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Enhancing assessment objectivity in dental education : transitioning from viva-voce to OSPEs
No full textAssessment in dental education plays a pivotal role in ensuring that fresh graduates possess the necessary competencies for safe clinical practice. Traditional viva-voce examinations have long been used to evaluate students’ depth of understanding and critical reasoning. However, concerns about examiner bias, inconsistent questioning, and grading have underscored the need for a more structured approach. This on-going study explores the advantages and disadvantages of transitioning from viva-voce assessments to Objective Summative Practical Examinations (OSPEs) at the University of Birmingham School of Dentistry. Beginning in the academic year 2022-23, standardized stations with objective scoring criteria were introduced, eliminating inconsistencies and fostering fairness across candidates. By providing equitable examination conditions and improving inter-rater reliability, OSPEs have enhanced transparency and aligned with competency-based education frameworks. This shift has boosted student confidence in the assessment process and reinforced the evaluation of practical skills rather than subjective reasoning. While logistical challenges and examiner calibration require continued refinement, OSPEs present a promising strategy for strengthening assessment objectivity. Future research should evaluate the impact of this transition on student performance and perceptions while comparing assessment reliability across various institutions. The adoption of OSPEs marks a progressive step toward ensuring fairness and accuracy in dental education assessments
Digital inequalities and subsistence entrepreneurship : An intersectional study of women in sub-Saharan Africa
No full textScience Engagement and Blue Peter Book Badge Applications
No full textDo you love books and reading? A quote from Dr. Seuss “The more that you read, the more things you will know. The more that you learn, the more places you’ll go.” Dr. Seuss (American children’s author, political cartoonist, illustrator, poet, animator, and filmmaker) This resource is designed to help you engage in activities related to science, technology, engineering, mathematics and medicine, and read fun science books that inspire you to apply for a Blue Peter Book badge. Blue Peter Book badges are for people aged between 5 – 15 years old. Blue Peter Book badge holders have free access to >200 attractions in the UK
The density of routinely collected neurology data depends on patient visit type: : an investigation using the Observational Medical Outcomes Partnership Common Data Model
No full textThe ethics of sustainable AI : Empowering responsible innovation in digital learning and beyond
No full textAs Artificial Intelligence (AI) becomes increasingly embedded in digital infrastructure, its dual role in driving climate-positive innovation and contributing to environmental and human costs demands urgent ethical scrutiny. This paper explores the ethical dimensions of sustainable AI, with a particular focus on its implications for e-learning, professional practice, and global climate strategy. Drawing on recent research and case studies, we examine how AI can support smart energy systems, climate resilience, and sustainable logistics—while also interrogating the hidden carbon footprint of model training, data centre operations, and exploitative human labour practices. We highlight the evolving responsibilities of IT professionals and educators in shaping AI systems that balance performance, scalability, and social value. The paper proposes actionable frameworks for ethical stewardship, including procurement guidelines, lifecycle analysis, and the integration of sustainability principles into curriculum design and professional development. By aligning AI with the UN Sustainable Development Goals and the Earth Charter, we advocate for a future where digital innovation supports both planetary and human flourishing. This presentation will be of interest to educators, technologists, and policy-makers seeking to embed ethical and environmental responsibility into the design, deployment, and teaching of AI systems
An Ontological Decolonization of the Global Innovation Discourse : a Neo-Gramscian Perspective
No full textOral evidence: "Transnational Repression in the UK", 5 Feb 2025 (Joint Committee on Human Rights, UK Parliament)
No full textForecasting intraday particle number size distribution : a functional time-series approach
Get PDFParticulate matter data now include various particle sizes, which often manifest as a collection of curves observed sequentially over time. When considering several distinct particle sizes, these curves form a high-dimensional functional time series observed over equally spaced and densely sampled grids. Whilst high dimensionality poses statistical challenges due to the curse of dimensionality, it also offers a rich source of information that enables detailed analysis of temporal variation across short-time intervals for all particle sizes. To model this complexity, we propose a multilevel functional time-series framework incorporating a functional factor model to facilitate one-day-ahead forecasting. To quantify forecast uncertainty, we develop a calibration approach and a split conformal prediction approach to construct prediction intervals. Both approaches are designed to minimise the absolute difference between empirical and nominal coverage probabilities using a validation dataset. Furthermore, to improve forecast accuracy as new intraday data become available, we implement dynamic updating techniques for point and interval forecasts. The proposed methods are validated through an empirical application to hourly measurements of particulate matter in several size categories in London
TSTMotion : Training-free Scene-aware Text-to-motion Generation
Get PDFText-to-motion generation has recently garnered significant research interest, primarily focusing on generating human motion sequences in blank backgrounds. However, human motions commonly occur within diverse 3D scenes, which has prompted exploration into scene-aware text-to-motion generation methods. Yet, existing scene-aware methods often rely on large-scale ground-truth motion sequences in diverse 3D scenes, which poses practical challenges due to the expensive cost. To mitigate this challenge, we are the first to propose a \textbf{T}raining-free \textbf{S}cene-aware \textbf{T}ext-to-\textbf{Motion} framework, dubbed as \textbf{TSTMotion}, that efficiently empowers pre-trained blank-background motion generators with the scene-aware capability. Specifically, conditioned on the given 3D scene and text description, we adopt foundation models together to reason, predict and validate a scene-aware motion guidance. Then, the motion guidance is incorporated into the blank-background motion generators with two modifications, resulting in scene-aware text-driven motion sequences. Extensive experiments demonstrate the efficacy and generalizability of our proposed framework. We release our code in \href{https://tstmotion.github.io/}{Project Page}