Browsing by Author "Briney, Kristin A."

Now showing 1 - 5 of 5
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    A Comprehensive Primer to Library Learning Analytics Practices, Initiatives, and Privacy Issues
    (American Library Association, 2020-04) Jones, Kyle M. L.; Briney, Kristin A.; Goben, Abigail; Salo, Dorothea; Asher, Andrew; Perry, Michael R.; Library and Information Science, School of Informatics and Computing
    Universities are pursuing learning analytics practices to improve returns from their investments, develop behavioral and academic interventions to improve student success, and address political and financial pressures. Academic libraries are additionally undertaking learning analytics to demonstrate value to stakeholders, assess learning gains from instruction, and analyze student-library usage, et cetera. The adoption of these techniques leads to many professional ethics issues and practical concerns related to privacy. In this narrative literature review, we provide a foundational background in the field of learning analytics, library adoption of these practices, and identify ethical and practical privacy issues.
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    Data Management Planning for an Eight-Institution, Multi-Year Research Project
    (OJS, 2022-09-07) Briney, Kristin A.; Goben, Abigail; Jones, Kyle M. L.; Library and Information Science, School of Computing and Informatics
    While data management planning for grant applications has become commonplace alongside articles providing guidance for such plans, examples of data plans as they have been created, implemented, and used for specific projects are only beginning to appear in the scholarly record. This article describes data management planning for an eight-institution, multi-year research project. The project leveraged four data management plans (DMP) in total, one for the funding application and one for each of the three distinct project phases. By understanding researcher roles, development and content of each DMP, team internal and external challenges, and the overall benefits of creating and using the plans, these DMPs provide a demonstration of the utility of this project management tool.
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    Foundational Practices of Research Data Management
    (Pensoft, 2020-07-27) Briney, Kristin A.; Coates, Heather L.; Goben, Abigail
    The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.
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    Questions of Trust: A Survey of Student Expectations and Perspectives on Library Learning Analytics
    (University of Chicago Press, 2022-04) Asher, Andrew D.; Briney, Kristin A.; Jones, Kyle M. L.; Regalado, Mariana; Perry, Michael R.; Goben, Abigail; Smale, Maura A.; Salo, Dorothea; Library and Information Science, School of Computing and Informatics
    Universities are developing learning analytics initiatives that include academic library participation. Libraries rarely inform their students about learning analytics projects or general library data practices. Without a clear student voice in library learning analytics projects, libraries and librarians are creating potential privacy complications. This study seeks to document students’ thoughts on academic library participation in learning analytics and privacy concerns. A survey was developed and fielded at eight US higher education institutions, and this article covers the findings from the approximately 2,200 responses. Although most students reported high levels of trust in libraries and librarians, a consistent minority indicated little or no trust at all. Findings demonstrate that students considered librarian access to and sharing of personally identifiable information to constitute a privacy violation but also lacked awareness of the data and analytic practices on which libraries rely. Notable demographic differences were also discovered.
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    “We're being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education
    (Wiley, 2020) Jones, Kyle M. L.; Asher, Andrew; Goben, Abigail; Perry, Michael R.; Salo, Dorothea; Briney, Kristin A.; Robertshaw, M. Brooke; Library and Information Science, School of Informatics and Computing
    Higher education institutions are continuing to develop their capacity for learning analytics (LA), which is a sociotechnical data‐mining and analytic practice. Institutions rarely inform their students about LA practices, and there exist significant privacy concerns. Without a clear student voice in the design of LA, institutions put themselves in an ethical gray area. To help fill this gap in practice and add to the growing literature on students' privacy perspectives, this study reports findings from over 100 interviews with undergraduate students at eight U.S. higher education institutions. Findings demonstrate that students lacked awareness of educational data‐mining and analytic practices, as well as the data on which they rely. Students see potential in LA, but they presented nuanced arguments about when and with whom data should be shared; they also expressed why informed consent was valuable and necessary. The study uncovered perspectives on institutional trust that were heretofore unknown, as well as what actions might violate that trust. Institutions must balance their desire to implement LA with their obligation to educate students about their analytic practices and treat them as partners in the design of analytic strategies reliant on student data in order to protect their intellectual privacy.