Determinants of Depressive Symptoms Among Women on Public Assistance in Louisiana

Depression can be a significant barrier in the welfare-to-work transition of poor women. Fortunately, support from social networks can lessen symptoms and facilitate entry into the workplace. Inconsistency in the literature concerning the effects of social networks on the poor suggests further research is needed. Thus, we examine the level and determinants of depressive symptoms among participants in the Temporary Assistance to Needy Families program. Having a good job, being in good health, married, and black, and living in rural areas inhibit symptoms of depression. Remaining on TANF and having several children increases symptom levels. Those who report that they frequently have people to help them show lower levels of depression. The larger the social network, and the higher the percent of the network that is made up of neighbors, the higher the level of depression. While some of our findings suggest the success of 1996 welfare reform legislation others suggest important policy considerations. Good physical health (including access to health care), reduction of economic hardships, and effective social supports are ongoing issues to be addressed among low-income populations

Creation and Implementation of the Innovation-Based Learning Framework: A Learning Analytics Approach

To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where students learn fundamental engineering concepts and apply them to an innovation project with the goal of producing value outsidethe classroom. The model has been fairly successful, but questions still remain about how to best support students and instructors in open-ended innovation spaces. To answer these questions, learning analytics and educational data mining (LA/EDM) techniques were used to better understand student innovation in IBL settings. LA/EDM is a growing field with the goal of collecting and interpreting large amounts of educational data to support student learning. In this work, five LA/EDM algorithms and tools were developed: 1) the IBL framework which groups student actions into illustrative categories specific to innovation environments, 2) a classifier model that automatically groups student text into the categories of the framework, 3) classifier models that leverage the IBL framework to predict student success, 4) clustering models that group students with similar behavior, and 5) epistemic network analysis models that summarize temporal student behavior. For each of the five algorithms/tools, the design, development, assessment, and resulting implications are presented. Together, the results paint a picture of the affordances and challenges of teaching and learning innovation. The main insights gained are how language and temporal behavior provide meaningful information about students? learning and innovation processes, the unique challenges that result from incorporating open-ended innovation into the classroom, and the impact of using LA/EDM tools to overcome these challenges

Regional Poverty and Population Response:A Comparison of Three Regions in the United States and Germany

In this paper, we examine poverty in three regions in the United States and Germany and discuss its causes and demographic consequences. The three regions are those with the highest rates of poverty in the two countries: the Mississippi Delta and Texas Borderland in the United States and the Northeastern Border Region in Germany. We show that standard models to explain poverty need to be placed in the historical legacies of the three regions in order to understand their current levels of poverty. While our results show many common factors for poverty in the three regions, they also point to important differences. Similarly, we identify differences among the regions in their demographic responses to poverty, in part reflecting their different historical legacies. Thus, one implication of the paper is the importance of place-based poverty-mitigation strategies for successful policy planning.In this paper, we examine poverty in three regions in the United States and Germany and discuss its causes and demographic consequences. The three regions are those with the highest rates of poverty in the two countries: the Mississippi Delta and Texas Borderland in the United States and the Northeastern Border Region in Germany. We show that standard models to explain poverty need to be placed in the historical legacies of the three regions in order to understand their current levels of poverty. While our results show many common factors for poverty in the three regions, they also point to important differences. Similarly, we identify differences among the regions in their demographic responses to poverty, in part reflecting their different historical legacies. Thus, one implication of the paper is the importance of place-based poverty-mitigation strategies for successful policy planning

