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.
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Notes
It may also be the case that while student learning improves, the cost to deploy LA tools and strategies does not reap financial savings. In this case, LA may not be justifiable given stakeholder pressures to reduce the cost of earning a higher education degree. To recoup the lost savings, it is plausible that institutions may resort to selling data or data products, or negotiate for a lesser amount for vendor products and services in exchange for data; there is some evidence of this already (see Unizin 2018). For more on this argument, see Rubel and Jones (2017) or Jones and Salo (2018).
“Student Success Forecast” is a pseudonym.
Abbreviations
- ASU:
-
Arizona State University
- GSU:
-
Georgia State University
- LA:
-
Learning Analytics
- RCM:
-
Responsibility Center Management
- RFID:
-
Radio Frequency Identification
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Acknowledgements
The author sincerely thanks the study’s anonymous participants, who provided their time and openly shared their stories and experiences. Additionally, the author expresses his gratitude to Roderic Crooks and Rachel Applegate for critically reviewing early drafts of this article.
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Jones, K.M.L. Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Educ Inf Technol 24, 437–458 (2019). https://doi.org/10.1007/s10639-018-9781-8
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DOI: https://doi.org/10.1007/s10639-018-9781-8