Browsing by Subject "learning analytics"

Now showing 1 - 10 of 11
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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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    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.
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    Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy
    (Springer, 2019) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    By tracking, aggregating, and analyzing student profiles along with students’ digital and analog behaviors captured in information systems, universities are beginning to open the black box of education using learning analytics technologies. However, the increase in and usage of sensitive and personal student data present unique privacy concerns. I argue that privacy-as-control of personal information is autonomy promoting, and that students should be informed about these information flows and to what ends their institution is using them. Informed consent is one mechanism by which to accomplish these goals, but Big Data practices challenge the efficacy of this strategy. To ensure the usefulness of informed consent, I argue for the development of Platform for Privacy Preferences (P3P) technology and assert that privacy dashboards will enable student control and consent mechanisms, while providing an opportunity for institutions to justify their practices according to existing norms and values.
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    Learning Analytics and Its Paternalistic Influences
    (Springer, 2017-07) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    Learning analytics is a technology that employs paternalistic nudging techniques and predictive measures. These techniques can limit student autonomy, may run counter to student interests and preferences, and do not always distribute benefits back to students–in fact some harms may actually accrue. The paper presents three cases of paternalism in learning analytics technologies, arguing that paternalism is an especially problematic concern for higher education institutions who espouse liberal education values. Three general recommendations are provided that work to promote student autonomy and choice making as a way to protect against risks to student academic freedom.
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    Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads
    (ACRL, 2018) Jones, Kyle M. L.; Library and Information Science, School of Informatics and Computing
    In this paper, the authors address learning analytics and the ways academic libraries are beginning to participate in wider institutional learning analytics initiatives. Since there are moral issues associated with learning analytics, the authors consider how data mining practices run counter to ethical principles in the American Library Association’s “Code of Ethics.” Specifically, the authors address how learning analytics implicates professional commitments to promote intellectual freedom; protect patron privacy and confidentiality; and balance intellectual property interests between library users, their institution, and content creators and vendors. The authors recommend that librarians should embed their ethical positions in technological designs, practices, and governance mechanisms.
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    A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics
    (Wiley, 2020) Jones, Kyle M. L.; Rubel, Alan; LeClere, Ellen; Library and Information Science, School of Informatics and Computing
    Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.
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    New Methods, New Needs: Preparing Academic Library Practitioners to Address Ethical Issues Associated with Learning Analytics
    (ALISE, 2020-10) Jones, Kyle M. L.; Janicke Hinchliffe, Lisa; Library and Information Science, School of Informatics and Computing
    Academic libraries are participating in the collection and analysis of student data. Under the umbrella of learning analytics, these practices are directed toward developing an understanding of how libraries contribute to student learning, the educational experience, and efficient operations of academic institutions. Learning analytics, however, is loaded with ethical issues, which are complicated by privacy-related values espoused by library practitioners. This work-in-progress paper discusses emerging findings from a survey of academic library practitioners. The survey identifies what ethical issues practitioners associate with leaning analytics and the degree to which they are prepared to address such issues.
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    Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework
    (Taylor & Francis, 2019) Jones, Kyle M. L.; McCoy, Chase; Library and Information Science, School of Informatics and Computing
    In this article, we argue that the contributions of documentation studies can provide a useful framework for analyzing the datafication of students due to emerging learning analytics (LA) practices. Specifically, the concepts of individuals being ‘made into’ data and how that data is ‘considered as’ can help to frame vital questions concerning the use of student data in LA. More specifically, approaches informed by documentation studies will enable researchers to address the sociotechnical processes underlying how students are constructed into data, and ways data about students are considered and understood. We draw on these concepts to identify and describe three areas for future research in LA. With the description of each area, we provide a brief analysis of current practices in American higher education, highlighting how documentation studies enables deeper analytical digging.