Studying the Causes and Consequences of Internal Labor Migration Using Survey and Administrative Data Sources

This dissertation comprises three chapters. The first chapter motivates the use of a novel data set combining survey and administrative sources for the study of internal labor migration. By following a sample of individuals from the American Community Survey (ACS) across their employment outcomes over time according to the Longitudinal Employer-Household Dynamics (LEHD) database, I construct a measure of geographic labor mobility that allows me to exploit information about individuals prior to their move. This enables me to explore aspects of the migration decision, such as homeownership and employment status, in ways that have not previously been possible. In the second chapter, I use this data set to test the theory that falling home prices affect a worker’s propensity to take a job in a different metropolitan area from where he is currently located. Employing a within-CBSA and time estimation that compares homeowners to renters in their propensities to relocate for jobs, I find that homeowners who have experienced declines in the nominal value of their homes are approximately 12% less likely than average to take a new job in a location outside of the metropolitan area where they currently reside. This evidence is consistent with the hypothesis that housing lock-in has contributed to the decline in labor mobility of homeowners during the recent housing bust. The third chapter focuses on a sample of unemployed workers in the same data set, in order to compare the unemployment durations of those who find subsequent employment by relocating to a new metropolitan area, versus those who find employment in their original location. Using an instrumental variables strategy to address the endogeneity of the migration decision, I find that out-migrating for a new job significantly reduces the time to re-employment. These results stand in contrast to OLS estimates, which suggest that those who move have longer unemployment durations. This implies that those who migrate for jobs in the data may be particularly disadvantaged in their ability to find employment, and thus have strong short-term incentives to relocate.

Bento Box Discovery

Poster presentation at the University of Maryland Libraries Research & Innovative Practice Forum on June 8, 2016. The poster proposes that the UMD Libraries should evaluate adoption of Bento Box Discovery for improved user search experience.

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.

EXAMINING SECONDARY MATHEMATICS TEACHER CANDIDATES’ LEARNING AND ENACTMENT OF MATHEMATICS TEACHING PRACTICES: A MULTIPLE CASE STUDY

This qualitative case study explored three teacher candidates’ learning and enactment of discourse-focused mathematics teaching practices. Using audio and video recordings of their teaching practice this study aimed to identify the shifts in the way in which the teacher candidates enacted the following discourse practices: elicited and used evidence of student thinking, posed purposeful questions, and facilitated meaningful mathematical discourse. The teacher candidates’ written reflections from their practice-based coursework as well as interviews were examined to see how two mathematics methods courses influenced their learning and enactment of the three discourse focused mathematics teaching practices. These data sources were also used to identify tensions the teacher candidates encountered. All three candidates in the study were able to successfully enact and reflect on these discourse-focused mathematics teaching practices at various time points in their preparation programs. Consistency of use and areas of improvement differed, however, depending on various tensions experienced by each candidate. Access to quality curriculum materials as well as time to formulate and enact thoughtful lesson plans that supported classroom discourse were tensions for these teacher candidates. This study shows that teacher candidates are capable of enacting discourse-focused teaching practices early in their field placements and with the support of practice-based coursework they can analyze and reflect on their practice for improvement. This study also reveals the importance of assisting teacher candidates in accessing rich mathematical tasks and collaborating during lesson planning. More research needs to be explored to identify how specific aspects of the learning cycle impact individual teachers and how this can be used to improve practice-based teacher education courses.

An Analysis of the Implementation and Perceived Effectiveness of the SchoolMAX Family Portal

ABSTRACT Title of Document: AN ANALYSIS OF THE IMPLEMENTATION AND PERCEIVED EFFECTIVENESS OF THE SCHOOLMAX FAMILY PORTAL Warren Wesley Watts, Doctor of Education, 2015 Directed By: Margaret J. McLaughlin, Ph.D. Department of Counseling, Higher Education and Special Education School districts have spent millions of dollars implementing student information systems that offer family portals with web-based access to parents and students. One of the main purposes of these systems is to improve school-to-home communication. Research has shown that when school-to-home communication is implemented effectively, parent involvement improves and student achievement increases (Epstein, 2001). The purpose of the study was to (a) understand why parents used or refrained from using the family portal and (b) determine what barriers to use might exist. To this end, this descriptive study identified the information parent users accessed in the SchoolMAX family portal, determined how frequently parents accessed the portal, and ascertained whether parents perceived an increase in communication with their children about academic matters after they began accessing the portal. Finally, the study sought to identify whether barriers existed that prevented parents from using the family portal. The inquiry employed three data sources to answer the aforementioned queries. These sources included (a) a survey sent electronically to 19,108 parents who registered online for the SchoolMAX family portal; (b) SchoolMAX portal usage data from the student information system for system usage between January 1, 2015 and June 30, 2015; and (c) a paper survey sent to 691 parents of students that had never used the SchoolMAX family portal in one elementary school, one middle school and one high school that were representative of other schools in the district. Survey results indicated that parents at all grade levels used the family portal. Usage data also confirmed that approximately 19% of the students had parents who monitored their progress through the family portal. Usage data also showed that parents were monitoring approximately 25% of students in secondary schools (6th – 12th grade) and 16% of students in elementary schools. Of the wide menu of resources available through the SchoolMAX family portal, parents used three areas most frequently: attendance, daily grades, and report cards. Approximately 70% of parents responded that their communication had improved with their children about academic matters since they started using the SchoolMAX family portal, and 90% of parents responded that the SchoolMAX family portal was an effective or somewhat effective tool. Parents also expressed interest in the addition of additional information to the SchoolMAX family portal. Specifically, the top three additions parents wanted to see included homework assignments, high stakes test scores, and graduation requirements. Parents also reported that 92% of them spoke to their children at least 2 to 3 times per week about academics. Due to the low response rate of the parent non-user survey, potential barriers to using the SchoolMAX family portal could not be addressed in this study. However, this issue may be a useful research topic in a future study. Keywords: school to home communication, student information systems, family portal, parent portal

