comfort levels with UCD activities and overall feedback on the UCD Sprint process. The students rated the UCD Sprint steps as generally thought-provoking, particularly valuing steps involving direct user engagement, such as interviews and highfidelity prototyping. However, process-related steps were rated lower for perceived long-term usefulness. Qualitative feedback highlighted the UCD Sprint process as clear and beneficial. Suggestions for improvements to the process included reducing its complexity and optimising the learning materials for guiding the students in using the process. Statistically significant increases in comfort levels with UCD activities, including user research, evaluation, and design were observed post-course. The main contribution of this paper lies in its insights into the student experiences with the individual UCD activities contained within the UCD Sprint, assessing how early exposure to UCD activities can enhance their comfort with performing core UCD activities. By offering early, scaffolded exposure to UCD activities through the UCD Sprint, we are actively designing a student-centred learning environment that prioritises hands-on experience and engagement with UCD principles. This approach not only fosters student comfort and competence in UCD activities but also empowers them to take an active role in their learning journey, encouraging them to become more adept at understanding and addressing user needs, ultimately preparing them for future careers in a rapidly evolving, user-focused tech industry.

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning . Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages. This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

This research paper addresses the significant problem of identifying students-at-risk of failing or dropping out in educational settings. While extensively studied, improving early detection of students likely to fail or drop out remains essential for universities to provide support resources. Previous methods have relied on the students' academic performance to analyse and predict learning strategies, but the complexity of implementing predictive models is heightened by various factors influencing student outcomes. Notably, learning is both a dynamic and socially regulated process, with time and social interactions playing key roles for academic achievement. Nonetheless, despite the importance of these elements, educational research investigating their combined effect is scarce. As grade distribution is affected by several elements, including teaching modalities, grading policies, and course design, identifying students-at-risk and their learning strategies is generally an imbalanced problem, which can lead to biases in predictive algorithms. Our work addresses this issue from a social and temporal perspective, guided by two research questions: (1) To what extent is it possible to inform the early identification of students-at-risk of failing based on interaction data from online discussion forums?, and (2) How does the classification performance compare between traditional oversampling methods and oversampling methods that take the structure of the interactions into account? We based our research on data from an undergraduate course's online forum to build a temporal network of students' communication events across the 12 weeks of the course. Temporal sequences of centrality measures from these interactions served as input for time series classification algorithms. Two oversampling methods are compared: baseline minority oversampling, and a state-of-the-art graph oversampling method that accounts for network structure. Our results show that a temporal network approach, coupled with node oversampling, can enhance student-at-risk identification. However, due to the complexity of the problem and the interactions' sparsity the classification performance is limited when relying solely on this data. We discuss the impact of our findings and contributions, implications, limitations, and future research directions.

The rise of social media has revolutionized communication and the sharing of information and interests, with a significant impact on purchasing behavior. Consumers increasingly rely on social media for product recommendations and reviews, often finding themselves "accidentally influenced" by other users' posts and advice. This study examines the impact of social media in Iceland during the COVID-19 pandemic when there was a surge of posts giving dietary advice to prevent or treat the virus or its symptoms. One example is the rise and fall of fish oil advice. Using a large-scale dataset from one of the most popular supermarket chains in Iceland and netnography, we apply Data Science to analyze: sales data; Google search trends; Twitter posts from 2019 and 2020 to understand the impact of the online world on purchasing behavior in the offline world. Our results show the massive power of social media on people's purchasing behavior, particularly during a pandemic, and provide a comparison of consumer behavior before and during COVID-19.

Abstract Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has a high clinical relevance in pediatrics, as it can negatively impact the physical and mental health of children. Since mouth breathing is an anomalous condition in the general population with only 2% prevalence in our data set, we are facing an anomaly detection problem. This type of human medical data is commonly approached with deep learning methods. However, applying multiple supervised and unsupervised machine learning methods to this anomaly detection problem showed that classic machine learning methods should also be taken into account. This paper compared deep learning and classic machine learning methods on respiratory data during sleep using a leave-one-out cross validation. This way we observed the uncertainty of the models and their performance across participants with varying signal quality and prevalence of mouth breathing. The main contribution is identifying the model with the highest clinical relevance to facilitate the diagnosis of chronic mouth breathing, which may allow more affected children to receive appropriate treatment.

