Development of Ambient PM 2.5 Management Strategies

INE/AUTC 11.2

Engagement Levels During Implementation of Co-Teaching Models

Student engagement in the classroom is a priority for educators and policy makers because disengaged students are more likely to perform poorly in school. Students with disabilities, particularly those with high-incidence disabilities, are a subset of that population of potentially disengaged and definitely poor-performing students. When attending school, they are served increasingly in inclusive, co-taught settings, allowing for inquiry into engagement practices in these instructional arrangements. One purpose of this study was document levels of student engagement for six commonly described co-teaching models implemented in a teacher education course for both special education and general education preservice teacher candidates and inservice special education teachers. A single subject alternating treatments design was implemented to address the following research question: What are the levels, variability, and trends of total (active and passive) engagement for six co-teaching models described in the professional literature? A second overall purpose of the study was to ascertain participant preferences and perceptions of co-teaching models after implementing and/or observing implementation. Three research questions were addressed descriptively and qualitatively: (a) what do participants view as benefits/strengths of co-teaching? (b) what do participants view as weaknesses of co-teaching? and (c) what model(s) do participants prefer? A third overall purpose was to ascertain if there were statistically significant and meaningful differences in gain scores across co-teaching models for content taught using the models during the teacher education course. Repeated measures analysis of variance procedures were used to test for differences in gain scores for each model individually as well as for models that incorporated large group (team teaching, one teach/one assist, one teach/one observe) or small group (station teaching, parallel teaching, alternative teaching) implementation formats. Overall findings indicated that, first, engagement levels of students were higher in co-teaching models that reduced the teacher to student ratio. That is, station teaching, parallel teaching, and alternative teaching formats collectively, produced higher levels of engagement than the combination of one teach/one assist, one teach/one observe, and teaming. Second, station teaching and teaming were the most preferred models of the participants surveyed. Common themes in the identification of strengths and benefits included co-teaching increasing the amount of individual attention, allowing for a variety of teaching methods, and allowing for collaboration between teachers. Noise level and unequal distribution of tasks were the common themes identified as weaknesses of co-teaching. Finally, measurement of participant gain scores across repeated co-teaching model implementations indicated that statistically significant and small in magnitude differences were noted across models. That is, in evaluating pre/post gain scores on content tests for individual models, there was a difference between station teaching and the remaining co-teaching models, with the difference dependent upon what time the students were tested. Moreover, results suggested that there was a difference between co-teaching models that utilized small or large group, with students receiving instruction in small groups showing stronger gains than those taught in larger groups, again with these differences dependent upon the time students were tested. Research limitations are presented as are implications for co-teaching practice and teacher education

A Problem of Cosmic Proportions: Floyd Henry Allport and the Concept of Collectivity in American Social Psychology

Floyd Henry Allport (1890-1978) is widely regarded as a significant figure in the establishment of experimental social psychology in the United States in the early twentieth century. His famous 1924 textbook and his early experimental work helped set the stage for a social psychology characterized by individualism, behaviorism, and experiment. Allport is particularly well-known for his banishment of the group concept from social psychology and his argument that the individual is the only viable, scientific object of study for the serious social psychologist. This early part of Allport’s career and the role it played in establishing American social psychology is relatively well documented. However, there is little scholarship regarding Allport’s work after the 1920s. An examination of this time period demonstrates that Allport’s earliest individualism was in fact rather short-lived, as he subjected it to serious revision in the early decades of the twentieth century. The increasing complexity of the bureaucratic structure of American society in the early 1900s, the economic collapse of the 1930s, and the onset of the Second World War were significant events in the development of Allport’s ideas regarding the individual. While his early work is marked by a concerted effort to create an ideal science for understanding the individual and the social, his later work was much more concerned with the social implications of individualism and collectivism. As the social world around him grew more complex, so too did his own social psychology, culminating in a significant change in Allport’s philosophy of science. These findings contribute to our understanding of social psychology and its history by: providing a novel view of one of social psychology’s central historical figures; demonstrating the difficult, persistent, and context-dependent nature of the individualism-collectivism divide in American social psychology; and providing a platform for thinking about the ways in which historians remember and write the stories of important figures in the field

Rheumatoid meningitis sine arthritis.

Rheumatoid meningitis is a rare and very serious extra-articular manifestation of rheumatoid arthritis. We present a case of a 7()year-old female with no history of arthritis who developed stroke-like symptoms, seizures, psychosis and compulsive behavior. Serial brain magnetic resonance images (MRI) over four months demonstrated progressive interhemispheric meningeal thickening. She had mild lymphocytic pleocytosis on the cerebrospinal fluid analysis and serum anti-cyclic citrullinated peptide antibodies resulted positive in high titers. She underwent a brain biopsy showing necrotizing granulomas consistent with rheumatoid meningitis. Her symptoms resolved with treatment with glucocorticoids and cyclophosphamide. She has not been diagnosed with rheumatoid arthritis even after 1 year of follow up. Clinicians should be aware of the possibility of rheumatoid meningitis without rheumatoid arthritis and keep it on the differential for patients with aseptic meningitis and otherwise negative work up

