Page 14 - Campus Technology, October 2017

ANALYTICS
Next Steps
Although PSU is continuing its work on the OER textbook project, Bowen said, “What we uncovered was that using this machine learning approach to generate textbooks was potentially one of the least interesting things we could do with it.” The institution’s data scientists have moved into three other areas with the intent of taking on even more complex issues:
1) Prerequisite knowledge. In terms of sequencing how material is presented, machine learning might help instructors understand the prerequisite knowledge a person would need in order to understand a particular body of text. “We want to make sure that as you are coming into a class, the prerequisite
knowledge has already been introduced,” Bowen said. “You could do that yourself by charting out the concepts to see how they relate across the material. But in this case, the machine can more effectively construct concept maps and identify disconnects inside of them.”
2) Generating assessment questions. Anybody who has crafted a multiple-choice midterm or final exam knows how challenging it is to make it representative of the work and create distractors to effectively assess understanding of a topic. PSU is working on a prototype algorithm that, given an OER chapter or a textbook, can suggest multiple-choice assessments.
“This gets into an area of machine learning called adver- sarial learning, which comes out of security. It is how the computer identifies spam messages,” Bowen said. Spam e-mails aren’t real e-mails, although they are trying to look like they are. With the creation of a spam filter, machine learning identifies pattern matches. “We want to do the opposite,” he said. “We want to identify things that don’t fit the pattern but look like they would. What are some things that might exploit gaps in someone’s knowledge? What we have found is the machine creates really difficult multiple- choice tests. It shows very little mercy.”
PSU has not yet begun testing this solution with faculty. “It is important to explain that it is not the goal to replace what the person is doing, but rather to assist the faculty member,” Bowen said. The goal would not be to have the machine
generate multiple choice assessments on the fly, but to help a faculty member craft a multiple choice test that is representative of the material and help simplify the process of creating those tests, he added. The same is true with prerequisite knowledge. It is not to replace the work being done by faculty members, but to support them as they think about prerequisite knowledge.
3) Brainstorming with your computer. A third conceptual area PSU is working on is letting the computer help you brainstorm.
“We all have friends who are really smart and who we go to to bounce ideas off of,” Bowen said. Such a friend might ask if you have thought about other concepts. “You can do that with your computer,” he explained. If you are thinking about a topic, the machine can say, “well based on that, have you thought about x?” It can help you brainstorm an activity and also form or prototype ideas and come out with a concept map or outline that helps you explore new areas.
“So although the original algorithm was designed to generate texts, when we look at it, these three areas are potentially higher value problems to work on. We have moved away from our original research to look at how we can provide more targeted assistance on pain points in developing OER material.”
David Raths is a freelance writer based in Philadelphia. BACK TO TOC
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CAMPUS TECHNOLOGY | October 2017
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