2 resultados para Empirical training
em CentAUR: Central Archive University of Reading - UK
Resumo:
Improving the quality of teaching is an educational priority in Kenya, as in many developing countries. The present paper considers various aspects of in-service education, including views on the effectiveness of in-service, teacher and headteacher priorities in determining in-service needs and the constraints on providing in-service courses. These issues are examined though an empirical study of 30 secondary headteachers and 109 teachers in a district of Kenya. The results show a strong felt need for in-service provision together with a firm belief in the efficacy of in-service in raising pupil achievement. Headteachers had a stronger belief in the need for in-service for their teachers than did the teachers themselves. The priorities of both headteachers and teachers were dominated by the external pressures of the schools, in particular the pressures for curriculum innovation and examination success. The resource constraints on supporting attendance at in-service courses were the major problems facing headteachers. The results reflect the difficulties that responding to an externally driven in-service agenda creates in a context of scarce resources.
Resumo:
A significant challenge in the prediction of climate change impacts on ecosystems and biodiversity is quantifying the sources of uncertainty that emerge within and between different models. Statistical species niche models have grown in popularity, yet no single best technique has been identified reflecting differing performance in different situations. Our aim was to quantify uncertainties associated with the application of 2 complimentary modelling techniques. Generalised linear mixed models (GLMM) and generalised additive mixed models (GAMM) were used to model the realised niche of ombrotrophic Sphagnum species in British peatlands. These models were then used to predict changes in Sphagnum cover between 2020 and 2050 based on projections of climate change and atmospheric deposition of nitrogen and sulphur. Over 90% of the variation in the GLMM predictions was due to niche model parameter uncertainty, dropping to 14% for the GAMM. After having covaried out other factors, average variation in predicted values of Sphagnum cover across UK peatlands was the next largest source of variation (8% for the GLMM and 86% for the GAMM). The better performance of the GAMM needs to be weighed against its tendency to overfit the training data. While our niche models are only a first approximation, we used them to undertake a preliminary evaluation of the relative importance of climate change and nitrogen and sulphur deposition and the geographic locations of the largest expected changes in Sphagnum cover. Predicted changes in cover were all small (generally <1% in an average 4 m2 unit area) but also highly uncertain. Peatlands expected to be most affected by climate change in combination with atmospheric pollution were Dartmoor, Brecon Beacons and the western Lake District.