Advising the whole student: eAdvising analytics and the contextual suppression of advisor values
- 103 Downloads
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.
KeywordsHigher education Advising Learning analytics Educational data mining Professional values
Arizona State University
Georgia State University
Responsibility Center Management
Radio Frequency Identification
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.
Compliance with ethical standards
Conflict of interest
The author declares that he has no conflict of interest.
- Aguilar, S., Lonn, S., & Teasley, S. D. (2014). Perceptions and use of an early warning system during a higher education transition program. Proceedings from LAK’14: The Fourth International Conference on Learning Analytics and Knowledge (pp. 113–117). New York, NY: ACM.Google Scholar
- Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Proceeding from LAK’12: The 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). New York, NY: ACM.Google Scholar
- Belkin, D. (2015). Cracking down on skipping class. The Wall Street Journal. Retrieved from http://www.wsj.com/articles/cracking-down-on-skipping-class-1421196743
- Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. U.S. Department of Education, Office of Educational Technology. Retrieved from http://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf. Accessed 15 March 2018.
- Brown, S. (2017). Where every student is a potential data point. The Chronicle of Higher Education. Retrieved from http://www.chronicle.com/article/Where-Every-Student-Is-a/239712. Accessed 15 March 2018.
- Burns, B., Crow, M., & Becker, M. (2015). Innovating together: Collaboration as a driving force to improve student success. EDUCAUSE Review, 50(2), 10–20 Retrieved from https://er.educause.edu/articles/2015/3/innovating-together-collaboration-as-a-driving-force-to-improve-student-success. Accessed 15 March 2018.Google Scholar
- Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57 Retrieved from http://er.educause.edu/~/media/files/article-downloads/erm0742.pdf. Accessed 15 March 2018.Google Scholar
- Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis (1st ed.). Thousand Oaks: SAGE Publications.Google Scholar
- Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Thousand Oaks: SAGE Publications.Google Scholar
- Cheslock, J., Hughes, R. P., & Umbricht, M. (2014). The opportunities, challenges, and strategies associated with the use of operations-oriented (big) data to support decision making within universities. In J. E. Lane (Ed.), Building a smarter university: big data, innovation, and analytics (pp. 211–238). Albany: State University of New York Press.Google Scholar
- Conner, T. W., & Rabovsky, T. M. (2011). Accountability, affordability, access: A review of the recent trends in higher education policy research. Policy Studies Journal, 39(s1), 93–112. https://doi.org/10.1111/j.1541-0072.2010.00389_7.x.CrossRefGoogle Scholar
- Cook, H. (2016). University students, you are being watched. The Sydney Morning Herald. Retrieved from http://www.smh.com.au/national/university-students-you-are-being-watched-20160811-gqqet7.html. Accessed 15 March 2018.
- Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Los Angeles: SAGE Publications.Google Scholar
- Denley, T. (2013). Degree Compass: A course recommendation system. EDUCAUSE Review Online. Retrieved from http://er.educause.edu/articles/2013/9/degree-compass-a-course-recommendation-system. Accessed 15 March 2018.
- Eckersen, W. W. (2007). Predictive analytics: Extending the value of your data warehousing investment. TDWI Best Practices Report, First Quarter 2007. Retrieved from https://tdwi.org/research/2007/01/bpr-1q-predictive-analytics-executive-summary.aspx. Accessed 15 March 2018.
- Ekowo, M., & Palmar, I. (2016). The promise and peril of predictive analytics in higher education: A landscape analysis. New America. Retrieved from https://na-production.s3.amazonaws.com/documents/Promise-and-Peril_4.pdf. Accessed 15 March 2018.
- Felton, E. (2015). The new tool colleges are using in admissions data decisions: Big data. PBS Newshour. Retrieved from http://www.pbs.org/newshour/updates/new-tool-colleges-using-admissions-decisions-big-data/. Accessed 15 March 2018.
