Department of Library and Information Science Works

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https://hdl.handle.net/1805/1876

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Browsing Department of Library and Information Science Works by Title

Now showing 1 - 10 of 239
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    A measurement of faculty views on the meaning and value of student privacy
    (Springer, 2022-06-04) Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika; Library and Information Science, School of Computing and Informatics
    Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In the findings, we detail faculty perspectives of their privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe privacy is important to intellectual behaviors and learning, but the discussion argues that faculty make choices that put students at risk. While there seems to be a “privacy paradox,” our discussion argues that faculty are making assumptions about existing privacy protections and making instructional choices that could harm students because their “risk calculus” is underinformed. We conclude the article with recommendations to improve a faculty member’s privacy decision-making strategies and improve institutional conditions for student privacy.
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    A systematic review of library makerspaces research
    (Elsevier, 2022-10) Kim, Soo Hyeon; Jung, Yong Ju; Choi, Gi Woong; Library and Information Science, School of Computing and Informatics
    Despite the abundance of research on library makerspaces, systematic reviews of library makerspace research are lacking. As research on library makerspaces advances, the field needs reliable empirical findings to examine the impact of library makerspaces and identify research areas that are valuable for future research. Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, 43 out of 838 records were selected for the systematic review. The overall trend of research methodologies and theories, settings, participants, research purposes, as well as tools, technologies and programming in library makerspace research were identified. The findings reveal that qualitative studies that were descriptive in nature were the predominant approaches. While appropriate literatures were explored, theoretical frameworks were less used. This systematic review contributes new areas and directions for future research, including the need for expansion of research methodologies and theoretical frameworks and investigation of diverse users and types of making.
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    'Academic Library Support Staff Competencies: What Should Support Staff Know and be Able to Do?
    (Association of College and Research Libraries, 2009) Applegate, Rachel
    This research reports on data from a recent widely-disseminated survey of academic and public librarians and library support staff. The paper describes what professional competencies respondents considered most (and least) important for support staff. It shows what are the most-highly-rated items overall, and examines areas where opinions differed the most, comparing academic and public libraries, and support staff, MLS, and director respondents. Connected to the ALA Library Support Staff Certification Project.
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    Academic Technology: The Convergence of Diverse Disciplines
    (2003-04) Johnson, Larry; Lamb, Annette; Teclehaimanot, Berhane
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    Addressing Diverse Needs: Differentiation in Distance Learning
    (2009) Lamb, Annette; Johnson, Larry
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    Advising the whole student: eAdvising analytics and the contextual suppression of advisor values
    (Springer, 2018) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies.
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    Alaska to Afghanistan: Maps of Real Places in Reading and Technology
    (2014-12) Lamb, Annette; Johnson, Larry
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    Analysis of public library users’ digital preservation practices
    (2011-05) Copeland, Andrea J.
    This research investigated preservation practices of personal digital information by public library users. This qualitative study used semistructured interviews and two visual representation techniques, information source horizons and matrices, for data collection. The constant comparison method and descriptive statistics were used to analyze the data. A model emerged which describes the effects of social, cognitive, and affective influences on personal preservation decisions as well as the effects of fading cognitive associations and technological advances, combined with information escalation over time. Because the preservation of personal digital information involves personal, social, and technological interactions, the integration of these factors is necessary for a viable solution to the digital preservation problem.
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    Application of Edge-to-Cloud Methods Toward Deep Learning
    (IEEE, 2022-10) Choudhary, Khushi; Nersisyan, Nona; Lin, Edward; Chandrasekaran, Shobana; Mayani, Rajiv; Pottier, Loic; Murillo, Angela P.; Virdone, Nicole K.; Kee, Kerk; Deelman, Ewa; Library and Information Science, School of Computing and Informatics
    Scientific workflows are important in modern computational science and are a convenient way to represent complex computations, which are often geographically distributed among several computers. In many scientific domains, scientists use sensors (e.g., edge devices) to gather data such as CO2 level or temperature, that are usually sent to a central processing facility (e.g., a cloud). However, these edge devices are often not powerful enough to perform basic computations or machine learning inference computations and thus applications need the power of cloud platforms to generate scientific results. This work explores the execution and deployment of a complex workflow on an edge-to-cloud architecture in a use case of the detection and classification of plankton. In the original application, images were captured by cameras attached to buoys floating in Lake Greifensee (Switzerland). We developed a workflow based on that application. The workflow aims to pre-process images locally on the edge devices (i.e., buoys) then transfer data from each edge device to a cloud platform. Here, we developed a Pegasus workflow that runs using HTCondor and leveraged the Chameleon cloud platform and its recent CHI@Edge feature to mimic such deployment and study its feasibility in terms of performance and deployment.
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