Learning Analytics and Its Paternalistic Influences

Jones, Kyle M. L.

doi:10.1007/978-3-319-58515-4_22

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Pushing forward with learning analytics without considering student privacy preferences-or ignoring such preferences all together-is foolhardy and morally suspect. I will not go as far to say that privacy-lite learning analytics initiatives are meant to do harm, in fact they are most likely well-intentioned but misplaced paternalistic actions (Jones, 2017). However, not considering student privacy preferences runs counter to norms of respecting individual autonomy and expressions thereof in choice making.…”

Section: The Emerging Student Voicementioning

confidence: 96%

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

Jones

2019

Int J Educ Technol High Educ

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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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Section: The Emerging Student Voicementioning

confidence: 96%

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

Jones

2019

Int J Educ Technol High Educ

Self Cite

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“…Extant arguments suggest that learning analytics and the algorithms that nudge students towards particular courses and programs and away from others create student autonomy issues (Jones, 2017;Rubel & Jones, 2016). The predictive models that score students also raise autonomy concerns, but they also bring to light digital redlining issues when such scores bias instructors in their allocation of time and resources for particular students (Gilliard & Culik, 2016).…”

Section: Data Ethics and Privacy Problemsmentioning

confidence: 99%

The syllabus as a student privacy document in an age of learning analytics

Jones

VanScoy

2019

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Purpose The purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi. Design/methodology/approach The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus. Findings Most syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments. Research limitations/implications The transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses. Practical implications The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities. Social implications Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience. Originality/value In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices.

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Ethical issues in learning analytics: a review of the field

Tzimas

Demetriadis

2021

Education Tech Research Dev

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Learning Analytics and Its Paternalistic Influences

Cited by 3 publications

References 19 publications

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

The syllabus as a student privacy document in an age of learning analytics

Ethical issues in learning analytics: a review of the field

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