Because we are better together, EduCred Services wants to provide our blog readers with various perspectives on higher education opportunities. Learning is greater when we can collaborate. This blog post is the first in a two-part series written by Myk Garn, mentor focusing on innovative learning programs. Myk will be presenting at the DEAC Annual Conference on Monday, April 24th in San Antonio, Texas. If you are attending the conference, we look forward to seeing you there. If you are unable to attend, please visit our website for the follow-up blog post next week.
There are many change drivers in higher education. Historically key among these have been the economy, politics, workforce, academic culture and, increasingly, technology. The amount of change being driven by technology can be assessed by measuring the level of ‘digitization’ or ‘digital reinvention’ occurring in academia. Today, that level is low--but rising every day.
The impact of digitization in the music and newspaper industries reveals the pervasive, (sometimes perverse) and challenging, nature of digital reinvention. The development of self-driving automobiles is the most recent example of the nascent becoming the national norm.
Two primary components of digitization that we can measure are the amount of data available and the algorithms that can effectively turn that data into usable, actionable output. To reinvent an industry there must be lots of data--BIG DATA. The data might already exist, such as recorded music in analog form that can be converted to digital (think textbooks and the Open Education Resources (OER) movement) or the processing and analysis of data that is generated from an activity such as the blogosphere or an autonomous vehicle.
In academia, we often use BIG DATA in our disciplines--but when it comes to our core enterprise--instruction--the generation and availability of data is not all that big…yet.
What will change digitization from nascent to necessary in academia will be success. For example, using predictive analytics and ten years of student data across more than 800 data points, allowed Georgia State University to increase graduation rates by 3% from 2012-2014 and add an estimated $3M in additional tuition revenue for 2014. This kind of success gets noticed--and drives similar investments across peer, benchmark, and other progressive institutions. As more institutions join and share in the predictive work, efficacy will increase and the pressure to capture and use data will increase as well. McKinsey & Company find that “bold, tightly integrated digital strategies will be the biggest differentiator between companies that win and companies that don’t.” This will be true for colleges as well. And predictive analytics is just the start.
The convergence of adaptive learning, Artificial Intelligence, machine learning and natural language programming (think IBM’s Watson) is heavily reliant on increased (real-time) data generated by learning events, the standardization of the data and the ability to communicate that data across networks for use in a broad array of functions from immediate feedback to highly personalized portfolios and transcripts.
