Problems faced by beginning principals

The job of a principal is becoming more demanding and more critical each year. Principals are asked to undertake huge challenges and to succeed regardless of what obstacles lie ahead. The purpose of this study was to identify which Administrative Task Areas and Specific Task Areas caused the most difficulties for first- and second-year principals.^ A survey was taken of first- and second-year principals in Dade County, Florida. These beginning principals rated their level of proficiency for each administrative task area and each specific task within those areas. Participants rated their perceptions on a scale from one to four. The data were analyzed based on frequency distributions, percentages, means, and standard deviations.^ Beginning principals perceived themselves as least proficient in the administrative task areas of management and personnel duties. They believed their strongest areas were curriculum and instruction and school-community relations. Within these areas, the specific administrative task areas identified as most problematic were identifying proper procedures for construction in the schools, visiting classrooms to help teachers improve instruction, awareness of issues related to school law, establishing accounting procedures for the school's internal funds, and procedures for dismissing incompetent staff members.^ Many beginning principals surveyed volunteered to make recommendations for future beginning principals. Of these recommendations, the most popular responses addressed obtaining more experience with the budget and internal funds prior to becoming a principal. In addition, there was a strong need for more training dealing with school personnel and the importance of networking with a veteran principal.^ The principal training programs for five of the largest school districts in Florida were reviewed. These programs were found to incorporate a vast amount of the recommendations included in the literature. Florida is moving in the right direction toward excellence in the public schools. ^

Cultural influence and learning to teach: Beginning teachers' perceptions of teacher preparation programs and school support systems

Beginning teachers in the field of English Language Arts and Reading are responsible for providing literacy instruction to students. Teachers need a broad background in teaching reading, writing, listening, speaking, and viewing, as well as critical thinking. In secondary schools in particular, beginning English Language Arts and Reading teachers are also faced with the challenge of preparing students to be proficient enough readers and writers to meet required State standards. Beginning teachers must navigate compelling challenges that exist during the first years of teaching. The school support systems available to new teachers are an integral part of their educational development. ^ This qualitative study was conceptualized as an in-depth examination of the experiences and perceptions of eight beginning teachers. They represented different racial/ethnic groups, attended different teacher preparation programs, and taught in different school cultures. The data were collected through formal and informal interviews and classroom observations. A qualitative system of data analysis was used to examine the patterns relating to the interrelationship between teacher preparation programs and school support systems. ^ The experiences of the beginning teachers in this study indicated that teacher education programs should provide preservice teachers with a critical knowledge base for teaching literature, language, and composition. A liberal arts background in English, followed by an extensive program focusing on pedagogy, seems to provide a thorough level of curriculum and instructional practices needed for teaching in 21st century classrooms. The data further suggested that a school support system should pair beginning teachers with mentor teachers and provide a caring, professional environment that seeks to nurture the teacher as she/he develops during the first years of teaching. ^

Beginning to Explore Dashboard Dining

It can be nutritious and healthy if done right. Fruits and vegetables, a granola bar, smoothie, or some fresh squeezed Florida orange juice would be good choices. On the other hand, it can poison you. Perishable protein and dairy products must be packed in a well- insulated cooler with plenty of ice and a refrigerator thermometer kept inside to en-sure the food stays below 40 degrees Fahrenheit. If you are not completely safe, it can kill you. According to Hagerty Insurance of Traverse City, Michigan, the top ten worst foods to consume are coffee, hot soups, tacos, chili, juicy hamburgers, fried chicken, any barbecued food, filled doughnuts, soft drinks, and chocolate. (see Lisa Chin, 2003) It simply takes a sudden scalding spill, an unexpected splash, or dripping condiments, any of which demand your immediate attention, to become an instant fatality.

The effects of an individualized clinical reading program on kindergarten and beginning grade one at -risk students: A longitudinal study

This was a longitudinal study that investigated the effects of an early intervention program which was implemented at the beginning of formal reading instruction and used individual clinical instruction with at-risk students. A total of 37 private school students were divided into three cognitive ability groups and evaluated over a three year period using the reading comprehension and study skills sections of the Stanford Achievement Tests (1982) administered annually. At-risk students were matched with a normal peer group for gender, cognitive ability, and time at school. Results showed there were no significant differences in the reading comprehension scores for program and non-program students. However, the at-risk group showed significantly lower scores on the study skills section at the end of grade three. These results indicate that early reading intervention for at-risk students promotes compensation and helps develop processes for adequate reading comprehension but these students continue to have weaker linguistic abilities. ^

