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Advising the Whole Student: eAdvising Analytics and the Contextual Suppression of Advisor Values

43 Pages Posted: 16 Aug 2018 First Look: Under Review

See all articles by Kyle M. L. Jones

Kyle M. L. Jones

Indiana University-Indianapolis (IUPUI)

Abstract

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.

Keywords: Higher Education, Advising, Learning Analytics, Educational Data Mining, Professional Values

Suggested Citation

Jones, Kyle, Advising the Whole Student: eAdvising Analytics and the Contextual Suppression of Advisor Values (2018). Jones, K. M. L. Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 2018 DOI/ 10.1007/s10639-018-9781-8 . Available at SSRN: https://ssrn.com/abstract=3222866

Kyle Jones (Contact Author)

Indiana University-Indianapolis (IUPUI) ( email )

535 W. Michigan Street
Indianapolis, IN 46202-3103
United States

HOME PAGE: http://thecorkboard.org

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