CSC News

Adaptive Experimentation Accelerator Team Wins XPRIZE Digital Learning Challenge Grand Prize

Dr. Thomas Price, Assistant Professor in Computer Science and Director of the HINTS lab at NC State, is part of a team called the Adaptive Experimentation Accelerator, that won the US$500,000 grand prize in the XPRIZE Digital Learning Challenge grand prize.

Launched in 2021, the Digital Learning Challenge is a global competition that incentivized teams to modernize, accelerate and improve technology and processes for evaluating and measuring effective learning and education.

Adaptive Experimentation Accelerator is a collaborative effort between Price from NC State; Joseph Jay Williams, Assistant Professor in the Computer Science Department at University of Toronto; Norman Bier, Director of the Open Learning Initiative (OLI) and Executive Director of the Simon Initiative at Carnegie Mellon University; and John Stamper, Associate Professor at the Human-Computer Interaction Institute at Carnegie Mellon University. 

Price and the team developed a tool that allows educators to conduct experiments in the classroom to determine which teaching methods are most effective.  The team addressed the disadvantages of utilizing a traditional experiment to test the effectiveness of an instructional practice. In a traditional experiment, researchers would apply the teaching strategy to one group, and the other group — the control group — would not experience this strategy throughout the experiment. However, Price said, this experimental structure isn’t always ideal in a classroom setting because the control group hasn’t received the potential benefits of the instructional method if it proves to be favorable, and his team uses adaptive experimentation to enhance and personalize learning for students.

“What we want to do is get the help to as many students as possible while still learning from the experiment,” Price said. “An adaptive experiment uses various methods, often based on machine learning, to actually move students to different conditions depending on how the experimental study is playing out.”

An adaptive experiment is advantageous because it tailors the educational experience of students throughout the duration of the experiment based on evidence, Price said. 

“One example is self-explaining — trying to formulate an explanation of why something worked,” Price said. “If you're really confident in the subject matter, then an explanation might help you reflect more deeply on what you already know. But if you aren't really sure you've mastered the material in the first place, trying to create that explanation might actually make you feel worse; it might make you feel nervous that you're not able to articulate it, it might make you feel frustrated because you don't actually have an answer. 

“In an adaptive experiment, the machine learning model that's assigning students to conditions can also be aware of those contextual factors, and it might pick up on that, especially with larger experiments where we have more data. So it might start to assign students who don't have as much experience not to self-explain, and students who have more experience to self-explain.”

The team employed a software architecture called MOOClet, which supports adaptive experimentation on educational platforms and uses machine learning to develop data-driven solutions for personalized learning. Price said this idea has the long-term potential to help build a learning experience for students that best suits their needs.

“One vision of this is the idea of a continually improving classroom: The course that you are taking today is collecting data that will inform what that course looks like, not just for the next semester, but even in the next lesson,” Price said. “So if we have tools that allow instructors to adapt their course as the semester progresses based on what they're learning from their students, to gain insights about what is working and what is not, for teachers to feel some freedom to try different things and choose the one that's working best or even assign the one that's working best to different groups depending on their needs, I think that's a vision that is really powerful; it's a vision of personalization that people have been talking about for many years.”

The Digital Learning Challenge incentivizes the use of AI methods, big data, and machine learning to better understand practices that support educators, parents, policymakers, researchers, and the tens of millions of Americans enrolled in formal education every year. Competitors are in pursuit of a deeper understanding of which educational processes are working well and which should be improved to achieve better outcomes. The competition is sponsored by the Institute of Education Sciences (IES), the independent and nonpartisan statistics, research, and evaluation arm of the U.S. Department of Education.  

Please click here for more information on the Digital Learning Challenge.

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