Open education, open textbooks, massive open online courses (MOOCs) — there’s a common element that ties together many of higher education’s most-hyped online learning buzz phrases.
You’ll find it in the first “O” of MOOC.
But educators adopting that “O,” also tend to inherit the “M,” as in massive amounts of students, information and resources. Managing a large volume of material and finding ways to get such a large body of students to connect with that material can be a challenge.
The Massachusetts Institute of Technology (MIT) has partnered with Fujitsu Laboratories of America, Inc., to begin addressing the problem with a new platform they call Guided Learning Pathways. The project’s team is currently exploring ways to introduce the platform into the MOOC systems of edX.
Announced June 17 at the Sixth Conference of MIT’s Learning International Networks Consortium but in the works since 2010, the platform allows students to access and organize free, high quality learning materials from all over the internet based on the student’s interest and level of understanding.
“Every learner has a different profile,” said Jun Wan, a researcher at Fujitsu. “In order to maximize their learning outcome, we’ve constructed a guided pathway and with it you can see personalized learning, adaptive learning. There’s a huge amount of these open educational resources, so we needed to make a way to help learners find what they need.”
See Page 2 for how the Guided Learning Pathways platform works.
The platform works by extracting a list of subjects that correspond with topics in the learning materials and then organizing the subjects by size into different layers. The subjects are linked to the material, and some materials can be linked to more than one topic.
For example, the top layer of the process could be science, technology, engineering and math (STEM). The next layer could be divided into physics and biology.
If a calculus student needs to sort through the thousands of open educational resources related to STEM just for materials about derivatives, a guided pathway is then created through the STEM layer, into the physics and biology layer, where it ignores the biology path and leads the student onto the next layer.
At this layer, the student can find differential calculus lecture notes and physics videos focusing on mechanics.
By using detailed learning behavior technology created by MIT and Fujitsu, educators and learning system providers can predict learning outcomes through simulations without having to rely on assessing the thousands of students who are enrolled in a large online course.
These algorithms can help system providers determine the best suited material for such a large amount of students, said Fujitsu’s director of business development Surya Kumar Josyula, further helping learners and teachers find the right material faster.
“This engine simulate environments, tests out what’s working and what’s not, without even the having students there,” Josyula said. “But at the same time the guided pathways are personalized down to the individualized student level, which typical learning engines do not do.”