- Ferguson, R., & Clow, D. (2016). Learning analytics community exchange: Evidence hub. Proceeding in LAK ‘16: The Sixth International Conference on Learning Analytics & Knowledge. New York, NY: ACM, https://doi.org/10.1145/2883851.2883878
- Ferguson, R., & Clow, D. (2017). Where is the evidence?: A call to action for learning analytics. Proceeding in LAK ‘17: The Seventh International Learning Analytics & Knowledge Conference (pp. 56–65). New York, NY: ACM, https://doi.org/10.1145/3027385.3027396
- Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., & Ullmann, T. (2016). Research evidence on the use of learning analytics – implications for education policy. R. Vuorikari, & J. Castaño Muñoz (Eds.), Joint Research Centre Science for Policy Report, EUR 28294 EN, https://doi.org/10.2791/955210
- Fonseca, F., & Marcinkowski, M. (2014). Who is the big data student. In J. E. Lane (Ed.), Building a smarter university: Big data, innovation, and analytics (pp. 121–142). Albany: SUNY Press.Google Scholar
- Friedman, B., & Kahn Jr., P. H. (1997). Human values, ethics, and design. In J. A. Jacko & A. Sears (Eds.), The human-computer interaction handbook (pp. 1177–1201). Hillsdale: L. Erlbaum Associates Inc..Google Scholar
- Goff, J. W., & Shaffer, C. M. (2014). Big data’s impact on college admission practices and recruitment strategies. In J. E. Lane (Ed.), Building a smarter university: Big data, innovation, and analytics (pp. 93–120). Albany: State University of New York Press.Google Scholar
- Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. EDUCAUSE Center for Applied Research, 8. Retrieved from https://library.educause.edu/resources/2005/12/academic-analytics-the-uses-of-management-information-and-technology-in-higher-education. Accessed 15 March 2018.
- Hall, E. (2016). University of Melbourne defends wi-fi tracking of students as planning move amid privacy concerns. ABC News–Australia. Retrieved from http://www.abc.net.au/news/2016-08-12/university-of-melbourne-tracking-students-through-wifi/7723468. Accessed 15 March 2018.
- Harrell, K., & Holcroft, C. (2012). Searching for an authentic definition of student success. Senate Rostrum: Academic Senate for California Community Colleges Newsletter, 16–17. Retrieved from http://www.asccc.org/content/searching-authentic-definition-student-success. Accessed 15 March 2018.
- Hoover, E. (2015). Getting inside the mind of an applicant. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Getting-Inside-the-Mind-of-an/233403. Accessed 15 March 2018.
- Jones, K. M. L., & LeClere, E. (2018). Contextual expectations and emerging informational harms: A primer on academic library participation in learning analytics initiatives. In P. Fernandez & K. Tilton (Eds.), Applying library values to emerging technology: Applying library values to emerging technology: Decision-making in the age of open access, maker spaces, and the ever-changing library. Chicago: Association of College and Research Libraries.Google Scholar
- Kamenetz, A. (2014). Chasing the elusive ‘quality’ in online education. NPR. Retrieved from http://www.npr.org/sections/ed/2014/06/27/323329818/chasing-the-elusive-quality-in-online-education. Accessed 15 March 2018.
- Kamenetz, A. (2016). How one university used big data to boost graduation rates. NPR. Retrieved from http://www.npr.org/sections/ed/2016/10/30/499200614/how-one-university-used-big-data-to-boost-graduation-rates. Accessed 15 March 2018.
- Kraft-Terry, S., & Kau, C. (2016). Manageable steps to implementing data-informed advising. NACADA. Retrieved from http://www.nacada.ksu.edu/Resources/Clearinghouse/View-Articles/Manageable-Steps-to-Implementing-Data-Informed-Advising.aspx. Accessed 15 March 2018.
- Lane, J. E., & Finsel, B. A. (2014). Fostering smarter colleges and universities: Data, big data, and analytics. In J. E. Lane (Ed.), Building a smarter university: Big data, innovation, and analytics (pp. 3–26). Albany: State University of New York Press.Google Scholar
- Lanier, J. (2010). You are not a gadget: A manifesto. New York: Alfred A. Knopf.Google Scholar
- Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40 Retrieved from https://er.educause.edu/~/media/files/article-downloads/erm1151.pdf . Accessed 15 March 2018.Google Scholar
- Lorin, J. (2014). College tuition in the U.S. against rises faster than inflation. Bloomberg. Retrieved from https://www.bloomberg.com/news/articles/2014-11-13/college-tuition-in-the-u-s-again-rises-faster-than-inflation. Accessed 15 March 2018.
- Love, P. (2008). Diverse perspectives on student success. Presented at the Faculty Resource Network National Symposium, San Francisco, CA. Retrieved from http://www.nyu.edu/frn/publications/defining.success/Love.plenary.html. Accessed 15 March 2018.
- Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149–163 Retrieved from http://www.jstor.org/stable/pdf/jeductechsoci.15.3.149.pdf. Accessed 15 March 2018.Google Scholar
- Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data: The future of education. New York: Houghton Mifflin Harcourt.Google Scholar
- McRae, P. (2013). Rebirth of the teaching machine through the seduction of data analytics: This time it’s personal [Blog post]. Retrieved from http://philmcrae.com/2/post/2013/04/rebirth-of-the-teaching-maching-through-the-seduction-of-data-analytics-this-time-its-personal1.html. Accessed 15 March 2018.
