To combat issues of inequity, more and more institutions, administrators, instructors, and publishers in higher education have turned to learning science—an interdisciplinary field of study that seeks to understand how we learn and devises strategies for enhancing our potential – as a potential solution. In examining a human’s cognitive functions, researchers hope to account for the wide variety of learning preferences, academic experience and preparation, and foundational deficits that allow some students to outpace others. Closely linked to this research has been the development of increasingly sophisticated algorithms: complex sets of rules and programs that can adapt to a learner’s past choices and answers, absorb this information, and then predict the best next steps for learning.
In many ways, algorithms—the processes that help to determine the ads we see on Facebook or the search results we find on Google—have provided us with a new approach to learning in the 21st century. On computer-assisted tutorial platforms, Bloom’s 2 Sigma Problem (once thought to be unworkable) inches closer and closer to reality. As intelligent learning systems grow more sophisticated, taking into account the human likelihood for forgetting, they begin to act more and more like one-on-one tutors: testing student learning, targeting problem areas, and adapting to the student’s skill level.
Together, learning science insights and the advent of newer, smarter technologies have made it more possible for students of all different levels and socio-economic backgrounds to have an equitable chance for success in higher education. This algorithmic revolution seems poised to transform education and fulfill the hopeful promises initially discovered Benjamin Bloom’s 2 Sigma Problem research, by addressing the individual needs and concerns of every student through mass adaptive personalization and tutoring.
For more information on how learning science and algorithmic calculation can aid the problem of equity, download this whitepaper.