Browsing by Author "Library and Information Science, School of Informatics and Computing"

Now showing 1 - 10 of 49
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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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    Blueprint: Cyberinfrastructure Center of Excellence
    (Zenodo, 2021-03) Deelman, Ewa; Mandal, Anirban; Murillo, Angela P.; Nabrzyski, Jarek; Pascucci, Valerio; Ricci, Robert; Baldin, Ilya; Sons, Susan; Christopherson, Laura; Vardeman, Charles; Ferreira da Silva, Rafael; Wyngaard, Jane; Petruzza, Steve; Rynge, Mats; Vahi, Karan; Whitcup, Wendy R.; Drake, Josh; Scott, Erik; Library and Information Science, School of Informatics and Computing
    In 2018, NSF funded an effort to pilot a Cyberinfrastructure Center of Excellence (CI CoE or Center) that would serve the cyberinfrastructure (CI) needs of the NSF Major Facilities (MFs) and large projects with advanced CI architectures. The goal of the CI CoE Pilot project (Pilot) effort was to develop a model and a blueprint for such a CoE by engaging with the MFs, understanding their CI needs, understanding the contributions the MFs are making to the CI community, and exploring opportunities for building a broader CI community. This document summarizes the results of community engagements conducted during the first two years of the project and describes the identified CI needs of the MFs. To better understand MFs' CI, the Pilot has developed and validated a model of the MF data lifecycle that follows the data generation and management within a facility and gained an understanding of how this model captures the fundamental stages that the facilities' data passes through from the scientific instruments to the principal investigators and their teams, to the broader collaborations and the public. The Pilot also aimed to understand what CI workforce development challenges the MFs face while designing, constructing, and operating their CI and what solutions they are exploring and adopting within their projects. Based on the needs of the MFs in the data lifecycle and workforce development areas, this document outlines a blueprint for a CI CoE that will learn about and share the CI solutions designed, developed, and/or adopted by the MFs, provide expertise to the largest NSF projects with advanced and complex CI architectures, and foster a community of CI practitioners and researchers.
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    Caregivers’ Role-taking during the Use of Discussion Prompts in At-Home Engineering Kits
    (ISLS, 2021-06) Kim, Soo Hyeon; Kim, Jungsun; Library and Information Science, School of Informatics and Computing
    This study presents a video-based case study of families who used discussion prompts in the at-home engineering kits. We examine different roles that caregivers took on during the implementation of the prompts to organize families’ engineering learning activities. Narrative accounts and transcriptions were analyzed to investigate the different roles that caregivers took. Three roles emerged: caregivers as monitor; caregivers as mentor; caregivers as partner. We further coded families’ talks to investigate how three different caregivers’ roles influenced families’ engineering practices and caregiver-child talk types. Preliminary findings illustrate how three caregivers’ roles enabled and constrained different types of engineering practices and caregiver-child talk types. Findings contribute to future considerations in designing discussion prompts for at-home engineering kits.
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    Celebrating the Nineteenth Amendment and Women's Suffrage
    (2019-04) Lamb, Annette; Library and Information Science, School of Informatics and Computing
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    Collaborative idea exchange and material tinkering influence families’ creative engineering practices and products during engineering programs in informal learning environments
    (Emerald, 2021) Kim, Soo Hyeon; Toomey Zimmerman, Heather; Library and Information Science, School of Informatics and Computing
    Purpose This paper aims to investigate how families’ sociomaterial experiences in engineering programs held in libraries and a museum influence their creative engineering practices and the creativity expressed in their products derived from their inquiry-driven engineering activities. Design/methodology/approach This research project takes a naturalistic inquiry using qualitative and quantitative analyses based on video records from activities of 31 parent–child pairs and on creativity assessment of products that used littleBits as prototyping tools. Findings Families engaged in two sociomaterial experiences related to engineering – collaborative idea exchange and ongoing generative tinkering with materials – which supported the emergence of novel ideas and feasible solutions during the informal engineering programs. Families in the high novelty score group experienced multiple instances of collaborative idea exchange and ongoing generative tinkering with materials, co-constructed through parent-child collaboration, that were expansive toward further idea and solution generation. Families in the low novelty score group experienced brief collaborative idea exchange and material tinkering with specific idea suggestions and high involvement from the parent. An in-depth case study of one family further illustrated that equal engagement by the parent and child as they tinkered with the technology supported families’ creative engineering practices. Originality/value This analysis adds to the information sciences and learning sciences literatures with an account that integrates methodologies from sociocultural and engineering design research to understand the relationship between families’ engagement in creative engineering practices and their products. Implications for practitioners include suggestions for designing spaces to support families’ collaborative idea exchange and ongoing generative tinkering to facilitate the development of creative engineering practices during short-term engineering programs.
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    Collecting, organizing, and preserving diverse publication sources for the good of one community archive: Legal challenges and recommendations
    (2017) Copeland, Andrea; Lipinski, Tomas; Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
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    A Community-Oriented Approach to school libraries and standards
    (2019) Lamb, Annette; Library and Information Science, School of Informatics and Computing
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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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    Confronting the Challenges of Computational and Social Perspectives of the Data Continuum
    (Sciendo, 2020-06) Murillo, Angela P.; Curty, Renata G.; Jeng, Wei; He, Daqing; Library and Information Science, School of Informatics and Computing
    As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data acumen and data stewardship approaches. In an effort to examine the convergence, tensions, and harmonies of these two approaches, a group of researchers participated in an interactive panel session at the Association of Information Science and Technology Annual meeting in 2019. The panel presenters described their various research activities in which they confront the challenges of the computational and social perspectives of the data continuum. This paper provides a summary of this interactive panel.
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    The Convergence of Computational and Social Approaches for Unveiling Meaningful and Valuable Data
    (Wiley, 2019) Murillo, Angela P.; Curty, Renata G.; Jeng, Wei; He, Daqing; Library and Information Science, School of Informatics and Computing
    The current data paradigm is seeking a more integrated and comprehensive framework to make sense of data and its derived issues. From the perspective of the data life cycle, we argue that computational and social approaches complement each other to confront data challenges. Computational approaches consist of ETL (extract, transform, and load), modeling, and machine learning techniques; social approaches include policy and regulations, data sharing and reuse behavior, reproducibility, ethical and privacy issues. In this panel, we frame these two approaches as data acumen and data stewardship. The merging of these two perspectives allows data not only to become discoverable, accessible, and interoperable, but also to further the value of revealing meaningful patterns and become supportive evidence for important decision making. In this panel, the opening facilitator and three panelists will report on their recent studies in terms of this convergence of both data acumen and stewardship while sharing their recent research insights on case studies in three disciplines: agriculture, biomedicine, and archeology.