Malmö University
Not a member yet
35467 research outputs found
Sort by
You want a system that supports you, not one you constantly have to work around : A qualitative study on how the automation of attendance lists and seating allocation can improve examination administration at Malmö university
No full textThis study explores how automation can improve the efficiency of exam administration at Malmö University, with a particular focus on participant lists and seat allocation. Despite ongoing digitalization in higher education, exam administration still largely relies on manual processes, which leads to inefficiency, heavy workloads, and an increased risk of errors. The study was conducted as a qualitative case study, combining semi-structured interviews with administrative staff and the development of a prototype. The prototype, designed according to principles of media design and user-centered design, integrates functions such as digital checkin and check-out, real-time updated participant lists, and automatic seat allocation. The findings indicate that automation can reduce repetitive tasks, strengthen traceability, and contribute to a more sustainable work environment. At the same time, organizational challenges emerge, including the need for training, anchoring, and readiness for change in order to ensure effective adoption of the systems. The study contributes to research on digitalization in higher education by highlighting both technical opportunities and human factors that influence the implementation of automated systems
Real-time evaluation of antibacterial efficacy using bioluminescent assays for Pseudomonas aeruginosa and Staphylococcus aureus
No full textThe emergence of antibiotic resistance necessitates effective strategies for evaluating antimicrobial agents. Bioluminescent bacteria, either naturally occurring or engineered with modified reporter genes like bacterial luciferase, provide real-time assessment of bacterial viability through light emission. We investigated the antibacterial effects of cefotaxime and doxycycline using bioluminescent strains of S. aureus and P. aeruginosa, combining optical density measurements with bioluminescence monitoring. Treatment with cefotaxime resulted in a significant reduction of the bioluminescent signal in P. aeruginosa compared to untreated controls, while doxycycline induced a delayed growth curve. Both antimicrobials demonstrated strong efficacy against S. aureus, as evidenced by decreased bioluminescence signals. Results from bioluminescence assays and classical minimum inhibitory concentration and minimum bactericidal concentration methods showed consistent alignment, validating the bioluminescence approach. This study demonstrates that bioluminescence-based methods offer a reliable, real-time alternative to traditional bacterial viability assays for evaluating antimicrobial efficacy
The heart of leadership. : A qualitative study from employees’ perspectives about affective experiences in leader-follower relationships across Sweden and Switzerland.
No full textThis research investigates what makes a leader-follower relationship feel “good” from the employee’s perspective. Grounded in a social constructivist approach, the study employs a qualitative methodology and draws on twelve in-depth interviews with knowledge workers from Sweden and Switzerland. Through thematic analysis, six key affective qualities were identified as central to high-quality leader-follower relationships: feeling heard and valued, feeling understood and secure, feeling trusted and respected, feeling acknowledged rather than overpowered, feeling supported, and feeling encouraged to grow. The findings also reveal that good relationships are built through mutual effort, informal connection, emotional intelligence, and a balance between closeness and professional boundaries. The interviewees’ expectations of the leader-follower relationship have changed over time. While most described these dynamics in face-to-face settings, many also stressed the importance of emotional presence in remote work contexts. Cultural attitudes towards hierarchy and work-life balance appeared to subtly shape how relationships were experienced in Swedish and Swiss workplaces. The findings suggest that effective leadership cannot be reduced to a fixed set of traits or styles. Instead, leadership is experienced as a deeply human and relational process. By placing employee voices at the centre, this study offers insight into the relational heart of leadership and encourages organisations to view leadership as not only a driver of performance, but also as a vital contributor to human connection and flourishing in the workplace
Från känsla till förtroende : Visuellt narrativ och retorik som strategi för social påverkan om alkoholkonsumtion
No full text"Vi måste kunna dansa i ramen" : en multietnografisk studie om vad undervisning kan bli i förskola
