Last week, I reviewed Thomas Siebel’s surprisingly good Digital Transformation: Survive and Thrive in an Era of Mass Extinction.

The components that makeup Siebel’s digital transformation are: cloud computing, big data, IoT (internet of things), and AI (artificial intelligence).

In that review, I asked if any books examine the impact of these technologies on the future of higher ed? 

But maybe that is the wrong question.

A better approach might be to ask: what are the academic analogs of each of the components of digital transformation? 

In this thought experiments, here are the analogs that I came up with. It would be interesting to hear what you’d choose:

Cloud Learning: 

Learning that is accessible anywhere, anytime, and on any device. Educators and learning environments are no longer exclusively found on physical campuses, but can be accessed from any place at any distance. Cloud Learning combines online and mobile education. 

Just as cloud computing made previously scarce computing goods such as processing and storage cheap and ubiquitous, cloud learning drives education and credentialing from scarce to abundant. In a cloud learning model, education is life-long rather than constrained, continuous as opposed to episodic.

Wise Data: 

Academic transformation will require colleges and universities to make data-informed decisions. These data will drive decisions about which academic programs to invest in, and which instructional methods are most effective in driving student success. 

This use of data, however, must be approached wisely. Higher ed must not follow the path of K-12 education in prioritizing high-stakes testing over authentic student learning. Learning outcome measures are important, but they should not substitute for prioritizing investments in educators and the defense of faculty autonomy.

Quality at Scale: 

Just as the internet of things (IoT) will revolutionize physical goods, quality at scale has the potential to transform our postsecondary ecosystem. Today, higher education is built around a scarcity model. Scarcity drives status. Scarcity causes educational costs to rise and student debt to swell. Scarcity underlies the adjunctification of the professoriate and the high rates of student attrition. 

What if new methods, technologies, and approaches could dramatically drive down the cost of learning and credentialing, therefore enabling many more students to receive undergraduate and graduate degrees? This is the promise of low-cost online programs. The demand for affordable quality higher education is so vast, that should this be achieved it would benefit learners and schools. The question is, can quality education scale?

Learning Science: 

Artificial intelligence (AI) is not a single technology, but instead the integration of a range of methods and technologies and frameworks. AI is not a technology, but a discipline devoted to the advancement of a range of technologies and methods to address human challenges. Learning science may be the AI of academia. 

Increasingly, the research on how humans learn will drive how universities structure their organizations and design their physical and digital educational environments. Learning science will influence how faculty are trained (and continuously developed), and how students are taught. Academic leaders will become learning scientists, and learning scientists will become academic leaders.

What would you pick as the four elements of academic transformation?