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Correspondence on “The Stockholm Convention at a Crossroads: Questionable Nominations and Inadequate Compliance Threaten Its Acceptance and Utility”
No full textThe development of preservice English teachers’ TPACK in a course based on TPACK
Get PDFThis qualitative case study investigates how a Technological Pedagogical Content Knowledge (TPACK) course impacts the development of preservice English teachers’ TPACK, as well as their perceptions and attitudes towards technology integration in English Language Teaching (ELT). While much of the existing research on TPACK development in preservice teacher education focuses on generic technology courses, this study addresses the gap by exploring how content-specific courses can enhance TPACK in preservice teachers. To achieve this, the course was designed using TPACK Design-Based Learning (TPACKDBL) principles (Baran & Uygun, 2016), aimed specifically at exploring the TPACK development and experiences of preservice English teachers. Despite numerous studies examining TPACK in preservice education, concrete evidence of TPACK development within content-specific contexts remains scarce. Therefore, this study collected and analysed self-report data (semi-structured interviews, peer feedback, reflective journals, and macroteaching application reports), observations (microteaching), and performance assessments (design activities) to explore the development of TPACK and perceptions of technology in a course offered at a higher education institution in Israel. The data analysis resulted in five key themes: Design Activities, Experiences in Technology-Enhanced Teaching, Integrating Technology to Support Pedagogical Practices in English Language Instruction, Perceptions and Attitudes Towards Integration of Technology into Teaching, and Perceived Value of the Course. The findings demonstrate that the preservice teachers developed TPACK by making connections between technology, content, and pedagogy. In addition, the study provides insights into preservice teachers’ perceptions and attitudes towards technology integration in ELT and the TPACK-based course. This study contributes to enhancing teaching practices and informs the design of technology courses in preservice teacher education, recommending that such courses be tailored to subject-specific content using TPACK-DBL principles to develop preservice teachers’ skills in the design and implementation of technology
IFIP Task Force on ‘Sustaining relevant digital inclusive education for young people (5-18 years of age)’ : Northern Ireland Case Study
Get PDFThe International Federation for Information Processing (IFIP) was founded in 1960 under the auspices of the United Nations Educational, Scientific and Cultural Organization (UNESCO), as a federation for societies working in information processing. Set up by the President of IFIP in 2022, the work of an IFIP Task Force has sought to gain an international perspective to support the UN, UNESCO, ITU, affiliated organisations, national societies, regional and local organisations in moving forward a focus on ‘Sustaining relevant digital inclusive education for young people (5-18 years of age)’. Interim IFIP Task Force findings identified five key sustaining factors that the Task Force believed warranted specific and particular attention: aspiration; diversity, inclusion, the digital divide and the under-represented; computational thinking and its links to problem-solving; developing teacher practices; and short- and long-term plans and actions . Using these five key sustaining factors as a framework, the details of the associated case study reported in this document show how these relate strongly to specific UN Sustainability Development Goals (SDGs) . As Northern Ireland (NI) can demonstrate how a long-term strategy and development has led to successes, and how challenges have been tackled over the period of the development of relevant digital inclusive education in NI over a sustained, long-term (in 2024) 35-year period from an initial start in 1989 with the Computerised Local Administration System for Schools (CLASS) project, a case study of how this has been done is reported here. Chapter 3 of this document offers a timeline of educational technology being implemented across Northern Ireland’s schools, to position the IFIP case study in the local context. Chapters 4 to 8 detail how the NI education technology development relates to the IFIP Task Force’s key sustaining factors. Chapter 9 provides an overview of resourcing involved, from a non-cost perspective, as costs need to be considered nationally and do not necessarily easily relate from one nation’s costs to another. The resources described in Chapter 9 are concerned with personnel involvement, personnel time, and physical and management resources. It should be noted that this case study is concerned with details that relate to the focus of the IFIP Task Force for 5-18-year-old young people, whilst, in NI, education concerns a wider 4-18-year-old age group
Transformations and the dynamics of memory : Gladstone and the Phoenix Park Murders
No full textIn this study, we explore how the Phoenix Park murders were written about in public and private discourse, utilising the Nineteenth Century Newspaper Corpus, personal diaries and historiography. With the use of social actor analysis (van Leeuwen, 2008), we examine how events underwent ‘transformations’ as they moved from reality to record, and how over time these records worked to shape the dynamics of memory, particularly in relation to notions of accountability. Gladstone was blamed by The Times for allowing the murders to take place but, by focussing on personal relationships, the Liberal press portrayed events far more sympathetically. Soon after Gladstone’s death, an influential biography by his friend, John Morley, worked to prove that Gladstone’s reputation was beyond reproach
Trajectories of legal work in the context of machine learning AI : conceptualising mediated evolution
