ABSTRACT. This article reviews and advances existing literature concerning digital education governance. Building my argument by drawing on data collected from Cowen, Deloitte, Forbes, Global Market Insights, Ironpaper, LinkedIn Talent Solutions, and Statista, I performed analyses and made estimates regarding U.S. artificial intelligence in education market share (by end-use, $ million), how smartphone users benefit from AI: awareness and usage of smartphone applications featuring machine learning (predictive text/route suggestions/voice assistants/voice search/translation apps/voice-to-text/email classification/ automated calendar entries/location-based app suggestions/automated photo classification), industries with the most machine learning talent, ranked by percentage of the total machine learning talent pool (higher education and research/IT and computer software/Internet/ finance and banking/management consulting), sectors targeted by machine learning application developers, and share of participants who believe AI applications will aid global education (by likelihood).

Keywords: big data learning analytics; algorithmic decision-making; digital education

How to cite: Balica, Raluca (2018). “Big Data Learning Analytics and Algorithmic Decision-Making in Digital Education Governance,” Analysis and Metaphysics 17: 128–133.

Received 22 August 2018 • Received in revised form 8 October 2018
Accepted 12 October 2018 • Available online 7 December 2018


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