The Caring Teacher: A Multiple Case Study That Looks at What Teachers Do and Believe about Their Work With At-Risk Students

Caring teachers have been identified as a critical component of successful interventions with at-risk students, however just what constitutes a caring teacher is less well understood. Specifically, what are the behaviors, characteristics, and beliefs of caring teachers, and how are they impacted by the contexts within which they work? The purpose of this multiple case study was to understand more about caring teachers who work with at-risk students in secondary schools located in a Midwestern city and thereby to add complexity to the literature. Two middle school teachers and two high school teachers were recruited to participate. They were observed on multiple occasions and interviewed twice. The data from these observations and interviews were initially analyzed case by case; the cross case analysis based on the results from the individual case resulted in 6 themes that were present across the four cases. The following themes were identified: the role of relationships, perspective on at-risk students, providing opportunities for students to develop a positive sense of themselves, the value of a positive classroom experience for both students and teacher, negotiating power, and flexibility. Implications of this research for psychologists, educators, and policy makers, as well as future research are also discussed.

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.

Comparative Anylysis of Recyling Programs: A Case Study of Three Universities

Abstract The goal of this study was to conduct a comparative analysis of three university recycling programs. This study looked at several aspects of the programs that included the diversion rates, per capita ratios of materials recycled and disposed, and the average net costs of waste disposal and waste diversion. The universities included in this study were the University of Nebraska-Lincoln, the University of Colorado at Boulder, and the University of Oregon. To gather the information necessary for this analysis, I contacted each of the university’s recycling coordinators. To determine the average net costs of waste disposal and waste diversion I requested both the recycling budget and solid waste budget from each university for the fiscal years of interest which included: 2006-2007, 2007-2008, and 2008-2009. To calculate the diversion rates and per capita ratios, I requested performance records from each university listing the tonnage of materials recycled and disposed for the same years. This study’s findings reported that the average net costs for waste diversion in all three universities were $22-$122 less per ton than costs for waste collection and disposal. This study also indicated that the universities with the highest diversion and recycling rates were the University of Colorado at Boulder and the University of Oregon. The university with the lowest waste generated per capita was the University of Oregon followed by the University of Nebraska-Lincoln.