Get PDFThis paper explores the impacts of machine learning (ML), as one form of artificial intelligence, on legal work by examining three questions. First, it considers trajectories and how ML is being used in legal work. Actually existing use cases are examined to reveal how ML is changing legal work. Second, it considers questions about the barriers that are standing in the way of different trajectories, with the more rapid adoption of tried and tested forms of ML and some of the more radical changes that have been predicted being contingent on a range of factors. Third, this paper considers how evolution might change spaces of legal work and the legal profession. It examines both what ML might do to reconfigure the role of the lawyer within law firms and other spaces, and how lawyers might respond to this as the professional project adapts to the challenge of artificial intelligence. Through the analysis the paper develops the concept of mediated evolution which is a way of conceptualising change in legal work that is material and meaningful, but which is also path dependent and non-linear and thus needs to be understood through situated analysis of the enactment in practice of chang
Oncology professionals’ perceptions and recommendations to improve wellbeing and health at work in times of crisis : Qualitative thematic analysis from the ESMO Resilience Task Force survey series
Get PDFBACKGROUND: The European Society for Medical Oncology (ESMO) Resilience Task Force (RTF) was established to address burnout and well-being issues among oncology professionals. In this article, we present findings on shared perceptions and recommendations to support and improve oncology professionals' well-being and health at work. MATERIALS AND METHODS: Inductive thematic analysis of qualitative responses from three global ESMO RTF surveys (2020-2021) was conducted using Braun and Clarke's six-step approach. Open-ended questions elicited suggestions, including descriptions of 'pleasant physical working conditions' in the third survey. Respondents (n = 989) were gender-balanced, from 90 countries, with half practising in Europe. Most were of white ethnicity, worked in medical oncology, and had over 10 years of experience. RESULTS: Six main themes described help and support needs from oncology professionals: training, education, information and learning; well-being; activism and advocacy; financial support; safety; and opportunities and career. Six additional themes described factors contributing to a 'pleasant physical working environment': physical working environment; working conditions and job role; safety; well-being and coping; working relations and support from others; and career and professional development. CONCLUSIONS: This is the largest global qualitative analysis of oncology professionals' needs during the coronavirus disease 2019 (COVID-19) pandemic, offering actionable recommendations for ESMO and other stakeholders to address work-related issues. Addressing these needs can foster resilience, improve working conditions, and promote better health and well-being
Reliability of Matching AMPERE Field‐Aligned Current Boundaries With SuperDARN Lower Latitude Ionospheric Convection Boundaries During Geomagnetic Storms
Get PDFHigh‐latitude ionospheric convection is a useful diagnostic of solar wind‐magnetosphere interactions and nightside activity in the magnetotail. For decades, the high‐latitude convection pattern has been mapped using the Super Dual Auroral Radar Network (SuperDARN), a distribution of ground‐based radars which are capable of measuring line‐of‐sight (l‐o‐s) ionospheric flows. From the l‐o‐s measurements an estimate of the global convection can be obtained. As the SuperDARN coverage is not truly global, it is necessary to constrain the maps when the map fitting is performed. The lower latitude boundary of the convection, known as the Heppner‐Maynard boundary (HMB), provides one such constraint. In the standard SuperDARN fitting, the HMB location is determined directly from the data, but data gaps can make this challenging. In this study we evaluate if the HMB placement can be improved using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), in particular for active time periods when the HMB moves to latitudes below 55 ° . We find that the boundary as defined by SuperDARN and AMPERE are not always co‐located. SuperDARN performs better when the AMPERE currents are very weak (e.g., during non‐active times) and AMPERE can provide a boundary when there is no SuperDARN scatter. Using three geomagnetic storm events, we show that there is agreement between the SuperDARN and AMPERE boundaries but the SuperDARN‐derived convection boundary mostly lies ∼ 3 ° equatorward of the AMPERE‐derived boundary. We find that disagreements primarily arise due to geometrical factors and a time lag in expansions and contractions of the patterns
CODE-ACCORD : A Corpus of building regulatory data for rule generation towards automatic compliance checking
No full textAutomatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland. Only the self-contained sentences, which express complete rules without needing additional context, were considered as they are essential for ACC. Each sentence was manually annotated with entities and relations by a team of 12 annotators to facilitate machine-readable rule generation, followed by careful curation to ensure accuracy. The final dataset comprises 4,297 entities and 4,329 relations across various categories, serving as a robust ground truth. CODE-ACCORD supports a range of ML and Natural Language Processing (NLP) tasks, including text classification, entity recognition, and relation extraction. It enables applying recent trends, such as deep neural networks and large language models, to ACC
Interpretable adversarial example detection via high-level concept activation vector
Get PDFDeep neural networks have achieved amazing performance in many tasks. However, they are easily fooled by small perturbations added to the input. Such small perturbations to image data are usually imperceptible to humans. The uninterpretable nature of deep learning systems is considered to be one of the reasons why they are vulnerable to adversarial attacks. For enhanced trust and confidence, it is crucial for artificial intelligence systems to ensure transparency, reliability, and human comprehensibility in their decision-making processes as they gain wider acceptance among the general public. In this paper, we propose an approach for defending against adversarial attacks based on conceptually interpretable techniques. Our approach to model interpretation is on high-level concepts rather than low-level pixel features. Our key finding is that adding small perturbations leads to large changes in the model concept vector tests. Based on this, we design a single image concept vector testing method for detecting adversarial examples. Our experiments on the Imagenet dataset show that our method can achieve an average accuracy of over 95%. We provide source code in the supplementary material