How does big data impact education?

The influence and growth of big data is difficult to ignore but so too is the lack of healthy criticism against it 1. On the surface, many popular articles make technological determinism appear palatable as a proposition inferring that big data is the panacea for all things (old) and in ‘need of disruption’. This chorus of heightened expectations and unlimited possibilities reflects the beginnings of a hype-cycle narrative. Common themes in many online readings reflect an idealistic view of data, amplify the popularizing of data science and reinforce an authoritarian stance as a replacement for other types of knowledge.

In reflecting broadly about my personal experience of big data it is, thus far, something that happens to me though it’s often pitched as something for me. In the advertising domain the pitch is that personalizing my shopping experience will improve my life, or at least the efficiency of my transactional life though I am never given a choice about it nor am I convinced that I’m getting more than I’m giving 2.

A parallel pitch is seen in personalizing education. Knowing what we do about the economic success of the data economy I’m inclined to believe that the convenience benefits afforded to me are, once again, outweighed by the economic benefits afforded to the companies that profit off my data. This consumer dynamic is inherently problematic and arguments against it can be tabled in discussions about antitrust and consumer protection law, human rights and privacy protection 3. Placing this dynamic in an educational context elevates how disconcerting this is especially when looked at through the lens of education being primarily a public service aimed at minors.

In the ‘Introspection and Prospects of Learning Analytics’ a discussion equates the function of Learning Analytics with the function of mass collection for national security purposes revealing an attitude of entitlement to other people’s data. If it was unintentional to invoke the notion that students are to be treated like the enemy and need to be monitored as though they are a threat, it is nevertheless a terrible analogy to garner widespread support. In a learning environment where vulnerability should be encouraged and a safe environment cultivated so that people can feel free to express their views, the last thing people need is to be survielled. Yet, look no further than Athabasca’s partnership with Amazon to see how disparate the opinions are on that 4.

The Wired’s article, ‘The End Of Theory: The Data Deluge Makes The Scientific Method Obsolete by Chris Anderson arguably falls within a click-bait-headline category and misses an opportunity to frame a relevant issue. Framing data science as a replacement for theory driven research is hyperbolic, at best. Data-driven research methods have existed for decades which makes confounding the inference that data-driven research coincides with the coining of the term ‘big data’. Maass et al. looks at opportunities and challenges for information systems research when these two approaches, data-driven and theory-driven, are analyzed together 5. Despite the perception that data science is finally here to bring us new ways of knowing and at the same time diminishing the value of a theory-driven approach the relevant issue at this intersection of two paradigms is how they can benefit each other.


  1. B. Beaton, “How to respond to data science: early data criticism by Lionel Trilling,” Inf. Cult., no. 3, p. 352, 2016
  2. U. McMahon-Beattie, “Trust, fairness and justice in revenue management: Creating value for the consumer,” J. Revenue Pricing Manag., vol. 10, no. 1, pp. 44–46, Jan. 2011.
  3. A. A. Miller, “What Do We Worry about When We Worry about Price Discrimination – The Law and Ethics of Using Personal Information for Pricing [article],” J. Technol. Law Policy, no. Issue 1, p. 41, 2014.
  4. D. Van Puyvelde, S. Coulthart, and M. Shahriar Hossain, “Beyond the buzzword: big data and national security decision-making,” Int. Aff., vol. 93, no. 6, pp. 1397–1416, Nov. 2017.
  5. W. Maass, J. Parsons, S. Purao, V. C. Storey, and C. Woo, “Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research,” J. Assoc. Inf. Syst., vol. 19, no. 12, pp. 1253–1273, Dec. 2018.


A few of my favourite things: Agile software development with the potential for significant social impact combined with responsible and appropriate use of data, machine learning algorithms and systems that support research and evidence based decision making.

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