- Mitchell, M., Leachman, M., & Masterson, K. (2016). Funding down, tuition up: State cuts to higher education threaten quality and affordability at public colleges. Center on Budget and Policy Priorities. Retrieved from http://www.cbpp.org/research/state-budget-and-tax/funding-down-tuition-up. Accessed 15 March 2018.
- NACADA. (2005). The statement of core values of academic advising. Retrieved from http://www.nacada.ksu.edu/Resources/Clearinghouse/View-Articles/Core-values-of-academic-advising.aspx. Accessed 15 March 2018.
- Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119–158 Retrieved from http://heinonline.org/HOL/P?h=hein.journals/washlr79&i=129. Accessed 15 March 2018.Google Scholar
- Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford: Stanford University Press.Google Scholar
- Oliff, P., Palacios, V., Johnson, I., & Leachman, M. (2013). Recent deep state higher education cuts may harm students and the economy for years to come. Center on Budget and Policy Priorities. Retrieved from https://www.cbpp.org/sites/default/files/atoms/files/3-19-13sfp.pdf. Accessed 15 March 2018.
- Parry, M. (2012). Big data on campus. The New York Times. Retrieved from http://www.nytimes.com/2012/07/22/education/edlife/colleges-awakening-to-the-opportunities-of-data-mining.html. Accessed 15 March 2018.
- Renick, T. M. (2014). GPS advising at Georgia State University. Retrieved from http://oie.gsu.edu/files/2014/04/Advisement-GPS.pdf. Accessed 15 March 2018.
- Rubel, A., & Jones, K. (2017). Data analytics in higher education: Key concerns and open questions. University of St. Thomas Journal of Law and Public Policy, 11(1), 25–44. Retrieved from https://ir.stthomas.edu/ustjlpp/vol11/iss1/2/. Accessed 15 March 2018.
- Selwyn, N. (2014). Distrusting educational technology: Critical questions for changing times. New York: Routledge.Google Scholar
- Selwyn, N. (2017). Education and technology: Key issues and debates (2nd ed.). New York: Bloomsbury Academic.Google Scholar
- Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. Proceeding in LAK ‘12: The 2nd International Conference on Learning Analytics and Knowledge (pp. 4–8). New York, NY: ACM, https://doi.org/10.1145/2330601.2330605
- Sydney, T. D., de Brey, C., & Dillow, S. A. (2016). Postsecondary education. In the Digest of Education Statistics, 2015 (NCES 2016–014) (51st ed.) (pp. 437–754). Washington, DC: National Center for Education Statistics (NCES), Institute of Education Sciences (IES), U.S. Department of Education. Retrieved from https://nces.ed.gov/pubs2016/2016014.pdf. Accessed 15 March 2018.
- University Innovation Alliance. (n.d.). Collaborative project goal: Predictive analytics. Retrieved from http://www.theuia.org/sites/default/files/UIA_predictive_onepagers.pdf. Accessed 15 March 2018.
- Unizin. (2018). McGraw-Hill Education and Unizin partner to make affordable digital learning materials available to 25 universities and nearly 1 million students [Press release]. Retrieved from http://unizin.org/press_release/mcgraw-hill-education-unizin-partner-make-affordable-digital-learning-materials-available-25-universities-nearly-1-million-students/. Accessed 15 March 2018.
- van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. ELI Paper 1: 2012. EDUCAUSE Learning Initiative (ELI). Retrieved from https://library.educause.edu/~/media/files/library/2012/1/eli3026-pdf.pdf. Accessed 15 March 2018.
- Wallace, S. O., & Wallace, B. A. (2016). Defining student success. In T. J. Grites, M. A. Miller, & J. G. Voler (Eds.), Beyond foundations: Developing as a master academic advisor (pp. 83–106). Hoboken: Jossey-Bass.Google Scholar
- Walsham, G. (1993). Interpreting information systems in organizations. Chichester: Wiley.Google Scholar
- Wyatt, S. (2003). Non-users also matter: The construction of users and non-users of the internet. In N. Oudshoorn & T. Pinch (Eds.), How users matter: The co-construction of users and technology (pp. 67–79). Cambridge: MIT Press.Google Scholar
- Young, J. R. (2011). The Netflix effect: When software suggests students’ courses. The Chronicle of Higher Education. Retrieved from http://www.chronicle.com/article/The-Netflix-Effect-When/127059/. Accessed 15 March 2018.