Browsing by Author "Jones, Kyle M. L."

Now showing 1 - 10 of 24
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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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    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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    Asymmetries in Online Job-Seeking: A Case Study of Muslim-American Women
    (ACM, 2021-10) Afnan, Tanisha; Rabaan, Hawra; Jones, Kyle M. L.; Dombrowski, Lynn; Human-Centered Computing, School of Informatics and Computing
    As job-seeking and recruiting processes transition into digital spaces, concerns about hiring discrimination in online spaces have developed. Historically, women of color, particularly those with marginalized religious identities, have more challenges in securing employment. We conducted 20 semi-structured interviews with Muslim-American women of color who had used online job platforms in the past two years to understand how they perceive digital hiring tools to be used in practice, how they navigate the US job market, and how hiring discrimination as a phenomenon is thought to relate to their intersecting social identities. Our findings allowed us to identify three major categories of asymmetries (i.e., the relationship between the computing algorithms' structures and their users' experiences): (1) process asymmetries, which is the lack of transparency in data collection processes of job applications; (2) information asymmetries, which refers to the asymmetry in data availability during online job-seeking; and (3) legacy asymmetries, which explains the cultural and historical factors impacting marginalized job applicants. We discuss design implications to support job seekers in identifying and securing positive employment outcomes.
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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 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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    The Development of an Undergraduate Data Curriculum: A Model for Maximizing Curricular Partnerships and Opportunities
    (Springer, 2018) Murillo, Angela P.; Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    The article provides the motivations and foundations for creating an interdisciplinary program between a Library and Information Science department and a Human-Centered Computing department. The program focuses on data studies and data science concepts, issues, and skill sets. In the paper, we analyze trends in Library and Information Science curricula, the emergence of data-related Library and Information Science curricula, and interdisciplinary data-related curricula. Then, we describe the development of the undergraduate data curriculum and provide the institutional context; discuss collaboration and resource optimization; provide justifications and workforce alignment; and detail the minor, major, and graduate opportunities. Finally, we argue that the proposed program holds the potential to model interdisciplinary, holistic data-centered curriculum development by complimenting Library and Information Science traditions (e.g., information organization, access, and ethics) with scholarly work in data science, specifically data visualization and analytics. There is a significant opportunity for Library and Information Science to add value to data science and analytics curricula, and vice versa.
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    Do They Even Care? Measuring Instructor Value of Student Privacy in the Context of Learning Analytics
    (AIS, 2021) Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Library and Information Science, School of Informatics and Computing
    Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether or not 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. The findings detail faculty perspectives of their own privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe that privacy is important to intellectual behaviors and learning. This work reports initial findings of a multi-phase, grant-funded research project that will further uncover instructor views of learning analytics and its student privacy issues.
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    Introduction
    (Project Muse, 2019) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
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    "Just Because You Can Doesn't Mean You Should": Practitioner Perceptions of Learning Analytics Ethics
    (JHU Press, 2019) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    Learning analytics involve the process of gathering data about students and using the information to intervene in their lives to improve learning and institutional outcomes. Many academic libraries now participate in learning analytics. However, such practices raise privacy and intellectual freedom issues due to sensitive data practices. But, few research studies address how library practitioners perceive the ethical issues. This article does so by analyzing interviews with library practitioners. The findings suggest that library professionals seek ethical "bright lines"—that is, clearly defined standards—where few exist and that ethical guidance is limited. Though library practitioners recognize that data practices should be scoped and justified, their efforts have come under severe scrutiny—and sometimes harassment—from their professional peers. The article highlights why ethical dissonance has emerged in the profession regarding learning analytics and how library practices might better account for the harms and benefits of learning analytics.