There has been no shortage of enthusiasm for the transformative potential of educational technology.

In 1922, Thomas Edison assured us that “the motion picture…will supplant largely, if not entirely, the use of textbooks.” Writing in 1972 to advise the Carnegie Commission on the role of technology in higher education Eric Ashby forecast that: “The technology … that has had such a profound impact on American society generally in recent decades promises to have powerful influences on higher education as well.” Further, Thomas Friedman in writing about MOOCs in 2013 claimed that “nothing has more potential to lift more people out of poverty—by providing them an affordable education to get a job or improve in the job they have.”

With so many examples of premature excitement, all of us working at the nexus of technology and education must be continually cautious in gauging the future impact of learning technology trends.

Two Trends with the Most Potential

With that caveat in place, I will nevertheless venture that there are some technologies proving an out-sized impact on teaching and learning today, and the continuous improvement they facilitate ensure they will also have a profound ongoing effect.

Adaptivity: The most important of these is the application of adaptive tutors to personalize the teaching of math and the sciences.

Typically accessed through the internet through laptop computers and mobile devices, these adaptive tutors provide students with high frequency, high quality feedback at very low marginal cost, allowing large numbers of students to pursue complex curricula to mastery.

These technologies, powered by ongoing advances in artificial intelligence, continue to grow in capability, providing students and their teachers with unprecedented insight into individual learning patterns.

Guided by computer tutors with deep understanding of their particular strengths and weaknesses, students can more easily focus on steadily mastering skills and concepts in their zone of proximal development, working at their own pace to ensure mastery before advancing. Backed by this computer support, teachers can more easily shift from “sage on the stage” to “guide on the side”, focusing on providing personal attention to struggling students instead of trying to move whole classes through complex material in synch.

Even as they grow in capability, these technologies continue to fall in cost. Though their initial development costs can run into the millions, once deployed at internet-scale the marginal cost of helping each additional student is negligible. The rapid growth in cloud capabilities have streamlined the ability to offer courses built around these technologies, replete with “just-in-time” instructional resources and integrated support services that can serve hundreds of thousands—even millions—of students from around the world.

Why Adaptivity has Massive Potential: The adaptive approach is destined for the long haul because it addresses directly the core challenge facing higher education today: “How can we increase access to high-quality education while dramatically decreasing its cost?”

(Next page: Scalability trends and the future)

Scalability: At the core of this challenge is the need to scale the human attention so central to learning.

Adaptive tutors help students help themselves, allowing students to practice what they’ve learned and receive detailed, personalized feedback on that practice without requiring human intervention. Students get more “time on task” and teachers can focus their attention on meeting students’ more human needs—for encouragement, motivation, and the development of the habits of effective scholarship.

As the world-wide demand for higher education increases, adaptive systems will allow us to increase access to individualized learning processes, allowing us to reach new populations of learners at more manageable cost.

As exponential increases in raw computing power and advances in artificial intelligence continue unabated, we can expect their technologies—which have already crossed the threshold of utility—to become an increasingly integral part of students’ education with each passing year.

As my colleague Lou Pugliese, managing director of the Action Lab at Arizona State University, has stated, “adaptive systems transform digital learning to effectively address challenges including varying student learning ability, diverse student lifestyles, and limitations on faculty time or other resources.” As these technologies are able to capture ever more detailed and comprehensive data on the learning activity of millions of students, the feedback loops these systems facilitate will help us develop an increasingly detailed understanding of how students learn and how we can advise them during their journey.

Scaling this key component of instruction into the millions of students is central to its development. If you want to use technology in education, scale is your friend, not your enemy.

Traditional education models run up against barriers in multiplying resources like faculty and physical class space. The more students you need to reach, the more expensive the conventional enterprise becomes. Technology has the very real potential to reverse that equation.

Why Scalability is Critical for the Future: Scalability is a solution to an unsustainable model in which individual professors are expected to create individual digital experiences at cottage-industry scales.

The majority of today’s digital learning experiences are crafted by individual professors, working in near isolation, one at a time. It’s unreasonable to expect that such experiences can compete with resource-intensive experiences developed by teams of instructors and learning specialists aimed at supporting millions of students.

As these next-generation experiences emerge, faculty will welcome the increased support and students will embrace digital learning experiences whose usability and efficacy rival the best experiences on the internet.

Other Trends

Other trends emerge from adaptivity and scalability, including continuous and dynamic improvement for both the learner and course.

A traditional course only assesses student performance at intervals, whenever tests or papers are graded. An adaptive learning system is making assessments at every interaction, which allows the student to get any needed additional help in real time or to move ahead faster if everything is mastered. The data flowing back can be captured and help teams to make improvements to the course even while the scale reduce costs.  All of which will help secure a continued place for adaptivity and scalability in the trend tool kit of higher education.

About the Author:

Adrian Sannier is the chief academic technology officer for EdPlus at ASU and a professor of Practice in the School of Computing, Informatics, and Decision Engineering at Arizona State University. He is part of the ASU team pursuing an ambitious program of general education reform that Inside Higher Education called “ground zero for data-driven teaching in higher education,” combining Big Data, social networking, and evidence- based instruction to drive better student outcomes at scale.

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