48670 research outputs found
Sort by
Australian Higher Education Implementations of Personalized Learning: Understanding Motivations, Outcomes & Future Opportunities
No full textAs Australian universities have considered the potential of technology-enabled personalized learning, with some making significant resource investments, there has been little documented about their successes or failures. Additionally, for a sector with a history and culture of collaboration, the ability to learn from other implementations and generate better outcomes may be significantly curtailed without these shared insights. Through the perspectives of current and former university leaders and practitioners, at Australian institutions that have adopted technology-enabled personalized learning platforms, this qualitative, exploratory study seeks to understand and document what experiences have been to-date, and what other institutions and individuals may need to consider and support in their own pursuit of technology-enabled personalized learning. The study identified seven Australian universities with active or retired solutions and with Bolman and Deal’s four frames model, as well as Rogers’ diffusion of innovation theory, each institution’s experience was assessed for themes that could provide valuable insights for other adopters in the future. Several key themes were identified, and include the need to appropriately consider and plan for the resource impacts of introducing a technology-enabled personalized learning pedagogy and platform; the importance of coordinated organic development with leadership support conscious of the need to shape institutional culture and spark the imagination and curiosity of key staff; a well-defined understanding of an institution’s capacity and capability for change; and finally the need to avoid some of the most significant technology platform pitfalls experienced to-date. While the impacts of the COVID-19 pandemic on global higher education continue to be realized, technology-enabled personalized learning offers an opportunity to embrace the potential to deliver better learning outcomes, more efficiently
Applications of Advanced Statistical Modeling Techniques for Understanding Depression
No full textWe conducted three investigations into different facets of major depressive disorder. Chapter One describes the application of hierarchical modeling techniques in combination with the Linguistic Inquiry Word Count to characterize associations between depressed individuals’ emotion regulation strategies and their discrete state emotion. State emotion was associated with emotion regulation strategy selection, independent of the adaptiveness of the coping response. In Chapter Two we investigated the relationship between urban living and depression, using meta-analytic and meta-regression techniques to characterize how this relationship has changed over time across both developed and developing countries. In developed countries, depression prevalence was higher in urban areas than rural areas. In developing countries, no overall effect of urbanicity was found, although a relationship between urbanicity and depression appears to be emerging over time. Chapter Three describes an investigation comparing the accuracy of traditional and machine learning model building algorithms for predicting patient outcomes in response to antidepressant treatment. Traditional model building techniques designed for inferential analysis were found to be poorly suited for the purposes of prediction. Machine learning techniques designed for prediction can make accurate predictions; however, their utility is limited by how they are applied and to their access to informative predictors
Optical Imaging of Tissue Physiology with Exogenous Contrast Agents
No full textThis thesis describes experiments and analyses which push the frontier per what one can learn from optically emitting exogenous contrast agents in living tissue. The first set of experiments concurrently measured cerebral blood flow and both intravascular- and extravascular-tissue oxygen concentration in a rat brain during functional activation; the new instrumentation needed to collect this information used contrast agent phosphorescence lifetime to determine oxygen concentration and speckle contrast imaging to probe blood flow. The concurrent measurement of multiple physiological parameters with high temporal resolution (∼7 Hz) provided a unique opportunity to observe the interconnected dynamics of oxygen exchange, blood flow, and cerebral oxygen metabolism. The experiments showed that initial metabolic changes trigger a blood flow response; comprehensive theoretical modeling of the data exposed potential weaknesses of the well-known and often-used two-compartment oxygen diffusion model, and the experiments as a whole introduced a new tool for characterization of oxygen metabolism and neurovascular coupling in the brain. The second set of experiments developed instrumentation and a simple theoretical methodology for imaging fluorescent targets in turbid media such as tissue. This approach used the ideas of spatial frequency domain fluorescence diffuse optical tomography (SFD-FDOT). The new reconstruction algorithm modified the more complex SFD-FDOT reconstruction method to rapidly acquire the depth of fluorescent target(s) and then estimate the transverse margins of the fluorescent target(s). Tissue phantom experiments demonstrated the instrumentation and algorithm, and assessed limitations. The new methodology could be useful for image guidance during tumor resection surgery, and could also provide rapid and useful constraining information for more comprehensive fluorescent tomography
Essays on Machine Learning and Labor Economics
No full textObserved worker and firm characteristics only explain a small wage variation. Beyond characteristics that are directly observed from the data, my thesis develops new empirical methods aimed at identifying unobserved heterogeneity in the labor market. Chapter 1 proposes an empirical method to measure the effects of coworkers on wages. I take advantage of the recent cutting-edge clustering method that combines machine-learning and economic theory to identify groups of workers with similar latent productivity type. I further apply the cluster-based method to identify the effects of coworkers on wages and evaluate their economic implications in empirical-relevant simulations. The proposed method has proven potential to be applied to the real-world data to improve our ability to understand the role of coworkers in substantive questions where existing methods have limitations
Exploring Novel Materials to Be Used as Dual-Energy Mammography Contrast Agents for Breast Cancer Detection
No full textDual-energy mammography (DEM) is a recently FDA-approved x-ray imaging technology developed for breast cancer screening, especially advantageous for women with dense breasts. While studies have reported the benefits of DEM in breast cancer screening, thus far, no DEM-specific contrast agents have been approved. Therefore, clinics use iodinated contrast agents, which can have suboptimal contrast in DEM, short circulation half-life, and adverse effects on patients. Nanoparticle-based contrast agents can be designed to address some of these limitations. This thesis explores different materials to develop DEM-specific nanoparticle-based contrast agents with high contrast and biocompatibility. Based on previous research highlighting elements that produce high DEM contrast, we explored materials such as silver, tellurium, and molybdenum to develop DEM-specific nanoparticle-based contrast agents. First, 8 nm silver telluride nanoparticles (Ag2Te NPs) were developed as DEM-specific contrast agents. Ag2Te NPs are composed of two DEM high-contrast generating materials and thus, provide superior contrast than iodinated molecules, both in in vitro and in vivo settings. Additionally, by coating these with mPEG-SH 5K, we prolonged their circulation, tumor accumulation, and colloidal stability while maintaining biocompatibility. Next, to further improve the likelihood of clinical translation of Ag2Te NPs, we designed them to be 3 nm in size to achieve renal clearance. These 3 nm Ag2Te NPs provided similar contrast and biocompatibility to the larger Ag2Te NPs, even when studied for a longer term in vivo. Furthermore, 93% of the injected dose was excreted from the main organs in 24 hours, 95% in 7 days, and 97% in 28 days. This excretion is among the highest reported thus far for any nanoparticle type. Lastly, we developed 2 nm molybdenum disulfide nanoparticles (MoS2 NPs) with different coatings and explored them as DEM-specific contrast agents. Our findings suggest that MoS2 NPs can produce higher contrast than iodinated molecules and be biocompatible in vitro. Together, this work presents an advancement in the development of DEM-specific contrast agents and their potential progression towards clinical translation
Lattice Theory in Multi-Agent Systems
No full textIn this thesis, we argue that (order-) lattice-based multi-agent information systems constitute a broad class of networked multi-agent systems in which relational data is passed between nodes. Mathematically modeled as lattice-valued sheaves, we initiate a discrete Hodge theory with a Laplace operator, analogous to the graph Laplacian and the graph connection Laplacian, acting on assignments of data to the nodes of a Tarski sheaf. The Hodge-Tarski theorem (the main theorem) relates the fixed point theory of this operator, called the Tarski Laplacian, in deference to the Tarski Fixed Point Theorem, to the global sections (consistent global states) of the sheaf. We present novel applications to signal processing and multi-agent semantics and supply a plethora of examples throughout
Social Influences, Identities, Intentions: Essays on Fishbein and Ajzen\u27s Reasoned Action Approach to Predicting and Explaining Behavior
No full textThis dissertation consists of three essays on the Fishbein-Ajzen model of human behavior, the most famous iteration of which is the theory of planned behavior. In chapter one, I argue that Fishbein and Ajzen’s definitions of the attitudinal and normative components of their theory result in unnecessary and undesirable overlap between the two. In chapter two, I discuss Fishbein and Ajzen’s assessment of self-identity as a potential addition to their theory and argue – contra Fishbein and Ajzen – that many of the measures of self-identity that are typically used are not likely to measure the theory’s existing determinants of behavior, as opposed to a truly separate construct. In chapter three, I argue that Fishbein and Ajzen’s definition of intention as behavioral expectation is problematic because 1) it does not match our commonsense notion of intention, and 2) it is the commonsense notion that is best suited to fill the role of “intention” within the context of the theory
Five Findings on Teacher Diversity
Get PDFOver the past several decades, the teaching force has grown more racially-ethnically diverse, but not evenly across different types of schools or sustainably because of high turnover rates
What Types Of Professional Development Contribute To The Critical Skill Sets Of Education Professionals?
Get PDFProfessional development (PD) provides content knowledge, teaching tips, and resources to education administrators and direct-service staff. However, these experiences may seem irrelevant to the participants’ ultimate job responsibilities. Our research sought to answer this question: What types of professional development contribute to the critical skill sets of education professionals? This mixed-methods study included a comprehensive literature review, an online survey, and two online focus groups. We offer the following recommendations. For PD participants supervisors, PD designers, and PD facilitators: Broaden one’s definition of PD, employ self and external assessments, solicit and integrate participants’ feedback, recognize that different PD experiences cultivate different skill sets, and enhance staff relationship-building skills through multiple PD opportunities. For PD researchers: Increase the depth and breadth of the study, expand the geographic reach of the study, and dig deeper into our data, analysis, and recommendations.
We hope that our research results in professional development opportunities that better prepare educators for their current and future job responsibilities
Financial Literacy and Financial Behavior at Older Ages
Get PDFRecent research documents that people are increasingly entering old age with more debt than ever before and with little or no retirement planning. This paper examines some reasons why older people’s financial behaviors depart from the predictions of the life-cycle model, where the latter predicts that older persons would be at the peak of their wealth accumulation process and manage their money so as not to run out of savings in retirement. Drawing on the rapidly growing literature on financial literacy and financial behavior at older ages, we highlight findings on financial literacy patterns. We also document that “better” financial behaviors are strongly associated with greater financial literacy in later life. We close with some thoughts regarding limitations, policy implications, and next steps