What are the risks of Virtual Reality data? Learning Analytics,Algorithmic Bias and a Fantasy of Perfect Data

Abstract

Virtual reality (VR) is an emerging technology with the potential to extract significantly more data about learners and the learning process. In this article, we present an analysis of how VR education technology companies frame, use and analyse this data. We found both an expansion and acceleration of what data are being collected about learners and how these data are being mobilised in potentially discriminatory and problematic ways. Beyond providing evidence for how VR represents an intensification of the datafication of education, we discuss three interrelated critical issues that are specific to VR: the fantasy that VR data is ‘perfect’, the datafication of soft-skills training, and the commercialisation and commodification of VR data. In the context of the issues identified, we caution the unregulated and uncritical application of learning analytics to the data that are collected from VR training.

Keywords

Algorithmic bias automated decision-making data ethics datafication learning analytics virtual reality

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References

Amoore

(2020) Cloud Ethics. Durham, NC: Duke University Press.

Andrejevic

(2019) Automated Media. New York: Routledge.

Andrejevic

Selwyn

(2020) Facial recognition technology in schools: critical questions and concerns. Learning, Media and Technology 45(2): 115–128.

Angwin

Larson

Mattu

, et al. (2016) Machine bias. Propublica. Available at: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Beer

(2016) Metric Power. London: Palgrave Macmillan.

Beer

(2018) The Data Gaze: Capitalism, Power and Perception. Thousand Oaks, CA: SAGE.

Bergman

(2020) Talespin worker training platform raises $15 million. Dot.la, 3 March. Available at: https://dot.la/talespin-virtual-reality-training-2645361687.html

Bollmer

(2017) Empathy machines. Media International Australia 165(1): 63–76.

Bowker

Star

(2000) Sorting Things Out: Classification and Its Consequences. Cambridge, MA: MIT Press.

10.

Brock

(2018) Critical technocultural discourse analysis. New Media & Society 20(3): 1012–1030.

11.

Brown

(2020) Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education 25(4): 384–400.

12.

Buckingham

Ferguson

(2012) Social learning analytics. Educational Technology and Society 15(3): 3–26.

13.

Buckingham Shum

Luckin

(2019) Learning analytics and AI: politics, pedagogy and practices. British Journal of Educational Technology 50(6): 2785–2793.

14.

Carah

Angus

(2018) Algorithmic brand culture: participatory labour, machine learning and branding on social media. Media, Culture & Society 40(2): 178–194.

15.

Carter

Egliston

(2020) Facebook’s virtual reality push is about data, not gaming. The Conversation. Available at: https://theconversation.com/facebooks-virtual-reality-push-is-about-data-not-gaming-145730

16.

Carter

Webber

Rawson

, et al. (2020) Virtual reality in the zoo: a qualitative evaluation of a stereoscopic virtual reality video encounter with little penguins Eudyptula minor. Journal of Zoo and Aquarium Research 8(4): 239–245.

17.

Corrigan

(2018) Making implicit methods explicit: trade press analysis in the political economy of communication. International Journal of Communication 12: 2751–2772.

18.

Corrin

Kennedy

French

, et al. (2019) The ethics of learning analytics in Australian higher education: a discussion paper. Available at: https://melbourne-cshe.unimelb.edu.au/__data/assets/pdf_file/0004/3035047/LA_Ethics_Discussion_Paper.pdf

19.

Crawford

Joler

(2018) Anatomy of an AI System: the Amazon Echo as an anatomical map of human labor, data and planetary resources. AI Now Institute and Share Lab, 7 September. Available at: https://anatomyof.ai

20.

Crawford

Paglen

(2019) Excavating AI: the politics of training sets for machine learning. 19 September. Available at: https://excavating.ai

21.

Dalton

(2015) For fun and profit: the limits and possibilities of Google-Maps-based geoweb applications. Environment and Planning A: Economy and Space 47(5): 1029–1046.

22.

Daniela

(2020) New perspectives on virtual and augmented reality. In: Daniela

(ed.) New Perspectives on Virtual and Augmented Reality. Available at: https://www.taylorfrancis.com/books/edit/10.4324/9781003001874/new-perspectives-virtual-augmented-reality-linda-daniela

23.

Davis

(1995) Enforcing Normalcy: Disability, Deafness and the Body. Verso: London.

24.

Egliston

Carter

(2020) Oculus imaginaries: the promises and perils of Facebook’s virtual reality. New Media & Society. Epub ahead of print 24 September. DOI: 10.1177/1461444820960411.

25.

Evans

(2019) The Re-Emergence of Virtual Reality. Routledge: London.

26.