Welfare, Work, and Well-Being in Metro and Nonmetro Louisiana

This paper examines the extent to which persons in the Temporary Assistance to Needy Families (TANF) have been able to leave the TANF program. The analysis is based on the Louisiana Welfare Survey which is a panel study of 1,000 persons (500 in New Orleans and 500 in two labor market areas in northeastern Louisiana) who in 1998 and 1999 had been on welfare. The original respondents have been reinterviewed annually, with the fifth and final wave currently (May 2002) in the field. The findings reported in this paper are based on the first three waves of the panel survey, 1998- 2000. The findings show that by 2000 more than half of the respondents had left TANF, mostly because of employment. Well over one-half of all TANF leavers reported to be working in 2000. Although most respondents that had left TANF reported being better off economically, the work these persons could find consisted mostly of low-status low-pay jobs in service industries. As a result, TANF recipients as well as TANF leavers faced a good many economic hardships, such as not having enough to eat, having phone and utilities disconnected, and inability to obtain medical and dental services. The comparison of metro and nonrnetro areas showed that TANF recipients in nonmetro areas were less likely to leave the TANF program for work, had lower human capital, and tended to face more economic hardships than their metro counterparts. The picture that emerges from these findings shows that the economic situation of TANF leavers is fragile and tenuous, and that it is premature to consider the welfare reform legislation of 1996 a success

Socioeconomic Performance in Metropolitan and Nonmetropolitan Areas during the 1980s

The socioeconomic gap between metropolitan and nonmetropolitan areas increased during the 1980s. We test three competing explanations for this trend during the 1980s: overdependence on manufacturing, especially in nonmetro labor markets, the emergence of producer services as a catalyst of socioeconomic growth, and federal spending. Using a model that is informed by a variety of perspectives in sociology and economic geography, and commuter zones (CZs) as spatial units of analysis, we estimate the effects of manufacturing concentration, producer service concentration, and federal spending on per capita income, per capita earnings, and private nonfarm employment growth during the 1983-1988 business cycle recovery. The OLS and interaction models show that all three factors help explain why metro areas outperformed nonmetro areas during this time period. The effects of producer service concentration, however, best fit with our expectations. Implications of our findings are discussed

Understanding Learners\u27 Motivation through Machine Learning Analysis on Reflection Writing

Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students\u27 performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. intrinsic). These results will be compared against other measures of motivation, including student self-report, faculty observation, and externally validated surveys. As part of a longer-term study, this pilot work sheds light on the key question for student success and retention: how does student motivation evolve through the 3rd and 4th years in college? The purpose of this research project is to gain insights into learners’ motivation levels and how it evolves during the last two years in college, as well as to extend current Educational Data Mining research and Machine Learning analysis described in the literature. It is significant on two fronts: 1) we will extend the ability of ML in analyzing reflective written artifacts to explore student physiological and emotional development; 2) the longitudinal study will help monitor the progressive change of motivation in college students in a PBL environment. Preliminary results from an initial pilot study are promising. By analyzing written reflection journal entries from previous students, the ML algorithm has differentiated keywords into three student motivation levels: “high”, “neutral” and “low”. Using supervised classes, for example, the ML algorithm differentiated words in the highly motivated student text such as “team” and “learning”, while the text coded as low motivation included “use”, “pushed” and “nothing”. For our future research, we aim to create a dictionary that identifies words/phrases related to positive/negative motivation. We will extend the pilot study to a longitudinal evaluation of student motivation over four semesters of engineering education as well as prediction of student success in a PBL environment

Poverty in the Texas borderland and lower Mississippi Delta: A comparative analysis of differences by family type

We provide a comparative analysis of county-level poverty in the two poorest regions of the United States - the Texas Borderland and the Lower Mississippi Delta - with a special focus on differences by family type. Our results reveal important regional variation in both the prevalence of poverty and the composition of the poor population across major family types. Using OLS regression models of family type-specific poverty we demonstrate three key findings: 1) There are no significant regional differences in poverty levels by family type between the Borderland and the Delta, net of important structural factors that characterize the regions; 2) with the exception of the employment rate, the structural factors associated with poverty among married couple and single female-headed families are quite different; and 3) paradoxically, areas in the Borderland with high in-migration are especially likely to suffer from high married-couple family poverty. Our results suggest the need for regionally targeted and demographically tailored anti-poverty policies.family structure, Mississippi Delta, persistent poverty, poverty, regional poverty, Texas Borderland