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The Fourth Industrial Revolution: Promises and Perils of the Educational Intelligent Economy by Tavis D. Jules, Florin Salajan, and Richard Arnold

This NORRAG Highlights is contributed Tavis D. Jules (Associate Professor Cultural and Educational Policy Studies at Loyola University, Chicago), Florin Salajan (Associate Professor in the School of Education at North Dakota State University, Fargo, USA) and Richard Arnold (Doctoral student at Loyola University, Chicago). The authors explore the potential benefits of educational intelligence data – any form of data which can be recorded and disseminated in the course of improving educational systems. However, they also comment on the dangers this poses could pose in an increasingly modernized world. 

Consider this: For over 70 years, the vinyl record was the dominant format for storing, and listening to, music and other audio programming. For much of that time, records held less than an hour’s worth of sound. Its successor, the cassette tape, enjoyed a period of dominance in the market for less than 20 years, before Compact Discs (CDs) came to be the preferred format. CDs, in turn, quickly gave way to MP3 files sold and shared via the internet; this was the dominant format of music for little more than a decade before streaming services such as Spotify, Pandora, and others offered began offering access to literally trillions of hours’ worth of music and audio. There is so much audio available to us that we now regularly use Artificial Intelligence (AI) algorithms to help us choose what we want, as we could never process even a fraction of it in our lifetimes. In what follows, this conceptual paper explores the merits of the growth of the educational intelligent economy in an era defined by the rise of the Fourth Industrial Revolution – which blends the physical with the cyber-physical. Here, the educational intelligent economy is viewed as “the exponential production of digital data to measure, analyze and predict educational performance in comparative perspective” (Salajan & Jules, 2019, p. 1).

With the commencement of the so-called movement from government to governance, we are living in a time of unprecedented rapid advancements in digital data development and processing. Big Data, which includes cloud computing, high automation, and behavior analytics, creates a massive ecosystem of information. Not only that, but intuitive algorithms that can make predictions and create solutions with that data have advanced at an unprecedented rate. If an algorithm can be created to curate your music playlists and adjust based on your mood, what potential is there for educational intelligence algorithms?

Educational Intelligence is widely understood to encompass any data – individual or systemic – that can be recorded and disseminated in the course of improving educational systems. It is more and more equated with military or economic intelligence; vital assets with strategic value that can propel innovation in the field. In this sense, educational intelligence is vital to the understanding of a region’s educational system, and for the comparison and contrast of that educational system with others. Automatic algorithms can harvest data procedures in the areas of reading recovery, for example, analyzing massive amounts of data on students’ individual abilities and designating entire programs for improving reading abilities at rapid rates. And, like economic intelligence used to grow and integrate industries, educational intelligence can be traded. As such, these activities in education will need to be regulated across the board.

The marketization of data is not new in education. The International Evaluation of Educational Achievement (IEA) began assessing learning and commodifying data in the early 1960s, and their wide ranging FIMS/SIMS studies on math achievement helped connect departments of educational analytics across numerous countries. OECD launched the International Adult Literacy Survey (IALS) in the 1990s to establish correlations between literacy and economic status indicators, and UNESCO launched the World Education Report to collect, sort, monitor, and analyze data from its fragmented education initiatives around the world. While these initiatives frequently engaged with 3rd party private companies to collect data, they were still governed as public policy by intragovernmental, or supranational, entities. While there are still increasing protocols on data collection and dissemination (i.e. the General Data Protection Regulation (GDPR) of the EU), governmental attempts to regulate the data of students fall well short of universal and robust guidelines. This is all the more concerning in an age of blindingly fast growth of the data economy.

If, in the age of Big Data, there is an economy of data, then there must simultaneously exist an intelligent educational economy. In Silicon Valley, this economy revolves around a concept frequently recurring in recent years: “blitzscaling,” or the prioritization of speed over efficiency to spur and sustain massive growth over short periods of time. In a field undergoing rapid (usually taxpayer-funded) changes in a short period of time, one needs look no further for examples than the rapid rise of Edtech companies, such as Coursera (est. 2012), Chegg (est. 2005) or TAL Education (est. 2013). With a deterritorialization of traditional education markets, government actors are necessarily moving in to regulate the impacts of Big Data on education.