Perceptions of beginning teachers in Florida regarding their preparation as it relates to the Florida essential generic competencies

The purpose of the study is to investigate how beginning teachers in the state of Florida perceive their preparation to demonstrate the 27 Florida Essential Generic Competencies. The basic research question of this study was: How do beginning teachers perceive their level of preparation regarding their implementation of the Florida Essential Generic Competencies? This study identified and categorized the perceived degree of preparation for each of the competencies. Also, elementary, middle, and high school beginning teachers were compared to find significant differences and similarities in their perception of their preparation. A comparison was also done for graduates from in-state versus out-of-state and private versus public institutions. A survey developed in collaboration with the Department of Education, Florida State University, members of the Professional Orientation Program (POP) Coordinators, and the Project Director of Program Review in the College of Education at the University of South Florida, was sent to 5,076 beginning teachers. A total of 1,995 returned the survey in February of 1993. The Multivariate Analysis of Variance (MANOVA) procedure was used (Alpha = .05). Statistical analysis of the data involved a comparison of the different groups of beginning teachers by school level and kind of graduating institutions. The dependent variables analyzed were the responses to all items representing the generic competencies. The study identified and categorized the degree of preparation for each competency. The competencies receiving the lowest ratings for degree of preparation were: integrate computers in instruction; manage situations involving child abuse and/or neglect; severe emotional stress; alcohol and drug abuse. The Wilkes lambda and the Hotellings multivariate tests of significance were used to examine the differences among the groups. The competency items were further analyzed by a univariate F test. Results indicated that: (1) significant differences were found in nine competency items in which elementary teachers felt better prepared than middle and high school beginning teachers, (2) graduates from a Florida teacher education program felt they were better prepared in demonstrating the competencies than those from out-of-state schools, and (3) no significant difference was found in the perceptions of those who graduated from public versus private institutions. Based on the findings of this study, the following recommendations are made: (1) Florida's institutions responsible for teacher preparation programs need to focus on those competencies receiving the lowest ratings, (2) Districts should provide an orientation program for out-of-state beginning teachers, and (3) The survey instrument should be used annually to evaluate teacher education programs.

Strong inference from transect sign surveys : combining spatial autocorrelation and misclassification occupancy models to quantify the detectability of a recovering carnivore

Funding — Forest Enterprise Scotland and the University of Aberdeen provided funding for the project. The Carnegie Trust supported the lead author, E. McHenry, in this research through the award of a tuition fees bursary.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis

The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

An analysis of beginning mentors’ critical incidents in English post-compulsory education: navigating stormy waters

This qualitative study explores the barriers and dilemmas faced by beginning and novice mentors in post-compulsory education in the southeast of England. It analyses critical incidents (Tripp, 2012) taken from the everyday practice of mentors who were supporting new teachers and lecturers in the southeast of England. It categorises different types of critical incidents that mentors encountered and describes the strategies and rationales mentors used to support mentees and (indirectly) their learners and colleagues. The study explores ways in which mentors' own values, beliefs and life experiences affected their mentoring practice.  Methodology As part of a specialist master’s-level professional development module, 21 mentors wrote about two critical incidents (Tripp, 2012) taken from their own professional experiences, which aimed to demonstrate their support for their mentee’s range of complex needs. These critical incidents were written up as short case studies, which justified the rationale for their interventions and demonstrated the mentors' own professional development in mentoring. Critical incidents were used as units of analysis and categorised thematically by topic, sector and mentoring strategies used. Findings The research demonstrated the complex nature of decision-making and the potential for professional learning within a mentoring dyad. The study of these critical incidents found that mentors most frequently cited the controversial nature of teaching observations, the mentor’s role in mediating professional relationships, the importance of inculcating professional dispositions in education, and the need to support new teachers so that they can use effective behaviour management strategies. This study contributes to our understanding of the central importance of mentoring for professional growth within teacher education. It identifies common dilemmas that novice mentors face in post-compulsory education, justifies the rationale for their interventions and mentoring strategies, and helps to identify ways in which mentors' professional development needs can be met. It demonstrates that mentoring is complex, non-linear and mediated by mentors’ motivation and values.

The beginning of tourism in La Palma Island: Sixty years of the XX century

The sixties was a time of great interest for tourism development on the La Palma island. Various actions of public and private, as the policy of building a new airport, various tourist resorts, the tourism plan of 1968 or insular government also creating public entity "La Palma, Tourism SA” in 1969, will be the basis for future development of tourism on the island and will result push for private investment in this economic sector. Indeed, in the sixties, private investors had opened two hotels, while at least three others over a hundred beds each, weren´t finished.