Page 13 - Campus Technology, October 2017

ANALYTICS
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 ... [with] a standard textbook, I can type the ISBN number into Intellus,” explained Berrett. “Broken down by chapter, it will say here are the OER resources 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 algo- rithms used 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 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.
Bowen said the process of fine-tuning the algorithm works less like a Google search and more like a Netflix recommen- dation. “With a Google search, you provide a term, and if you don’t like the results you change your terms. Here you are changing how the machine is thinking about those terms,” he explained. “You are telling it ‘more like this, less like that,’ and you keep iterating. It begins to focus on what you are looking for and what you mean by that term. It goes by the meaning the faculty member is trying to get to.”4
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