Buried treasure or Ill-gotten spoils: the ethics of data mining and learning analytics in online instruction
- PDF / 725,866 Bytes
- 4 Pages / 439.37 x 666.142 pts Page_size
- 74 Downloads / 255 Views
Buried treasure or Ill‑gotten spoils: the ethics of data mining and learning analytics in online instruction Marie K. Heath1 Accepted: 1 October 2020 © Association for Educational Communications and Technology 2020
Abstract This paper considers the practical applications of the article, Ethical Oversight of Student Data in Learning Analytics: A Typology Derived from a Cross-continental, Cross-institutional Perspective by Willis et al. (Educ Technol Res Dev 64: 881–901, 2016). Students engaging in online learning leave behind vast quantities of data. In 2020, the rapid shift to online learning during the global pandemic allowed virtual data collection to outpace procedures and policies that promote ethical analysis. The mere availability of data does not confer ethical collection of data. Further, analysis of data under the assumption of learning outcomes does not necessarily ensure justice or learning for students. This article offers possible applications of the heuristic by Willis et al. (Educ Technol Res Dev 64: 881–901, 2016) for ethical learning analytics in order to mitigate harm to students. It extends their work by suggesting educators consider the racialized encoding of data themselves, and argues that every act of surveillance during the pandemic creates norms for future surveillance. Keywords Learning analytics · Online learning · Ethics · Metadata · Pandemic · Data analysis Students engaging in online learning leave behind vast quantities of data, buried as code, hiding in the virtual corridors of learning management systems, school emails, website clicks, and social media likes. University administrators, educators, and instructional designers seem captivated by the promise and possibilities of these data nuggets, growing a new field of learning analytics meant to “optimize student outcomes”—however that may be defined—through analysis of massive virtual data sets (e.g., Sin and Muthu 2015; West 2012; Yadav et al. 2012). The global pandemic of 2020 precipitated a massive migration to
Targeted Manuscript: Willis, J.E., Slade, S. & Prinsloo, P. Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective. Education Tech Research Dev 64, 881–901 (2016). https://doi.org/10.1007/s11423-016-9463-4 * Marie K. Heath [email protected] 1
Loyola University Maryland, Parkton, MD, USA
13
Vol.:(0123456789)
M. K. Heath
online learning that will generate even more virtual data, increasing this mineable trove of student information. Notably, it was neither an academic nor an educator, but rather a for-profit educational technology corporation (Blackboard, the WebCT Company) which coined the term academic analytics (Baepler and Murdoch 2010). Corporations’ business models rely on data scraping in order to predict human behavior to increase profits by selling items and goods back to people (Zuboff 2019). This practice of surveillance capitalism, in which personal and private data is commodified, packaged, and sold relies “on the in
Data Loading...