Fast approximate inference for longitudinal and multilevel data analysis

University of Technology Sydney. Faculty of Science.Generalised linear mixed models are the cornerstone of longitudinal and multilevel data analysis. However, exact inference for Bayesian mixed models with semiparametric extensions is typically intractable, requiring approximate inference methods for use in practice. Markov chain Monte Carlo or MCMC is one of the most commonly used approximate inference methods in this setting, but can be computationally intensive and often suffers from poor convergence in complex models. A faster, deterministic alternative to MCMC is variational approximations, a class of deterministic algorithms that is based on reformulating the problem of computing the posterior distribution as an optimisation problem, simplifying that problem and finding solutions to the perturbed problem. In this thesis, we work with a particular class of variational approximations, known as the mean field variational Bayes (MFVB). In essence, MFVB approximations are based upon optimising the Kullback-Leibler divergence with respect to the so-called approximating distribution. We derive MFVB algorithms for a wide variety of Bayesian semiparametric mixed models with Gaussian, Student-t, Bernoulli and Poisson responses. In order to overcome the computational cost of the direct naïve approach to the underlying MFVB calculations for models, we introduce a novel, streamlined approach that involves matrix permutation and block decomposition. Through a series of numerical studies, we demonstrate that the MFVB algorithms achieve a good level of accuracy compared to a MCMC benchmark (our gold standard). Furthermore, our developed streamlined algorithms are shown to have a complexity that is linear in the number of groups at each level, representing a two orders of magnitude improvement over the naïve approach. More importantly, the modularity of MFVB allows relatively simple extensions to more complicated scenarios, including higher-level random effects, measurement error and/or missing data problems, models with group-specific curves and real-time or online data processing. Illustrations from various real data examples are provided

The role of right and left parietal lobes in the conceptual processing of numbers

Neuropsychological and functional imaging studies have associated the conceptual processing of numbers with bilateral parietal regions (including intraparietal sulcus). However, the processes driving these effects remain unclear because both left and right posterior parietal regions are activated by many other conceptual, perceptual, attention, and response-selection processes. To dissociate parietal activation that is number-selective from parietal activation related to other stimulus or response-selection processes, we used fMRI to compare numbers and object names during exactly the same conceptual and perceptual tasks while factoring out activations correlating with response times. We found that right parietal activation was higher for conceptual decisions on numbers relative to the same tasks on object names, even when response time effects were fully factored out. In contrast, left parietal activation for numbers was equally involved in conceptual processing of object names. We suggest that left parietal activation for numbers reflects a range of processes, including the retrieval of learnt facts that are also involved in conceptual decisions on object names. In contrast, number selectivity in right parietal cortex reflects processes that are more involved in conceptual decisions on numbers than object names. Our results generate a new set of hypotheses that have implications for the design of future behavioral and functional imaging studies of patients with left and right parietal damage

Preceptor engagement in distributed medical school campuses

Background: There is increasing interest in distributed medical campuses and engagement of physicians in these communities.  To date, there has been suboptimal recruitment of physicians to participate in medical education at distributed campuses.  The purpose of this project was to identify barriers to engagement in medical education by community physicians in the geographical catchment of the Waterloo Regional Campus of McMaster.Method: In-depth, semi-structured, qualitative interviews were conducted with physicians not involved in teaching. Interview recordings were transcribed and analyzed using a closed-loop, iterative coding methodology and thematic analysis was performed.  Interviews were conducted until thematic saturation was achieved.Results: Six interviews were conducted and coded.  Nine key themes emerged: academic centre versus distributed sites, interest in teaching, financial considerations, administrative barriers, medical experience and knowledge currency, practice environment and schedule, training on teaching, setting up systems for learners in distributed campus settings, and student engagement and medical learner level.Conclusions: Barriers to engagement in teaching primarily focused on differences in job structure in the community, administrative barriers both at the hospital and through the medical school, and lack of knowledge on how to teach.  As medical schools look to expand the capacity of distributed campuses, misperceptions should be addressed and opportunities to improve engagement should be further explored

Exploring the Use of Free Bioinformatics Modules in an Introductory Biochemistry Course

Although bioinformatics, the use of computational science to study biology, has become imperative in many areas of the biological sciences and related career paths, introductory biochemistry courses may disregard practical knowledge on bioinformatics. For this reason, we merged a hands-on activity module into an undergraduate biochemistry course in two ways. First, we incorporated bioinformatics modules for building phylogenetic trees by aligning the active sites of 10 chosen related α-amylase enzymes using freely available data. Secondly, we chose three of those 10 α-amylase enzymes to compare the 3D structure of their active sites. This module gives the students an opportunity to understand how to access biological information from public databases such as GenBank, and analyze the information using software like MEGA, ClustalW, and RasMol. Overall, our module should provide instructors with ideas on how to develop similar modules and encourage students to develop further independence in the use of bioinformatics tools