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.
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 process of selecting material, CSU-Fresno has been piloting an analytics solution from Intellus Learning, which has indexed more than 45 million online learning resources and can make recommendations of matching OER content. “If I am teaching an English course and I have a standard textbook, I can type the ISBN number into Intellus,” explained Berrett. “Broken down by chapter, it will say here are all the OER resources that are available that match up with that content.” The faculty member can then upload the resources directly into the course learning management system.
Intellus says it can also index the millions of learning objects in use at an institution and provide real-time analytics on student usage.
A similar homegrown effort at Penn State University has branched out into new directions, said Kyle Bowen, director of education technology services. PSU’s BBookX takes a human-assisted computing approach to enable creation of open source textbooks. The technology uses algorithms to explore OER repositories and return relevant resources that can be combined, remixed and re-used to support learning goals. As instructors and students add materials to a book, BBookX learns and further refines the recommended material.
Bowen explained that the work was inspired to some degree by more nefarious uses of machine learning. Looking at examples of researchers using algorithms to generate fake research papers begged the question: If you can do something like that to create fake research papers, could you use it to create real ones or real content? “What better problem to try to solve than looking at open content?” he said. “How could we simplify or expedite the process of generating a textbook or a textbook replacement?”
In the process of training machines to search for appropriate content, the PSU researchers discovered that algorithms often surface content the faculty member may not have known about. Even if you are an expert in a topic area, there are still elements of the field you may not be as familiar with, and the algorithm is not biased by knowledge you already have.