What’s the Problem with Learning Analytics?

Main Article Content

Neil Selwyn

Abstract

This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective — positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning analytics to entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning “data economy.” In light of these charges, the article briefly considers some possible areas of change. These include the design of analytics applications that are more open and accessible, that offer genuine control and oversight to users, and that better reflect students’ lived reality. The article also considers ways of rethinking the political economy of the learning analytics industry. Above all, learning analytics researchers need to begin talking more openly about the values and politics of data-driven analytics technologies as they are implemented along mass lines throughout school and university contexts.

Article Details

How to Cite
Selwyn, N. (2019). What’s the Problem with Learning Analytics?. Journal of Learning Analytics, 6(3), 11–19. https://doi.org/10.18608/jla.2019.63.3
Section
Invited Dialogue: "What's the Problem with Learning Analytics?" (Selwyn, 2019)

References

Beer, D. (2018). The data gaze. New York: Sage.

Couldry, N., & Mejias, U. (2019). The costs of connection. Palo Alto CA: Stanford University Press.
Crawford, K. (2017). Why AI is still waiting for its ethics transplant. Wired, 1 Nov. 2017. www.wired.com/story/why-ai-is-still-waiting-for-its-ethics-transplant/

Eubanks, V. (2018). Automating inequality. New York: St. Martin’s Press.

Fourcade, M., & Healy, K. (2017). Classification situations: Life-chances in the neoliberal era. Historical Social Research/Historische Sozialforschung, Vol.42, No. 1 (159), Markets and Classifications. Categorizations and Valuations as Social Processes Structuring Markets (2017), pp. 23-51

Howard, P. (2017). Is social media killing democracy? Computational propaganda, algorithms, automation and public life. Inaugural lecture to the Oxford Internet Institute, 15 June 2017. www.youtube.com/watch?v=J1kXdA61AQY

Ivarsson, J. (2017). Algorithmic accountability. Lärande, 2 May 2017. http://lit.blogg.gu.se/2017/05/02/algorithmic-accountability/

Iveson, K. (2017). Digital labourers of the city, unite! In J. Shaw & M. Graham (Eds.), Our digital rights to the city (pp. 20–22). Oxford, UK: Meatspace Press.

Jarchow, T., & Estermann, B. (2015). Big data: Opportunities, risks and the need for action. Berner Fachhochschule, E-Government-Institut.

Li, F. (2017). Put humans at the centre of AI. MIT Technology Review, 9 Oct. 2017. https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai

Nemorin, S. (2016). Neuromarketing and the “poor in world” consumer. Consumption Markets & Culture, 20(1), 59–80. https:/dx.doi.org/10.1080/10253866.2016.1160897

Noble, S. (2018). Algorithms of oppression. New York: New York University Press.

Obar, J. (2015). Big data and the phantom public: Walter Lippmann and the fallacy of data privacy self-management. Big Data & Society, 2(2). http://dx.doi.org/10.1177/2053951715608876

O’Neil, C. (2016). Weapons of math destruction. New York: Broadway Books.

Pangrazio, L., & Selwyn, N. (2018). “Personal data literacies”: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2). http://dx.doi.org/10.1177/1461444818799523

Reidenberg, J., & Schaub, F. (2018). Achieving big data privacy in education. Theory and Research in Education, 16(3). http://dx.doi.org/10.1177/1477878518805308

Robinson, S. (2017). What’s your anonymity worth? Digital Policy, Regulation and Governance, 19(5), 353–366. https://dx.doi.org/10.1108/DPRG-05-2017-0018

Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64–82. https://dx.doi.org/10.1080/17439884.2014.921628

Selwyn, N. (2016). Is technology good for education? Cambridge, UK: Polity Press.

Selwyn, N. (2019). What is digital sociology? Cambridge, UK: Polity Press.

Shelton, T. (2017). Re-politicizing data. In J. Shaw & M. Graham (Eds.), Our digital rights to the city (pp. 24–27). Oxford, UK: Meatspace Press.

Sims, C. (2017). Disruptive fixation: School reform and the pitfalls of techno-idealism. Princeton, NJ: Princeton University Press.

Singer, N. (2018). Just don’t call it privacy. New York Times, 22 Sept. 2018. www.nytimes.com/2018/09/22/sunday-review/privacy-hearing-amazon-google.html?smid=tw-nytopinion&smtyp=cur

Smith, A. (2018). Franken-algorithms: The deadly consequences of unpredictable code. The Guardian, 30 Aug. 2018. www.theguardian.com/technology/2018/aug/29/coding-algorithms-frankenalgos-program-danger

Tene, O., & Polonetsky, J. (2014). A theory of creepy: Technology, privacy, and shifting social norms. Yale Journal of Law and Technology, 16(1), article 2.

Tucker, I. (2013). Evgeny Morozov: We are abandoning all the checks and balances. The Guardian, 9 Mar. 2013. www.theguardian.com/technology/2013/mar/09/evgeny-morozov-technology-solutionism-interview

Wajcman, J. (2019). The digital architecture of time management. Science, Technology, & Human Values, 44(2). http://dx.doi/org/10.1177/0162243918795041