Published on 2014/03/14

Technology Set to Redefine Higher Education

Technology Set to Redefine Higher Education
As students begin behaving like savvy shoppers, institutions need to develop innovative academic models to set themselves apart from the crowd.
Co-written with Aric Krause | Vice Provost, University of Maryland University College

Over the next 10 years, higher education is poised to transform in ways and at a pace unlike anything we have seen in the past century. Several factors will contribute to this revolution in the form, function and financial model of higher education.

First, the demographics of our students are changing. Over the next five years, the number of students will decrease overall, signaling an end to the recent spurt in enrollment at most colleges and universities. We won’t see an uptick in enrollments until closer to 2020.

Second, students cannot afford the high cost of college tuition, nor are they willing to shoulder the burden of a lifetime of debt in order to complete college. Increasingly, students will view themselves as “smart shoppers,” searching for the best “value proposition” in higher education. Ultimately, they choose programs that most strongly catapult them toward their career aspirations.

Third, connected technologies, learning research and science and the proliferation of the internet as our go-to method of communicating and learning are converging to create change that is accelerating while also creating new opportunities in education beyond our traditional understanding. This leads to a state of not being able to easily predict the future.

What are these new innovations we’re likely to see rolling out in the next five to 10 years? Three are already on the horizon and being developed among the most forward-leaning institutions. They are competency-based education, predictive analytics and adaptive learning.

Competency-based education (CBE) will transform higher education in ways that are not yet understood. Rather than “cheapen” education, as some fear, the movement to clearly and accurately design curricula with competencies at the core, and new teaching models that value mastery over failure, will result in higher education that works better for all students. As the many versions of CBE are developed and implemented, we will see that CBE is much more than allowing students to take tests to earn credit; it is truly a revolution in how we design learning and how we assist students to learn successfully.

One of the critical technologies that will support student success in the CBE world will be adaptive learning software. Simply put, adaptive software “learns” what students do and do not know, and provides pathways to help them learn better. In competency-based models, faculty — finally — have the information they need about student progress so they can focus their efforts on activities for students who most need their guidance.

Predictive analytics, already embedded in many institutions’ approaches, will become ubiquitous, and will become the method that allows colleges and universities to create successful student experiences by determining optimal instructional methods, educational technologies, curriculum design, institutional policies and suggested student behaviors. By using what we learn about the path to student success, we can shape our institutions to support students to succeed.

Most readers of The EvoLLLution will be familiar with CBE, adaptive learning software and predictive analytics. And, yet, we believe these are simply evidence that the biggest innovation in higher education is that we’re finally applying what we have known for many years about student learning.

We know listening to a lecture is the least effective method of learning; the more active and engaging the experience, the more students will learn. We know students do not learn or remember in discrete, three-credit packages of knowledge; instead, students come to any new learning experience with some specific strengths and gaps. We know students do not all learn at the same speed or in the same ways; they need variable pathways, opportunities and time periods to learn.

Yet, what have we created over the last 150 years? An assembly model of one-size-fits-all higher education that values the lecture over other forms of active learning.

It isn’t the case that we don’t know the best ways in which students learn. But in order to keep tuition costs manageable, the large lecture classes have often prevailed.

Now, however, technology and learning science can help us create ways for each student to have his or her own customized pathway to academic achievement with active, engaged learning at the forefront. Interventions can be individualized, and pathways can be customized specifically for different types of learners. And faculty can be re-energized by creating effective learning experiences as opposed to simply lecturing to large classes.

Higher education is changing in form, but its function will be better than ever.

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Readers Comments

James Branden 2014/03/14 at 10:59 am

Predictive analytics creates a “personal touch” currently missing in a lot of large and/or online courses. Its greater adoption could allow institutions to “scale up” and expand their online programming.

Ian Richardson 2014/03/14 at 2:32 pm

I would be hesitant to look at predictive analytics as a way for institutions to scale up and, essentially, minimize the role of the instructor. While there’s value in using this type of technology to better identify struggling students and develop interventions for them, I think there’s still a need for a “human touch” in this process. Historically, instructors have been the ones to flag students who seem to be struggling and support remedial students. To usurp them from that position with new technology would be a great loss to students. All technology is fallible — just as all humans are. That’s why we need both technology and a human hand to help students who are struggling.

anon 2014/03/17 at 2:58 pm

I agree with Ian. I think what the authors are describing here works only if your end goal isn’t to use the technology to scale up. The purpose of adopting something like predictive analytics is that it allows you to create hybrid learning models, where the technology can help students on their own time, and classroom time can be used for engagement and activities to support learning. Currently, I feel that a lot of classroom time is spent going over issues with course content. Having those interventions take place outside of class would free up instructors to be more experimental and hands-on in the classroom.

Marie Cini 2014/03/22 at 3:18 pm

As first author of the article, I can tell you that my co-author and I agree that predictive analytics should not be considered simply a method to “scale up” online education. In fact, we work at an institution that is already at scale with tens of thousands of students online. We have historically asked faculty and advisors to reach out to “struggling” students.

Predictive learner analytics is a tool to help faculty understand which students are likely to struggle and which are likely to succeed. These powerful models can help predict this BEFORE the student is actually struggling. This allows faculty–who are key to our academic model–to better focus their time where it is best suited. We would never advocate for technology to replace faculty. Instead, we believe strongly that technology should support faculty to do what they do best–support, mentor, illuminate, motivate, and instruct.

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