No full textThis thesis explores what teaching can be in preschool. In this thesis, I begin with a curious exploration to understand teaching based on the premises of preschool practice, where pedagogical relationships are central. This means that teaching is specifically examined through the lens of preschool daily life. The overall aim of the thesis is to contribute knowledge about what teaching can be in preschool, based on a multiethnographic study. The thesis is composed of three articles and a kappa. Two of the articles are based on empirical studies conducted in preschools using a video camera as a tool to generate empirical data. The third article is driven by a theoretical consideration of children's education in preschool. Linked to Kansanen's pedagogical levels, the thesis contributes knowledge about teaching in preschools that involve a movement between 1) action language, 2) theoretical language, and 3) meta-theoretical language and methodological analysis language. The kappa is driven by an overarching research question, which is answered through a movement between Kansanen's pedagogical levels, offering further contributions through the concept of didaktik multilanguaging. Teaching in preschool, both in practice and in theory, can be both out of step and in step, involving educators, children, and objects/content, and can be much more than just finding tools to measure knowledge. The thesis contributes to ongoing discussions on how teaching, based on the preschool's premises, can have the opportunity to become socially just through didaktik multilanguaging in preschool education. Social Justice as didaktik multilanguaging focuses on social justice as the relationship between languages between the child, educator, and content/object, between the action level and the (meta)theoretical level.Den här avhandlingen utforskar vad undervisning kan bli i förskola. I avhandlingen utgår jag från ett nyfiket utforskande för att förstå undervisning utifrån förskolepraktikens premisser där pedagogiska relationer är centrala. Det innebär specifikt att undervisning utforskas med förskolevardagen som utgångspunkt. Det övergripande syftet med avhandlingen är att bidra med kunskap om vad undervisning kan bli i förskola utifrån en multietnografisk studie. Avhandlingen grundas på tre artiklar och en kappa. Två av artiklarna grundas på empiriska studier genomförda i förskola med en videokamera som verktyg för att generera empiri. Avhandlingens tredje artikel drivs av ett teoretisk tänkande om yngre barns utbildning i förskola. Med anknytning till Kansanens pedagogiska nivåer, bidrar avhandlingen med kunskap om undervisning i förskola som innehar en rörelse mellan 1) aktionsspråk 2) teoretiskt språk och 3) meta-teoretiskt språk och metodologiskt analysspråk. Kappan drivs av en övergripande forskningsfråga som besvaras genom en rörelse mellan Kansanens pedagogiska nivåer vilket erbjuder ytterligare bidrag genom begreppet didaktiskt flerspråkande. Undervisning i förskola såväl i praktik som i teori kan vara både i takt och otakt och inkludera pedagoger, barn och objekt/innehåll och kan vara mycket mer än att hitta verktyg för att mäta kunskap. Avhandlingen bidrar till fortsatta diskussioner om hur undervisning utifrån förskolans premisser kan ha en möjlighet att bli socialt rättvis genom didaktiskt flerspråkande i förskolans utbildning. Social rättvisa som didaktiskt flerspråkande fokuserar social rättvisa som språkande relation mellan barn, pedagog och innehåll/objekt, mellan aktionsnivå och (meta)teoretisk nivå. Paper II in dissertation as manuscript, not included in the full text online. Incorrect e-ISBN in the printed version</p
Stress och copingstrategier hos universitetsstudenter i relation till onlineundervisning under och efter COVID-19-pandemin inom det europeiska området för högre utbildning: En översiktsstudie
No full textThis scoping review seeks to determine the current state of knowledge concerning the field of stress and coping strategies of university students studying online during and after the COVID-19 pandemic in the European Higher Education Area. As online learning has become more widely adopted in higher education, it is important to understand common stressors and coping strategies to offer effective and more tailored support to students. The three electronic databases used to conduct the literature search included ERIC (EBSCO), PsycINFO, and PubMed. I followed the Arksey and O’Malley’s (2005) six-step framework to guide my scoping review. Thirty-four peer-reviewed journal articles published in English within the EHEA between January 2020 and March 2025 were included in the review. Information on the country of study, characteristics of the sample, publication year, online teaching format (synchronous/asynchronous), stressors, and coping were extracted. The articles revealed that research was mostly conducted at the peak of the pandemic and focused on students who had to suddenly transition to emergency online learning due to COVID-19 restrictions. The studies included focused on students from multiple disciplines, with clinical and health-related programmes being commonly represented. Research spanned sixteen EHEA countries, with the UK and Poland leading in contributions, while Northern European and smaller countries were not represented. Studies measured stress and coping using various instruments. Stress sources were linked to COVID-19 disruptions, financial pressures, health concerns, inadequate study environments, academic challenges, and social issues, which were consistently felt across countries. Unique stressors emerged in the UK, where students struggled with high tuition fees and living costs, while Polish students were concerned about the Ukrainian-Russian conflict. Using the Skinner and Zimmer-Gembeck’s (2016) coping framework to analyse students’ narratives revealed that students used adaptive coping strategies, though pandemic-related helplessness was also evident. This study concludes by discussing gaps in research and offering some suggestions for future research.
