Energy performance of traditionally constructed dwellings in Scotland

This research was commissioned by Historic Scotland, to ascertain what, if any, characteristics specific to traditionally constructed (stone masonry) dwellings in Scotland could impact on the result of an energy assessment. The research uses five case study dwellings, whose energy consumption has been assessed using three separate calculation methodologies: the two Government-accredited steady state methodologies SAP and RdSAP, in addition to a dynamic simulation using the IES Virtual Environment. The research finds primarily that traditionally constructed dwellings use more energy than the UK average, and that certain aspects of the steady state calculation methodologies give erroneous results. These errors are either specific to stone masonry dwellings through application of assumptions with respect to thermal storage and movement, or specific to Scottish dwellings through application of UK average climate variables. Furthermore, there are significant challenges to using dynamic simulations for these dwellings, which may not outweigh the benefits of perceived accuracy by the occupant. Therefore, the research concludes that the steady state methodologies should continue to be utilised, but with the awareness that the methodologies have limitations

Playing the system: incentives to "game" and educational ethics in school examination entry policies in England

There has been a period of intense policy change involving GCSE examinations in England, proposed partly in response to schools using tactics to maximise performance against accountability measures. The reforms included a change to linear rather than modular entry, removing partial re-sits, and limiting early and multiple entry to examinations by changing school accountability measures. We present new empirical data from interviews conducted with senior teachers at 15 schools. The focus of these interviews has been in the English and mathematics departments; the first subjects to be examined in the new specifications. The data suggest that teachers acknowledge this practice of ‘gaming’ but only as something ‘other’ schools did. Whilst the reforms have now allowed for the system to be viewed as a more level playing field, teachers still describe a constant tension in the decisions surrounding examination entry. They describe the desire for a balance that is not just between school and student outcomes, but also between different outcomes such as motivation, performance, and engagement. Tensions arise between these outcomes when entry choices are being made

An evaluation of the Writing Assessment Measure (WAM) for children's narrative writing

AbstractThe study evaluated the reliability and validity of the Writing Assessment Measure (WAM), developed to reflect the skills which children of different abilities are expected to achieve in written expression, as part of the National Curriculum guidelines in England and Wales. The focus was on its potential use in investigations of children's written narrative in order to inform and target related interventions. The study involved 97 children aged 7–11 from one urban primary school in England. Prompt 1 was administered to all the children in their classrooms together with a standardised written expression test. After three weeks, the same procedure was followed and Prompt 2 was administered. Statistical analyses of the reliability and validity of the instrument showed that it is consistent over time and can be scored reliably by different raters. Content validity of the instrument was demonstrated through inspection of item total correlations which were all significant. Analyses for concurrent validity showed that the instrument correlates significantly with the Wechsler Written Expressive Language sub-test. Significant differences between children of different age and writing skill were also found. The findings indicate that the instrument has potential utility to professionals assessing children's writing

Educación universitaria en tiempos de pandemia

La pandemia del COVID-19 constituye uno de los desafíos más importantes a los que se enfrentó la salud pública mundial en los últimos cien años. El aislamiento social y la cuarentena obligatoria han sido las políticas aplicadas por el gobierno de la nación. Esto ha conllevado, entre otras medidas, la suspensión de las clases presenciales en todos los niveles educativos. Las distintas universidades del país decidieron continuar el ciclo lectivo 2020 de manera virtual. En este contexto, nos surgieron una serie de interrogantes ligados específicamente a les estudiantes de la carrera de Antropología de la UNLP, que involucraron desde las distintas modalidades que se han elegido para el desarrollo virtual de sus clases, hasta la conectividad y los dispositivos necesarios. Reflexionamos también sobre cómo se construyeron las relaciones entre docentes y estudiantes y entre les mismes estudiantes debido a la ausencia del espacio presencial áulico. Además, sin perder de vista que dadas las circunstancias, las clases implicaron el ámbito doméstico, nos preguntamos, cómo estuvo construido este espacio, dónde habitaban les estudiantes y con quiénes transitaron el aislamiento. Planteamos como objetivo analizar las modalidades de enseñanza, y cómo éstas fueron percibidas por les estudiantes de Antropología de la FCNyM de la UNLP. De esta manera, propusimos un estudio cualitativo, donde la recolección de datos se realizó a través de una encuesta contestada voluntariamente por distintes compañeres de la carrera.Facultad de Ciencias Naturales y Muse

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Breast Radiation Therapy; Machine-Learning Prediction; Acute DesquamationRaditeràpia de mama; Predicció d'aprenentatge automàtic; Descamació agudaRadioterapia de mama; Predicción de aprendizaje automático; Descamación agudaPurpose Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan