Prioritizing Purposes: Two American History Teachers' Choices Among Subject Matter and Classroom-Related Purposes

This study explores how two American history teachers - one novice and one experienced – make in-the-moment choices among their history subject matter and classroom-related purposes during the teaching of an American history unit. Using classroom observations, lesson artifacts, student work products, and deep, retrospective interviews with the teachers as they watched videos of their teaching, this study maps out in detail the teachers’ purposes, both within and across different lesson activity structures. This study finds that the novice and the experienced teacher navigated among their purposes differently from each other, and that the characteristics of each teacher’s purposes navigation aligned with student outcomes in that teacher’s class. The novice teacher acted more like a juggler, with visible, reactive navigation among each purpose operational throughout his teaching; student outcomes in his class were similarly fragmented and discrete. The experienced teacher presented more like an orchestra conductor, interweaving his purposes and anticipating the navigation decisions that would create a more seamless whole; student outcomes in his class were aligned with his holistic navigation of purposes. Findings from this study have important implications for education research and teacher practice, including the relationship between teachers’ navigation among purposes and desired student outcomes, the integral role of classroom-related purposes interwoven with history subject matter purposes in teachers’ decision-making, and the differences in purposes navigation between a novice and an experienced history teacher.

Straddling The Gap: A MIddle School Teacher's Journey Towards Good Geography

Research points to a gap between academic or disciplinary based geography and what is taught in secondary classes across the nation. This study documents a teacher’s journey and efforts to bring a more disciplinary approach to two suburban heterogeneous sixth grade geography classrooms. The researcher traces student perspectives on geography and facility with geographic reasoning as well as his own perspectives and pedagogy with respect to student data. The study attempts to map the space where school geography meets and interacts with disciplinary oriented geography based upon the Geography for Life National Geography Standards. Participants completed two sets of baseline assessments and two sets of end of year assessments as well as an initial intake survey. The seven primary participants were interviewed five times each throughout the academic school year and data were openly coded. The data suggest that students can learn geography and geographic reasoning from a disciplinary perspective. Students sharpened their geographic skills through deeper subject matter knowledge and developing spatial and ecological perspectives. The data also indicate that the teacher researcher faced considerable challenges in implementing a disciplinary approach to teaching geography. The coverage demands of a crowded history-centric curriculum together with ill-fitting resources required a labor-intensive effort to put together and execute this study. Study findings indicate that the path to good geography pedagogy can be impeded by a host of external and internal challenges. However, to forward thinking practitioners, the effort to straddle the gap between school geography and disciplinary-based geography may be well worth it.

Visual Insight in Geometry

According to a traditional rationalist proposal, it is possible to attain knowledge of certain necessary truths by means of insight—an epistemic mental act that combines the 'presentational' character of perception with the a priori status usually reserved for discursive reasoning. In this dissertation, I defend the insight proposal in relation to a specific subject matter: elementary Euclidean plane geometry, as set out in Book I of Euclid's Elements. In particular, I argue that visualizations and visual experiences of diagrams allow human subjects to grasp truths of geometry by means of visual insight. In the first two chapters, I provide an initial defense of the geometrical insight proposal, drawing on a novel interpretation of Plato's Meno to motivate the view and to reply to some objections. In the remaining three chapters, I provide an account of the psychological underpinnings of geometrical insight, a task that requires considering the psychology of visual imagery alongside the details of Euclid's geometrical system. One important challenge is to explain how basic features of human visual representations can serve to ground our intuitive grasp of Euclid's postulates and other initial assumptions. A second challenge is to explain how we are able to grasp general theorems by considering diagrams that depict only special cases. I argue that both of these challenges can be met by an account that regards geometrical insight as based in visual experiences involving the combined deployment of two varieties of 'dynamic' visual imagery: one that allows the subject to visually rehearse spatial transformations of a figure's parts, and another that allows the subject to entertain alternative ways of structurally integrating the figure as a whole. It is the interplay between these two forms of dynamic imagery that enables a visual experience of a diagram, suitably animated in visual imagination, to justify belief in the propositions of Euclid’s geometry. The upshot is a novel dynamic imagery account that explains how intuitive knowledge of elementary Euclidean plane geometry can be understood as grounded in visual insight.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Sick Boy

This poetry collection explores the concepts of addiction and redemption. It does so through a series of vignette-style poems set in the Baltimore and DC area at the height of the heroin epidemic in the United States. Split into three parts, the first addresses the narrator’s initial drug use, the second follows the narrator at the strongest and least hopeful point of his addiction, and the third examines, through various scenes, the narrator’s attempts to find a life free from the confines of addiction. Although dealing with subject matter derived from dark and unfortunate circumstances, the narrator’s heroin addiction serves merely as a catalyst for the various situations that force the narrator to develop emotionally and grow even when trapped in the seemingly inescapable confines of addiction.