Software development presents technical and social challenges for new entrants to the field, particularly in empathizing with potential users. That can lead to misunderstanding of users' needs and requirements, affecting the user experience. Research has shown that implementing user-centred design (UCD) methods during software development positively impacts the overall user experience. Thus, teaching higher education students UCD methods from the beginning of their undergraduate studies is preferable, as upskilling later in their professional work can be a more complex endeavour. A process called the User-Centred Design Sprint (the UCD Sprint) covering 14 UCD methods was introduced to first-semester undergraduate Computer Science students at Reykjavik University, during a 7-week period through lectures and on-site problem-solving sessions. As additional support, the students were provided with a digital aid on the UCD Sprint, supporting the learning process. Feedback on the learning of the UCD Sprint process was collected through in-person surveys with 70 respondents, and qualitative digital aid evaluations through student reporting from 110 respondents. The findings show that the students rated direct interaction with users, especially high. On the opposite end, the students rated methods aimed at refining the initial ideas on the lower side of the rating scale. The students mentioned seeing the value of using the UCD Sprint process when developing software to better empathize with and cater to users' needs. The students used the digital aid provided alongside tools such as in-class slides, the textbook, and internet-based support. Overall, the student feedback on the digital aid was positive, with students appreciating clear instructions for conducting the methods in the UCD Sprint process. This paper reports a mixed-method study with results diverging into a three-fold contribution. Firstly, it reports the perceived usefulness of the UCD Sprint process and the experience of using it during the course. Secondly, findings from the usage of a digital aid to accompany the UCD Sprint process are reported. This provides the basis for the third contribution, which is presented in terms of recommendations that others can utilise when developing digital aids for assisting students in higher education.

Abstract Categorical work in chronic care is increasingly dependent on digital technologies for remote patient care. However, remote care takes many forms and while various types of digital technologies are currently being used, we lack a nuanced understanding of how to design such technologies for specific novel usages. In this paper, we focus on digital technologies for patient-generated health data and how their use changes categorical work in chronic care. Our aim is to understand how categorical work changes, which novel forms of categorical work emerge and what the implications are for the care relation. This paper is based on an ethnographic study of healthcare professionals’ work at a pelvic cancer rehabilitation clinic and their interactions with patients. In this setting, supportive talks between patients and nurses are central. To understand the complexities of categorical work in chronic care when patient-generated health data are introduced, we contrast the traditional supportive talks with supportive talks where the nurses had access to the patients’ patient-generated health data. We identify and analyze challenges connected to novel forms of categorical work. Specifically, we focus on categorical work and how it can undergo changes. Our empirical findings show how changes occur in the way patients’ lived experience of the chronic disease aligns with the categories from chronic care, as well as in the way the nurse works with clinical categories during the talk. These insights help us further understand the implications of patient generated-data use in supportive talks. We contribute to an improved understanding of the use of patient-generated health data in clinical practice and based on this, we identify design implications for how to make categorical work more collaborative.

Telecare has the potential to increase the quality of care while also decreasing costs. However, despite great potential, efficiency in care practices and cost reduction remain hypothetical. Within computer supported cooperative work (CSCW), one focus of telecare research has been on awareness support in distributed real-time communication in comparison to physical meetings since face-to-face consultations have been known as the “gold standard” of conducting care. Research has shown that it is hard to maintain qualities such as awareness through video-mediated meetings. In this research, the goal has not been to mimic the qualities of face-to-face consultations but rather to document the qualities of three types of patient meetings (consultations) and to understand in what kinds of situations each consultation type is a viable option. In this paper, we focus on the essential qualities of i) face-to-face consultations, ii) video-based consultations, and iii) telephone consultations and shed light on their affordances. The research contribution includes an extension of the affordance lens to incorporate socio-technical, two-sided affordances, that constitute important aspects for understanding complexity when heterogeneous actors co-existing in a practice, where affordances can differ for different “sides” in the complex practice—a view that is fruitful when dealing with heterogeneous actors and a set of analog and digital tools in a practice.