Gent

(2020) Is education the new currency? BBC Worklife. Available at: https://www.bbc.com/worklife/article/20200210-is-education-the-new-currency

27.

Gilliard

Hugh

(2016) Digital redlining, access, and privacy. Common Sense Education, 24 May. Available at: https://www.commonsense.org/education/articles/digital-redlining-access-and-privacy

28.

Golding

(2019) Far from paradise: the body, the apparatus and the image of contemporary virtual reality. Convergence 25(2): 340–353.

29.

Greller

Drachsler

(2012) Translating learning into numbers: a generic framework for learning analytics. Educational Technology and Society 15(3): 42–58.

30.

Haiven

(2013) Walmart, finance, and the cultural politics of securitization. Cultural Politics 9(2): 239–262.

31.

Harley

(2019) Palmer Luckey and the rise of contemporary virtual reality. Convergence 26(5–6): 1144–1158.

32.

Harwell

(2018) The Accent Gap. The Washington Post, 19 July. Available at: https://www.washingtonpost.com/graphics/2018/business/alexa-does-not-understand-your-accent/

33.

Hern

(2020) Shirking from home? Staff feel the heat as bosses ramp up remote surveillance. The Guardian, 27 September. Available at: https://www.theguardian.com/world/2020/sep/27/shirking-from-home-staff-feel-the-heat-as-bosses-ramp-up-remote-surveillance

34.

Hildebrandt

(2006) Privacy and identity. In: Claes

Duff

Gutwirth

(eds) Privacy and the Criminal Law. Oxford, UK: Intersentia, pp. 61–104.

35.

Hoffman

(2018) Data violence and how bad engineering choices can damage society. Available at: https://medium.com/s/story/data-violence-and-how-bad-engineering-choices-can-damage-society-39e44150e1d4

36.

Holler

(2019) Customers are changing: jobs are changing: at Walmart, the future of work is bright. Walmart Newsroom, 30 October. Available at: https://corporate.walmart.com/newsroom/2019/10/30/customers-are-changing-jobs-are-changing-at-walmart-the-future-of-work-is-bright

37.

Hong

S-H

(2020) Technologies of Speculation: The Limits of Knowledge in a Data-driven Society. New York: New York University Press.

38.

Jasanoff

(2017) Virtual, visible, and actionable: data assemblages and the sightlines of justice. Big Data & Society 4(2): 1–15.

39.

Katwala

(2020) An algorithm determined UK students’ grades: chaos ensued. Wired, 15 August. Available at: https://www.wired.com/story/an-algorithm-determined-uk-students-grades-chaos-ensued/

40.

Kelly

(2019) AR will spark the next big tech platform – call it mirrorworld. WIRED, December. Retrieved from https://www.wired.com/story/mirrorworld-ar-next-big-tech-platform/

41.

Keyes

(2019) Counting the countless why data science is a profound threat for queer people. Real Life Mag, 2 December. Available at: https://reallifemag.com/counting-the-countless/

42.

Kim

(2017) Data-driven discrimination at work. William & Mary Law Review 48: 857–936.

43.

Kitchin

(2014a) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Thousand Oaks, CA: SAGE.

44.

Kitchin

(2014b) Big Data, new epistemologies and paradigm shifts. Big Data & Society: 1(1): 1–12.

45.

Koenecke

Nam

Lake

, et al. (2020) Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences of the United States of America 117(14): 7684–7689.

46.

Komljenovic

(2020) The future of value in digitalised higher education: why data privacy should not be our biggest concern. Higher Education. Epub ahead of print 19 November. DOI: 10.1007/s10734-020-00639-7.

47.

Komljenovic

(2021) The rise of education rentiers: digital platforms, digital data and rents. Learning, Media and Technology. Epub ahead of print 22 February. DOI: 10.1080/17439884.2021.1891422.

48.

Kudina

Verbeek

(2019) Ethics from within: Google glass, the Collingridge dilemma, and the mediated value of privacy. Science Technology and Human Values 44(2): 291–314.

49.

LaRocco

(2020) Developing the ‘best practices’ of virtual reality design: industry standards at the frontier of emerging media. Journal of Visual Culture 19(1): 96–111.

50.

Liao

(2018) Mobile versus headworn augmented reality: how visions of the future shape, contest, and stabilize an emerging technology. New Media & Society 20(2): 796–814.

51.

Liu

Dede

Huang

, et al. (2017) Virtual, Augmented, and Mixed Realities in Education. Cham: Springer.

52.

Mehrabi

Morstatter

Saxena

, et al. (2019) A survey on bias and fairness in machine learning. Arxiv Preprint Arxiv:1908.09635. Available at: https://arxiv.org/abs/1908.09635

53.