Defining the Resolution of a Network for Transportation Analyses: a New Method to Improve Transportation Planning Decisions

Travel demand models are important tools used in the analysis of transportation plans, projects, and policies. The modeling results are useful for transportation planners making transportation decisions and for policy makers developing transportation policies. Defining the level of detail (i.e., the number of roads) of the transport network in consistency with the travel demand model’s zone system is crucial to the accuracy of modeling results. However, travel demand modelers have not had tools to determine how much detail is needed in a transport network for a travel demand model. This dissertation seeks to fill this knowledge gap by (1) providing methodology to define an appropriate level of detail for a transport network in a given travel demand model; (2) implementing this methodology in a travel demand model in the Baltimore area; and (3) identifying how this methodology improves the modeling accuracy. All analyses identify the spatial resolution of the transport network has great impacts on the modeling results. For example, when compared to the observed traffic data, a very detailed network underestimates traffic congestion in the Baltimore area, while a network developed by this dissertation provides a more accurate modeling result of the traffic conditions. Through the evaluation of the impacts a new transportation project has on both networks, the differences in their analysis results point out the importance of having an appropriate level of network detail for making improved planning decisions. The results corroborate a suggested guideline concerning the development of a transport network in consistency with the travel demand model’s zone system. To conclude this dissertation, limitations are identified in data sources and methodology, based on which a plan of future studies is laid out.

A Figured Worlds Perspective on Middle School Learners' Climate Literacy Development

The purpose of this study was to investigate the nature of the relationship between middle school science learners’ conditions and their developing understandings of climate change. I applied the anthropological theoretical perspective of figured worlds (Holland, Lachicotte, Skinner, & Cain, 1998) to examine learners’ views of themselves and their capacities to act in relation to climate change. My overarching research question was: How are middle school science learners’ figured worlds of climate change related to the conditions in which they are embedded? I used a descriptive single-case study design to examine the climate change ideas of eight purposefully selected 6th grade science learners. Data sources included: classroom observations, curriculum documents, interviews, focus groups, and written assessments and artifacts, including learners’ self- generated drawings. I identified six analytic lenses with which to explore the data. Insights from the application of these analytic lenses provided information about the elements of participants’ climate change stories, which I reported through the use of a storytelling heuristic. I then synthesized elements of participants’ collective climate change story, which provided an “entrance” (Kitchell, Hannan, & Kempton, 2000, p. 96) into their figured world of climate change. Aspects of learners’ conditions—such as their worlds of school, technology and media use, and family—appeared to shape their figured world of climate change. Within their figured world of climate change, learners saw themselves—individually and as members of groups—as inhabiting a variety of climate change identities, some of which were in conflict with each other. I posited that learners’ enactment of these identities – or the ways in which they expressed their climate change agency – had the potential to reshape or reinforce their conditions. Thus, learners’ figured worlds of climate change might be considered “spaces of authoring” (Holland et al., 1998, p. 45) with potential for inciting social and environmental change. The nature of such change would hinge on the extent to which these nascent climate change identities become salient for these early adolescent learners through their continued climate change learning experiences. Implications for policy, curriculum and instruction, and science education research related to climate change education are presented.

Harmonization of Life Cycle Climate Performance and Its Improvements for Heat Pump Applications

Life Cycle Climate Performance (LCCP) is an evaluation method by which heating, ventilation, air conditioning and refrigeration systems can be evaluated for their global warming impact over the course of their complete life cycle. LCCP is more inclusive than previous metrics such as Total Equivalent Warming Impact. It is calculated as the sum of direct and indirect emissions generated over the lifetime of the system “from cradle to grave”. Direct emissions include all effects from the release of refrigerants into the atmosphere during the lifetime of the system. This includes annual leakage and losses during the disposal of the unit. The indirect emissions include emissions from the energy consumption during manufacturing process, lifetime operation, and disposal of the system. This thesis proposes a standardized approach to the use of LCCP and traceable data sources for all aspects of the calculation. An equation is proposed that unifies the efforts of previous researchers. Data sources are recommended for average values for all LCCP inputs. A residential heat pump sample problem is presented illustrating the methodology. The heat pump is evaluated at five U.S. locations in different climate zones. An excel tool was developed for residential heat pumps using the proposed method. The primary factor in the LCCP calculation is the energy consumption of the system. The effects of advanced vapor compression cycles are then investigated for heat pump applications. Advanced cycle options attempt to reduce the energy consumption in various ways. There are three categories of advanced cycle options: subcooling cycles, expansion loss recovery cycles and multi-stage cycles. The cycles selected for research are the suction line heat exchanger cycle, the expander cycle, the ejector cycle, and the vapor injection cycle. The cycles are modeled using Engineering Equation Solver and the results are applied to the LCCP methodology. The expander cycle, ejector cycle and vapor injection cycle are effective in reducing LCCP of a residential heat pump by 5.6%, 8.2% and 10.5%, respectively in Phoenix, AZ. The advanced cycles are evaluated with the use of low GWP refrigerants and are capable of reducing the LCCP of a residential heat by 13.7%, 16.3% and 18.6% using a refrigerant with a GWP of 10. To meet the U.S. Department of Energy’s goal of reducing residential energy use by 40% by 2025 with a proportional reduction in all other categories of residential energy consumption, a reduction in the energy consumption of a residential heat pump of 34.8% with a refrigerant GWP of 10 for Phoenix, AZ is necessary. A combination of advanced cycle, control options and low GWP refrigerants are necessary to meet this goal.