Memory Box : Generative AI Awakens Distant Memories, Bringing the Past to Life: Smart Healthcare for Cognitive and Social Well-being
No full textAgainst the backdrop of global aging (WHO, 2023), this thesis addresses mental health challenges among the elderly—primarily cognitive decline (Small et al., 2011) and social isolation (Pinquart & Sorensen, 2001) through AI-assisted interaction. It explores how generative AI technologies (text to-text, text-to-image), combined with multimodal interfaces such as text-to-speech and speech-to-text, can support memory recall and social connection. Using Research through Design approach, the project integrates semi-structured interviews, cultural probes, and iterative prototyping to ground the design in user-centered needs. The resulting prototype, Memory Box, is an AI-powered memoir tool that enables elderly users to record, enhance, and share their memories through voice input or digitized handwritten diaries. These stories can be refined with AI and shared with family or community members to foster deeper engagement. The findings of the study highlight the potential of AI-generated stories to support older adults in creating meaningful experiences, enhancing memory recall, fostering social connection and reducing isolation. However, challenges persist, particularly related to fragmented recollections and differing levels of technological acceptance among users
Deep Learning Based Timeseries Modeling For Room Occupancy Estimation
No full textAccurate estimation of occupant presence in indoor environments is crucial for optimizing energy use, improving comfort, and ensuring safety. Traditional deep learning approaches often struggle to capture the complex and long-range temporal dependencies inherent in occupancy data. To address this challenge, we investigate a transformer-based model for analyzing time series data from non-intrusive environmental sensors, including light,sound, temperature, and CO2 concentration. Using the self-attention mechanism, the transformer architecture effectively models temporal patterns and outperforms conventional CNN and LSTM baselines. We systematically evaluate model performance across different sensor combinations, temporal feature configurations, and windowsizes. A key finding is that a minimal sensor setup using light and sound achieved superior performance compared to larger sensor arrays, demonstrating that strategic feature selection can enhance system accuracy while reducing complexity. The transformer model achieved the highest overall accuracy of 98.8 percent, confirming its robustness for occupancy estimation tasks. These results highlight the potential of transformer architectures to enable more intelligent, efficient, and scalable building management systems through accurate occupancy estimation
RetinaGate : A Gated Feature Pyramid Network for Improved Object Detection with SE-based Attention
No full textObject detection is a critical task in computer vision with wide-ranging applications, from autonomous driving tosurveillance systems. Despite notable progress, challenges such as detecting small objects, managing occlusions,and effectively integrating multiscale features persist. We propose RetinaGate, a novel object detection architec-ture that introduces a Gated Feature Pyramid Network (G-FPN) to adaptively fuse multi-scale features, enhancedby Squeeze-and-Excitation-based channel attention for improved accuracy. As a plug-and-play module, G-FPNcan be seamlessly integrated into existing detection models to enhance their accuracy. These enhancementsstrengthen the model’s capacity to capture fine-grained details and leverage contextual information more effec-tively. Experimental results on three benchmark datasets demonstrate that RetinaGate outperforms the baselineRetinaNet in terms of detection accuracy, particularly in challenging detection scenarios such as underwater
Non-invasive occupancy estimation and space utilization in smart buildings : Leveraging machine learning with PIR sensors and booking data
No full textOccupancy estimation in smart buildings is essential for optimizing resource usage and enhancing operational efficiency. Existing estimation methods predominantly rely on cameras or advanced sensor fusion techniques, which, while accurate, are often expensive, invasive, and raise privacy concerns. Additionally, these approaches frequently require extra hardware, increasing installation complexity and operational costs. A significant gap in the literature lies in the limited use of existing smart building infrastructure, such as detection systems and booking data, for people counting. This study addresses these limitations by exclusively utilizing two binary PIR sensors (in-door and in-room) and booking data. Since PIR sensors and booking systems are already integrated into most smart building infrastructures, leveraging these existing resources helps reduce costs and simplifies implementation. The primary goal is to estimate the number of people between each in-door sensor trigger using machine learning models by incorporating people counting levels and time thresholds. Among the evaluated machine learning algorithms, the Extra Trees Classifier delivered strong performance, achieving 68.5% accuracy when the estimated occupancy differed from the actual count by at most one person, and 81.56% with a tolerance of two. These results are based on periods when the room was occupied. When both occupied and unoccupied periods were included, the accuracy was 96.10% for ±1 tolerance. Moreover, incorporating booking data enhanced people counting accuracy by 4%. The study also explores the method's ability to identify underutilization and overutilization by comparing estimated occupancy with booking records and seating capacity, thereby supporting enhanced space management in smart buildings