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. Algorithmic learning analytics, such as those supported by IMS Caliper standards, will make visible a learning process we cannot see and make possible testing and interventions we cannot now implement at scale. Such data standards are a ‘force multiplier’ powering Metcalf’s Law of Network Effects and rapidly move the emergent to the eminent. For example, digitization of student information systems, across the enterprise, provides the mechanism to accurately analyze and successfully increase retention at Georgia State--and now many other institutions across the country.
Demand for digitization will also come from students. In a McGraw-Hill survey 87% of students expected learning analytics to improve their academic performance. Meeting the digital expectations of learners who are constantly in touch and continually processing input and feedback--in real-time--will only increase in necessity and criticality.
What is reinvention today will become required tomorrow. Accreditation models, just recently having shifted from measuring inputs to assessing outputs, are also feeling pressure to include more data-informed, and driven, evaluative models--models that effectively, efficiently, and transparently increase student success. As CHEA noted recently, the pillars of self-reporting and peer-review are, “no longer viewed as providing adequate information and…are now considered less reliable.” More and more, an institution’s ability to identify, collect, manage and leverage data effectively will be a representation of its competence--and competitiveness--in a rapidly transforming educational marketplace.
Reinventing the current, analog, classroom teaching models as digitized learning ecosystems is one of the greatest of vexations, opportunities, and absolutely necessary challenges of our campuses and careers. It is no easy lift, it will not happen overnight, and much has yet to be invented. While there are areas of deep digitization in many disciplines, and academic administration--institutions that begin the digital reinvention of instruction the soonest and most robustly will gain the leaders dividend of expertise and success.
Early elements of reinvention include incorporating the precepts and components of competency-based education to make instructional aims and processes more explicit by redesigning instructional models so they generate more, and more finely grained data about student activities and interactions, using adaptive learning platforms to frame and structure learning models that react to student actions in real-time, using learning-level data to feed faculty and student dashboards with actionable information, and re-envisioning their academic model from the current teaching enterprise to one that is a learning ecosystem.
These paths and processes of reinvention are not unique. Digitization is proving very successful in the medical field. Coalescing under the term ‘precision medicine,’ medical care is increasingly being guided by data to customize medical decisions, practices, and products tailored to the individual patient. Modeled on these precepts and practices, a learning/instructional modus-operandi described as ‘PRECISION ACADEMICS’ is gaining in mindshare and traction. Precision academics, as a goal and process, is providing a useful ‘bucket’ to organize the emergent paradigms of learning and connect them with the eminent practices of teaching. I shall describe this new field in greater detail at the Distance Education Accrediting Commission's Annual Conference in San Antonio, Texas--and in a forthcoming blog.