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LearningClues revolutionizes how students learn from lectures

"UMSI News." A photo of seven students walking on campus.

Tuesday, 12/13/2022

LearningClues, which resulted from an interdisciplinary effort that combines artificial intelligence with natural language processing to deliver study guides on demand, is revolutionizing how college students absorb lectures.

The brainchild of Perry Samson, professor of climate and space sciences and engineering, the application assists students with their studies by providing easily searchable lecture videos and contextual links to topics discussed in class.

“In surveys of students we’ve learned that study guides are offered in only about 15% of courses while about 85% of students would welcome study guides to help focus their study,” said Samson, who also is an Arthur Thurnau Professor and professor of information. “The question of ‘What should I study?’ is not uncommon, particularly in the first year or two of college.”

When instructors add LearningClues in Canvas, the application can identify key terms in class videos to produce a single, searchable database. Students can tap into the database anytime, whether they are studying for an exam or applying a concept they learned to the real world.

“What we’re attempting is to reverse-engineer the learning objectives of the course using AI,” Samson said. “Based on what’s being discussed in a course, we estimate what discipline it represents and identify terms and concepts that are commonly associated with that discipline.

“Instructors can, of course, overrule or edit the list of AI chosen key terms if they wish, but it’s our goal to support students whether instructors choose to actively participate or not.”

LearningClues can also mine documents and links that instructors have included in their Canvas site and link students to those resources automatically when topics discussed in class are mentioned in those documents or links. LearningClues can also point students to related pages in their textbooks.

“It becomes a way for students to find information more efficiently,” Samson said. “Students are often challenged by competing demands on their time and welcome ways to be more efficient in their studies.”

The application was provided in more than 400 courses in the College of Engineering during the winter 2022 semester, and it will be released in the Master of Applied Data Science program in 2023.

A long-term goal is to make the technology available to colleges and universities everywhere. It is available now at U-M and will be released to several universities across the United States in the coming months.

With funding from the National Science Foundation and support from CoE and Innovation Partnerships, many faculty and students have worked together to make this tech a reality. The application is based in research and built with strong safeguards, expanding what’s possible in college lectures.

Samson worked with 15 students over the summer to release the application to new courses this fall. Students contributed to its web development, coming from programs in computer science, engineering, business and architecture. Development will continue as part of CoE’s Multidisciplinary Design Program.

“I am blessed with an enthusiastic team of students,” Samson said. “Obviously it affects their own learning. We’ve got students both in the School of Information and the College of Engineering working together to make the model smarter.”

He also partnered with Information and Technology Services, the School of Information and CAEN, CoE’s central IT provider, to bring the vision to life.

“It literally takes a village to do this, and part of that village is people who are knowledgeable in what’s called natural language processing,” Samson said.

Kevyn Collins Thompson, associate professor of information in UMSI, and associate professor of computer science in CoE, has collaborated closely with Samson to provide expertise in natural language processing and search engine technology.

In his work, Thompson blends information retrieval, machine learning, natural language processing and large-scale data mining to optimally connect people with information, especially helping them to learn and discover. 

“On the technical side, this project has provided a beautiful opportunity to bring together several of my research themes, including search engines that help people learn, and reliable ways to predict the difficulty of STEM content for individual students,” he said.

“On the personal side, working with Perry has been truly inspiring: Every day he brings relentless energy to pursuing innovative ideas, and a true passion for helping students.”

— by Melissa Priebe. This story originally appeared in The University Record.