Enhancing student teacher self-efficacy beliefs to teach priority learners in New Zealand

The aim of this study was to explore the changes in student teacher efficacy beliefs for teaching priority learners over the course of a one-year postgraduate initial teacher education programme. The sample comprised 23 participants enrolled in the 2015 cohort in a pilot initial teacher education programme specifically tailored to enhance student teacher expertise to teach priority learners. Participants completed a specially designed and refined self-efficacy scale – Self-Efficacy with Diverse Learners: Student Teacher Scale – that targeted their efficacy beliefs about successfully promoting learning for priority learners at the start and at the end of their programme. Changes in efficacy beliefs were statistically measured and the findings indicated that student teacher efficacy beliefs for teaching priority learners had improved significantly over the course of their teacher education programme. In particular, the findings showed that their reported efficacy beliefs for implementing strategies for teaching English speakers of other languages, students with low socioeconomic status, and Māori learners had nearly doubled. Such findings have significant implications for teacher education reforms that aim to enhance student teacher adaptive expertise and in so doing, assist with the long-term goal of achieving more equitable educational outcomes in New Zealand. &nbsp

Enhancing student teacher self-efficacy beliefs to teach priority learners in New Zealand

The aim of this study was to explore the changes in student teacher efficacy beliefs for teaching priority learners over the course of a one-year postgraduate initial teacher education programme. The sample comprised 23 participants enrolled in the 2015 cohort in a pilot initial teacher education programme specifically tailored to enhance student teacher expertise to teach priority learners. Participants completed a specially designed and refined self-efficacy scale – Self-Efficacy with Diverse Learners: Student Teacher Scale – that targeted their efficacy beliefs about successfully promoting learning for priority learners at the start and at the end of their programme. Changes in efficacy beliefs were statistically measured and the findings indicated that student teacher efficacy beliefs for teaching priority learners had improved significantly over the course of their teacher education programme. In particular, the findings showed that their reported efficacy beliefs for implementing strategies for teaching English speakers of other languages, students with low socioeconomic status, and Māori learners had nearly doubled. Such findings have significant implications for teacher education reforms that aim to enhance student teacher adaptive expertise and in so doing, assist with the long-term goal of achieving more equitable educational outcomes in New Zealand. &nbsp

Non-parametric estimation of geographical relative risk functions : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand

The geographical relative risk function is a useful tool for investigating the spatial distribution of disease based on case and control data. The most common way of estimating this function is using the ratio of spatial kernel density estimates constructed from the locations of cases and controls respectively. This technique is known as the density ratio method. The performance of kernel density estimators depends on the choice of kernel and the smoothing parameter (bandwidth). The choice of kernel is not critical to the statistical performance of the method but the bandwidth is crucial. Di erent bandwidth selectors such as least squares cross validation (LSCV) and likelihood cross validation (LCV) are chosen to control the degree of smoothing during the computation of the density ratio estimator. An alternative way of estimating this relative risk function is local linear regression approach. This deserves consideration since the density ratio estimator can be less natural when the relative risk has a global trend, as one might expect to see when there is a line source of risk such as a polluted river or a road. The use of local linear regression for estimation of log relative risk functions per se has not been examined in any detail in the literature, so our work on this methodology is a novel contribution. A detailed account of local linear approach in the estimation of log relative risk function is provided, consisting of an analysis of asymptotic properties and a method for computing tolerance contours to emphasize the regions of signi cantly high risk. Data driven bandwidth selectors for the local linear method including a novel plug-in methodology is examined.A simulation study to compare the performance of density ratio and local linear estimators using a range of data-driven bandwidth selectors is presented. The analysis of two speci c data sets is examined. The estimation of the spatial relative risk function is extended to spatio-temporal estimation through the use of suitable temporal kernel functions, since time-scale is an important consideration when estimating disease risk. The extended version of the kernel density estimation is applied here to compute the unknown densities of the spatio-temporal relative risk function. Next we investigate the time derivatives of the space-time relative risk function to see how the disease change with time. This discussion provides novel contributions with the introduction to time derivatives of the relative risk function as well as asymptotic methods for the computation of tolerance contours to highlight subregions of signi cantly elevated risk. LSCV and subjective bandwidths are used to compute these estimators since it performs well in density ratio method. The analysis on a real application to foot and mouth disease (FMD) of 1967 outbreak is employed to illustrate these estimators. The relative risk function is investigated when the data include a spatially varying covariate. The discussion produces the introduction to generalized relative risk function in two ways and also asymptotic properties of estimators for both cases as novel works. Generalized kernel density estimation is used to replace the unknown densities in the relative risk function. Asymptotic theories are used to compute tolerance contours to identify the areas which show high risk. LSCV bandwidth selector is described in this estimation process providing the implicit formulae. We illustrate this methodology on data from the 2001 outbreak of FMD in the UK, examining the e ect of farm size as a covariate

Enhancing student teacher self-efficacy beliefs to teach priority learners in New Zealand

The aim of this study was to explore the changes in student teacher efficacy beliefs for teaching priority learners over the course of a one-year postgraduate initial teacher education programme. The sample comprised 23 participants enrolled in the 2015 cohort in a pilot initial teacher education programme specifically tailored to enhance student teacher expertise to teach priority learners. Participants completed a specially designed and refined self-efficacy scale – Self-Efficacy with Diverse Learners: Student Teacher Scale – that targeted their efficacy beliefs about successfully promoting learning for priority learners at the start and at the end of their programme. Changes in efficacy beliefs were statistically measured and the findings indicated that student teacher efficacy beliefs for teaching priority learners had improved significantly over the course of their teacher education programme. In particular, the findings showed that their reported efficacy beliefs for implementing strategies for teaching English speakers of other languages, students with low socioeconomic status, and Māori learners had nearly doubled. Such findings have significant implications for teacher education reforms that aim to enhance student teacher adaptive expertise and in so doing, assist with the long-term goal of achieving more equitable educational outcomes in New Zealand. &nbsp

A first‐order, ratio‐based nonparametric separability test for spatiotemporal point processes

Testing whether the intensity function of a spatiotemporal point process is sep- arable should be one of the first steps in the analysis of any observed pattern. Under separability, the risk of observing an event at time t is spatially invariant, that is, the ratio between the intensity functions of the spatiotemporal point pro- cess and its spatial marginal does not depend on the spatial location of events. Considering this property, this work proposes testing separability through a regression test that checks the dependence of the ratio function on the spa- tial locations. To implement the test, we introduce a kernel estimator of the log-ratio function and a cross-validation bandwidth selector. The simulation studies conducted to analyze the performance of the test point out the need to use a permutation test to calibrate the null distribution. Comparison with non- parametric separability tests currently available reported that the no-effect test provides a better calibration under the null hypothesis, and it is competitive in power with the current tests under the alternative hypothesis. The perfor- mance of the test is also illustrated throughout its application to the analysis of the spatiotemporal patterns of wildfires registered in Galicia (NW Spain) during 2006