Working with Student Teachers: A Mixed Methods Study to Examine the Roles and Self-Identified Dispositions of Cooperating Teachers

A critical component of teacher education is the field experience during which candidates practice under the supervision of experienced teachers. Programs use the InTASC Standards to define the requisite knowledge, skills, and dispositions for teaching. Practicing teachers are familiar with the concepts of knowledge and skills, but they are less familiar with dispositions. Practicing teachers who mentor prospective teachers are underrepresented in the literature, but they are critical to teacher preparation. The research goals were to describe the self-identified dispositions of cooperating teachers, identify what cooperating teachers consider their role in preparing prospective teachers, and explain challenges that cooperating teachers face. Using a mixed methods design, I conducted a quantitative survey followed by a qualitative case study. When I compared survey and case study data, cooperating teachers report possessing InTASC critical dispositions described in Standard 2: Learning Differences, Standard 3: Learning Environments, and Standard 9: Professional Learning and Ethical Practice, but not Standard 6: Assessment and Standard 10: Leadership and Collaboration. Cooperating teachers assume the roles of modeler, mentor and advisor, and informal evaluator. They explain student teachers often lack skills and dispositions to assume full teaching responsibilities and recommend that universities better prepare candidates for classrooms. Cooperating teachers felt university evaluations were not relevant to teaching reality. I recommend modifying field experiences to increase the quantity and duration of classroom placements. I suggest further research to detail cooperating teacher dispositions, compare cooperating teachers who work with different universities, and determine if cooperating teacher dispositions influence student teacher dispositions.

Structural characterization of interaction of K11 and K63 mixed-linkage polyubiquitin chains with the tUIMs of epsin1

Ubiquitylation or covalent attachment of ubiquitin (Ub) to a variety of substrate proteins in cells is a versatile post-translational modification involved in the regulation of numerous cellular processes. The distinct messages that polyubiquitylation encodes are attributed to the multitude of conformations possible through attachment of ubiquitin monomers within a polyubiquitin chain via a specific lysine residue. Thus the hypothesis is that linkage defines polyubiquitin conformation which in turn determines specific recognition by cellular receptors. Ubiquitylation of membrane surface receptor proteins plays a very important role in regulating receptor-mediated endocytosis as well as endosomal sorting for lysosomal degradation. Epsin1 is an endocytic adaptor protein with three tandem UIMs (Ubiquitin Interacting Motifs) which are responsible for the highly specific interaction between epsin and ubiquitylated receptors. Epsin1 is also an oncogenic protein and its expression is upregulated in some types of cancer. Recently it has been shown that novel K11 and K63 mixed-linkage polyubiquitin chains serve as internalization signal for MHC I (Major Histocompatibility Complex I) molecule through their association with the tUIMs of epsin1. However the molecular mode of action and structural details of the interaction between polyubiquitin chains on receptors and tUIMs of epsin1 is yet to be determined. This information is crucial for the development of anticancer therapeutics targeting epsin1. The molecular basis for the linkage-specific recognition of K11 and K63 mixed-linkage polyubiquitin chains by the tandem UIMs of the endocytic adaptor protein epsin1 is investigated using a combination of NMR methods.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.