Learners’ Reading Metacognition and Summary Writing Skills Nested in Psychological and Sociocultural Factors

This paper investigates the relationship between secondary school students’ reading metacognition and summary writing skills—which represent a part of their academic language skills—within a network of psychological and sociocultural factors consisting of positivity, tolerance and respect, resilience, and culture and cooperation. We used data from the 2018 OECD’s Programme for International Student Assessment (PISA) database (n = 612,004) from 79 countries. We conducted a series of confirmatory factor analyses (CFA) to validate the variance and covariance structure of the tests and questionnaires used. Next, we performed structural equation modeling (SEM) to investigate the effect of the predictors on reading metacognition and summary writing skills. A significant relationship was found between sociocultural factors, reading metacognition, and summary writing skills. We propose that it is essential to consider how a student’s background, experiences, and school curriculum determine how these indicators present themselves in each student and affect their academic language performance

Do Employees From Less-Healthy Communities Use More Care and Cost More? Seeking to Establish a Business Case for Investment in Community Health.

INTRODUCTION: Few studies have examined the impact of community health on employers. We explored whether employed adults and their adult dependents living in less-healthy communities in the greater Philadelphia region used more care and incurred higher costs to employers than employees from healthier communities. METHODS: We used a multi-employer database to identify adult employees and dependents with continuous employment and mapped them to 31 zip code regions. We calculated community health scores at the regional level, by using metrics similar to the Robert Wood Johnson Foundation (RWJF) County Health Rankings but with local data. We used descriptive analyses and multilevel linear modeling to explore relationships between community health and 3 outcome variables: emergency department (ED) use, hospital use, and paid claims. Business leaders reviewed findings and offered insights on preparedness to invest in community health improvement. RESULTS: Poorer community health was associated with high use of ED services, after controlling for age and sex. After including a summary measure of racial composition at the zip code region level, the relationship between community health and ED use became nonsignificant. No significant relationships between community health and hospitalizations or paid claims were identified. Business leaders expressed interest in further understanding health needs of communities where their employees live. CONCLUSION: The health of communities in which adult employees and dependents live was associated with ED use, but similar relationships were not seen for hospitalizations or paid claims. This finding suggests a need for more primary care access. Despite limited quantitative evidence, business leaders expressed interest in guidance on investing in community health improvement

Implications of probabilistic data modeling for rule mining

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.Series: Research Report Series / Department of Statistics and Mathematic

EcoGIS – GIS tools for ecosystem approaches to fisheries management

Executive Summary: The EcoGIS project was launched in September 2004 to investigate how Geographic Information Systems (GIS), marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt Ecosystem Approaches to Fisheries Management (EAFM). EcoGIS is a collaborative effort between NOAA’s National Ocean Service (NOS) and National Marine Fisheries Service (NMFS), and four regional Fishery Management Councils. The project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. Of these four functional areas, the project team first focused on developing a working prototype for catch and effort analysis: the Fishery Mapper Tool. This ArcGIS extension creates time-and-area summarized maps of fishing catch and effort from logbook, observer, or fishery-independent survey data sets. Source data may come from Oracle, Microsoft Access, or other file formats. Feedback from beta-testers of the Fishery Mapper was used to debug the prototype, enhance performance, and add features. This report describes the four priority functional areas, the development of the Fishery Mapper tool, and several themes that emerged through the parallel evolution of the EcoGIS project, the concept and implementation of the broader field of Ecosystem Approaches to Management (EAM), data management practices, and other EAM toolsets. In addition, a set of six succinct recommendations are proposed on page 29. One major conclusion from this work is that there is no single “super-tool” to enable Ecosystem Approaches to Management; as such, tools should be developed for specific purposes with attention given to interoperability and automation. Future work should be coordinated with other GIS development projects in order to provide “value added” and minimize duplication of efforts. In addition to custom tools, the development of cross-cutting Regional Ecosystem Spatial Databases will enable access to quality data to support the analyses required by EAM. GIS tools will be useful in developing Integrated Ecosystem Assessments (IEAs) and providing pre- and post-processing capabilities for spatially-explicit ecosystem models. Continued funding will enable the EcoGIS project to develop GIS tools that are immediately applicable to today’s needs. These tools will enable simplified and efficient data query, the ability to visualize data over time, and ways to synthesize multidimensional data from diverse sources. These capabilities will provide new information for analyzing issues from an ecosystem perspective, which will ultimately result in better understanding of fisheries and better support for decision-making. (PDF file contains 45 pages.