Browsing by Subject "Data collection"

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Aligning data with organization's and workers' goals: designing data labeling systems for social service case notes
(2023-08) Gondimalla, Apoorva; Lee, Min Kyung, Ph. D.
In the era of data-driven approaches in non-profit and social service government organizations, prevailing data collection methods for performance and funding reports are ineffective and unsatisfactory for both workers and organizational leaders. Within social service provision, evaluating outcomes necessitates intricate subjective assessments, resulting in social workers equipped with profound insights into services and outcomes shouldering the burden of manual record-keeping. Simultaneously, organizational leaders grapple with insufficient data for reporting. While existing research explores data collection challenges, there is a dearth of studies that delve into solutions for enhancing these systems. This study examines data labeling systems that encapsulate client interaction outcomes, focusing on caseworkers aiding those experiencing homelessness. Despite advances in domains such as crowd-sourced data labeling, their approaches often fail to consider the unique values and contexts of social workers who intertwine data labeling with their caregiving work. By employing interviews, ideation, and a speed-dating approach, we scrutinize preferences, potential solutions, and challenges in crafting efficient data labeling systems. We evaluate 15 diverse design ideas across four dimensions: alignment with case management objectives, comprehensive portrayal of caseworker contributions, clarity in data labels, and enhancements in labeling process usability. Our findings highlight the collective aspiration for data labeling systems that cater to varied stakeholder information goals while effectively capturing nuanced casework details, streamlining data labeling into a seamless, efficient task. Leveraging our insights, we offer design implications for enhancing data labeling systems, aligning them with the objectives of both organizations and workers.
Bike big data : how GPS route data collected from smartphones can benefit bicycle planning
(2013-05) Meyer, Joel Loren; Zhang, Ming, 1963 April 22-
In order to determine the most effective ways to increase ridership in their communities, bicycle planners require quality data on bicycling behavior. Traditional bicycle data collection methods, however, are limited by the large amount of time and expertise required to process and analyze the data, by their inability to provide information at the level of detail needed to understand the complexities of bicycling behavior, and by issues related to sampling bias and poor respondent trip recall. Fortunately, a relatively new method for collecting travel data has emerged that has the potential to provide higher quality and lower cost bicycle data to local planning agencies than has previously been possible with traditional data collection methods: the use of global positioning system (GPS) sensors in smartphones. Researchers at The University of Texas recently evaluated the usefulness of one such smartphone application - “CycleTracks” - to collect bicycle route data. Over 3,600 unique trips were collected from around 300 cyclists in Austin, Texas between May and October, 2011. While they found the CycleTracks app to be useful for collecting a large dataset, to this point there has been only limited analysis of the route data in terms of its usefulness in the planning field. This report will explore the ways in which GPS route data collected from smartphones can address some of the limitations of traditional data collection methods. Austin is used as a case study to show how the GPS route data can be used to plan for network connectivity, to identify barriers in the bicycle network, and to analyze cycling behavior before and after the installation of new facilities. The report finds that despite a number of limitations, smartphone-based GPS data collection has the potential to become an important part of local planning agencies’ regular data collection efforts.
Data-driven decision making in physical education : a case study
(2014-05) Dauenhauer, Brian Daniel; Keating, Xiaofen; Lambdin, Dolly, 1951-
The purpose of this study was to explore the data-driven decision making process within the context of K-12 physical education. Although the topic has received extraordinary attention in other areas of education, it has yet to be investigated directly in physical education settings. A conceptual framework proposed by Mandinach, Honey, Light, and Brunner (2008) guided the investigation. Using a multi-site case study design, one school district previously awarded a Carol M. White Physical Education Program Grant served as the overarching case and eight schools within the district served as embedded cases. Eight physical education teachers, three district coordinators, one principal, and one school counselor participated in the study. Evidence was gathered through interviews, observations, documents, archival records, and artifacts. Analytic strategies such as pattern matching, examining rival explanations, and drawing diagrams were utilized to generate common themes within the data. Overall, findings indicated that physical education teachers collected substantial amounts of physical activity and fitness data aligned with policy requirements, often at the expense of data related to other important teaching domains. Evidence also indicated that teachers rarely transformed collected data into actionable knowledge. It seemed as though teachers were only collecting data because they were required to and held little value in the data once they were collected. Teachers reported that the data collection process was time-consuming and challenges associated with pedometers and information management systems served as barriers to the collection/organization process. In addition, professional development was not utilized to help teachers use data for effective teaching as district coordinators had limited access to teachers on designated professional development days. It is important to note that teachers had substantial concerns surrounding the validity and reliability of the data that were collected. This likely contributed to the low value that was placed upon data. Based upon the findings, ten recommendations for the enhancement of the DDDM process in physical education were generated. One of the most important recommendations is to provide physical education teachers with support in developing data literacy skills so they can take full advantage of the data they collect for the benefit of student learning.
Trinity River Basin Environmental Flows Information Collective
(Center for Research in Water Resources, University of Texas at Austin, 2008-08) Hersh, Eric S.; Marney, Katherine Anne; Maidment, David R.