Miller

Herrera

Jun

, et al. (2020) Personal identifiability of user tracking data during observation of 360-degree VR video. Scientific Reports 10: 17404.

54.

Noble

(2018) Algorithms of Oppresion. New York: New York University Press.

55.

O’Kane

(2018) Tesla vs. Waymo: who’s winning the race for self-driving cars. The Verge, 18 April. Available at: https://www.theverge.com/transportation/2018/4/19/17204044/tesla-waymo-self-driving-car-data-simulation

56.

Pesce

(2020) Augmented Reality. London: Polity Press.

57.

Pfeuffer

Geiger

Prange

, et al. (2019) Behavioural biometrics in VR. In: Conference on human factors in computing systems – Proceedings 12. Available at: https://dl.acm.org/doi/10.1145/3290605.3300340

58.

Rhagavan

Barocas

Kleinberg

, et al. (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices. In: Proceedings of the 2020 conference on fairness, accountability, and transparency (FAT*’20), Barcelona, January, pp. 469–481. New York: ACM Press.

59.

Rosenblat

(2018) Uberland. Berkeley, CA: UC Press.

60.

Sadowski

(2019) When data is capital: datafication, accumulation, and extraction. Big Data & Society 6(1): 882054.

61.

Sadowski

(2020) Too Smart. Cambridge, MA: MIT Press.

62.

Saker

Frith

(2019) From hybrid space to dislocated space: mobile virtual reality and a third stage of mobile media theory. New Media & Society 21(1): 214–228.

63.

Saker

Frith

(2020) Coextensive space: virtual reality and the developing relationship between the body, the digital and physical space. Media, Culture & Society 42: 1427–1442.

64.

Sanchez- Mondero

Dencik

Edwards

(2019) What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems. Available at: https://arxiv.org/abs/1910.06144

65.

Selwyn

(2020) Re-imagining ‘Learning Analytics’ . . . a case for starting again? The Internet and Higher Education 46: 100475.

66.

Slade

Prinsloo

(2013) Learning analytics: ethical issues and dilemmas. American Behavioral Scientist 57(10): 1510–1529.

67.

Smyth

(2017) The Toxic University Zombie Leadership, Academic Rock Stars and Neoliberal Ideology. London: Palgrave Macmillan.

68.

Society for Learning Analytics Research (SoLAR) (nd) What is learning analytics? Available at: https://www.solaresearch.org/about/what-is-learning-analytics/

69.

Srnicek

(2016) Platform Capitalism. New York: Wiley.

70.

Statt

(2020) Facebook reportedly ignored its own research showing algorithms divided users. The Verge, 26 May. Available at: https://www.theverge.com/2020/5/26/21270659/facebook-division-news-feed-algorithms

71.

Stilgoe

Owen

Macnaghten

(2013) Developing a framework for responsible innovation. Research Policy 42(9): 1568–1580.

72.

Swauger

(2020) Our bodies encoded: algorithmic test proctoring in higher education. Hybrid Pedagogy. Available at: https://hybridpedagogy.org/our-bodies-encoded-algorithmic-test-proctoring-in-higher-education/

73.

Tuchscherer

(2019) Walmart uses virtual reality to test new store managers. USA TODAY, 8 July. Available at: https://www.usatoday.com/story/tech/2019/07/08/walmart-uses-virtual-reality-hire-new-managers/1635311001/

74.

Wallis

Ross

(2020) Fourth VR: indigenous virtual reality practice. Convergence 27(2): 313–329.

75.

Warzel

(2020) Is QAnon the most dangerous conspiracy theory of the 21st century? The New York Times, 4 August. Available at: https://www.nytimes.com/2020/08/04/opinion/qanon-conspiracy-theory-arg.html

76.

Wasko

(2004) The political economy of communications. In: Downing

McQuail

Schlesinger

, et al. (eds) The SAGE Handbook of Media Studies. Thousand Oaks, CA: SAGE, pp. 309–330.

77.

Wilken

Burgess

Albury

(2019) Dating apps and data markets: a political economy of communication approach. Computational Culture a Journal of Software Studies 7(7): 1–26.

78.

Wilken

Sinclair

(2009) ‘Waiting for the kiss of life’ mobile media and advertising. Convergence 15(4): 427–445.

79.

Williamson

(2020) Making markets through digital platforms: Pearson, edu-business, and the (e)valuation of higher education. Critical Studies in Education 62: 50–66.

80.

Williamson

Bayne

Shay

(2020) The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education 25(4): 351–365.

81.

XR Safety Initiative (2020) The XRSI privacy framework (version 1.0). Available at: https://xrsi.org/publication/the-xrsi-privacy-framework

82.

Zuboff

(2019) The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London: Profile Books.