Page 12 - Campus Technology, October 2017

ANALYTICS david raths How Machine Learning
Is Easing OER Pain Points
Algorithms can help faculty discover and select open educational resources for a course, map the concepts covered in a particular text, generate assessment questions and more.
THE BASIC DEFINITION of machine learning is that it allows a computer to learn and improve from experience without being explicitly programmed. One obvious example: the way a Netflix algorithm learns our TV-watching habits to make suggestions of other movies we might like. We come into contact with dozens of such machine-learning algorithms every day.
Algorithms are even starting to make an impact on university campuses, taking on time-consuming tasks to ease faculty and administrator workloads. For example, RiteClass’s predictive admissions platform uses machine learning to produce a “Prospective Student Fit Score” by ingesting data about current students and alumni. The Fit Score will determine how similar (or different) a prospective student is to current students and alumni, according to the company, helping institutions make data-driven admissions decisions.
And in support of faculty members, several efforts are underway to use machine learning to analyze the contents of open educational resources (OER) for their fit in a particular course.
Algorithm-Assisted Content
California State University, Fresno has been urging its faculty members to seek out appropriate no- or low-cost course materials. The problem: Replacing costlier course material with appropriate OER content is time-consuming, said Bryan Berrett, director of the campus’s Center for Faculty Excellence. To ease the
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CAMPUS TECHNOLOGY | October 2017
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