The rapid rise in private Edtech companies places massive amounts of personal data in the hands of private corporations; while supranational entities have taken steps to protect data, the fear is that it will be too little, too late. For example, in recent years, the EU has launched multiple initiatives to steer digital development in its member states, such as when the  General Data Protection Regulation (GDPR) moved to limit the transfer of personal data both internally and externally. In a high-profile case, the EU fined Google €50 million last year for its violation of the regulation. Yet, this is only a small inroad into protecting the rights of students, as the GDPR has no specific apparatus for protecting student information within member states, lest the EU be seen as interfering with the education systems of its members. Other supranational organizations are watching and waiting as developments occur, yet the most important question remains: what does this mean for educational intelligence?

In the arena of educational policy decision-making, we can expect a high degree of algothrimization. For obvious reasons, companies that could harvest this massive quantity of data using machine learning to create predictive analyses that could maximize student performance would have services in very high demand. And, if successful, such algorithms entice educators to keep coming back and adapt other aspects over large time spans; for example, teachers could subject their lesson plans to algorithms that maximize their time in a period, scour teaching strategy databases, or even create ideal seating charts based on behaviors. In this ecosystem, educational companies become more like manufacturing companies, working to develop solutions in a servitization system: the product takes a backseat to the product-service provider relationship. Such a relationship requires supranational governance of the trading of data between corporations and people.

And governance (here understood to be government entities exerting influence over the data trade) we have, in many forms, as Big Data looms over the educational intelligent economy. International organizations, such as UNESCO and OECD collate data for measurement and assessment, while national and local systems integrate themselves more and more within this system. In fact, there is so much integration of different educational systems within the same data spheres that we can argue there is a reterritorialization of educational governance, as data is both omnipresent and delineated for different educational purposes. Thus, new education networks will arise based on the data that is shared within said network. Already, data connects people with, for example, similar tastes in music, creating online communities of people who connected through a love of, say, 80’s pop. Now, similar governance structures can arise within the educational intelligent economy: data could connect educational communities between, say, Chicago and Johannesburg as assessment data is compared and contrasted. Fragmented, yes, but with endless possibilities.

In this new era, data has become a commodity – the so-called ‘new oil’ –  traded in networks and disseminated, analyzed, and used as bases for predictive analysis. As with any commodity, this causes a rise in third-party actors looking to profit from, and created services for, consumers of that commodity. This, in turn, leads to new actors exerting governance in new arenas. But more than anything, data provides us with an opportunity. Comparative International Education is predicated on the sharing of data and the search for solutions. And just as the rise of the CD spelled near doom for the vinyl record, yet ultimately provided consumers with previously unthought of options, so too will the rise of the educational intelligent economy imperil many outdated practices in education, yet provide educators and students with opportunities untold. Yet, just as consumers of music also give up personal data to music services (look no further than the recent Spotify data hack), educational consumers run the risk of terrible consequences with their data. Like the music industry, it all depends on how well we adapt to the deluge of changes upon us.

Reference

Salajan F. D. & Jules, T. D. (2019). Introduction: The Educational Intelligent Economy, Educational Intelligence, and Big Data. In T. D. Jules and F. D. Salajan (eds), The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education (pp. 1-32). Bingley: Emerald Publishing

About the authors: Tavis D. Jules is an Associate Professor Cultural and Educational Policy Studies at Loyola University Chicago, USA, specifically focusing on Comparative and International Education and International Higher Education. His upcoming book (with Florin D. Salajan) is The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education (Emerald, 2020). Florin D. Salajan is an Associate Professor in the School of Education at North Dakota State University, Fargo, USA. His research interests include Comparative and International Education, European educational policies and digital education. He teaches undergraduate and graduate courses in comparative education, teacher education and technology-enhanced learning. Richard Arnold is a doctoral student at Loyola University Chicago. He is currently studying and conducting research in the area of cultural and educational policy studies. He has taught in numerous school districts, and more recently conducted product field research for Google for Education.

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