Rice University's OpenStax Tutor Tackles Personalized Learning

Educational 'Technology' Across the Ages

Can personalized learning technology save university students from their own bad study habits?

One of the great promises of applying technology to education is that it will allow every student to have a personalized learning plan, with a closed-loop link between learning assessments and further instruction on areas of weakness.

At SXSWEdu, the educational innovation and learning technologies spin-off of the South by Southwest conference, Richard G. Baraniuk of Rice University and Andrew C. Butler of Duke University spoke about some of the barriers to achieving that goal and presented one possible solution, OpenStax Tutor, an open-source resource aimed at improving college study skills.

"This is not the first time technology has promised to revolutionize education," Baraniuk said. In their day, the movie projector and the television were new technologies promoted for their revolutionary educational potential, he noted.

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"The difference is, for the first time we have technologies that are not just broadcast technologies. We have technologies that can acquire massive amounts of personalized data about students as they work through problems," said Baraniuk. That data can drive feedback to the students, as well as feedback for the improvement of curricula and research into better educational methods, he said.

This is the latest in a series of projects Baraniuk has championed through the Rice Center for Digital Learning and Scholarship. Back in 1999, his team introduced Connexions, a site that encourages educators to create modular learning resources and share them freely. OpenStax College was introduduced in February 2012 to promote the creation and promotion of open-source college textbooks. OpenStax Tutor also is aimed primarily at college-level education, although components of the software also are being used as part of Rice's STEMScopes program supporting science, technology, engineering and math education for the K-12 grades.

For the OpenStax Tutor project, Baraniuk, a professor of electrical and computer engineering, has teamed up with experts in cognitive science including Butler.

One of the problems with current approaches to learning is they tend to be "cognitively uninformed," meaning they fail to take advantage of the latest research on how we really learn and remember what we have learned, Baraniuk said.

Left to their own devices, students also tend to choose ineffective study strategies, Butler said. "They're very focused on the short term, so there's a lot of cramming going on," he said. That's a problem because information learned in a rush tends not to be retained very well, he said. What works much better is to space out learning, with testing and refreshing doses of learning spread out over time.

What's most productive isn't necessarily what's easiest. "We want to present students with desirable difficulties," Butler said. "So we have this tension between what people like and what's good for them." Although these cognitive principles could be applied without the use of technology, the benefit of an online learning system is the volume of data it can collect and drive back into future improvements, Butler said. Early results from OpenStax Tutor show that the boost in knowledge retention, just by driving students to retrieve and use their knowledge more frequently, is worth about a half letter grade in performance improvement, he said.

Attempts to personalize the learning experience are not new. Their downfall in most cases has been that they are too expensive and labor intensive, requiring the hand coding of rules assembled by lectures in each discipline, Butler said. OpenStax Tutor is trying to break that cycle by applying machine learning techniques to the problem of analyzing student performance and making recommendations for further study, he said. "We need to make this technology cheaper and easier to apply."

Although the idea that a computer could figure out study recommendations for students without human intervention might be foreign to educators, it's exactly the sort of thing Netflix and Amazon are doing when they recommend additional products you might enjoy based on your past search and purchase activity, Baraniuk said. "Tremendous progress has been made on so-called recommender systems."

OpenStax Tutor is designed so that individual students decide whom their data will be shared with, making it possible for them to be working with multiple instructors and learning coaches on the same material. Still, he recognizes the continuing need to focus on issues of student data privacy that will be "as vexing as anything having to do with electronic medical records."

Currently in beta, OpenStax Tutor is being made available as a free Web resource, although in the future there could be a charge to institutions that implement it on a large scale, Baraniuk said. So far, it works best with multiple-choice assessments, although he hopes that technologies for assessing free-form text will advance rapidly in the coming years. The application is designed for use with other open educational resources, such as those from Connexions or Quadbase assessment quizzes. However, individual professors can assemble any assortment of Web-accessible resources, including proprietary ones, to create homework assignments or digital study guides for their students, Baraniuk said.

"My feeling is the more open your tools, the more users your system is going to have and the more data you get to work with," Baraniuk said.

Follow David F. Carr on Twitter @davidfcarr or Google+.

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