ABSTRACT

From elementary school math games to workplace training, computer-based learning applications are becoming more widespread. With these programs, it becomes increasingly possible to use the data generated, such as correct and incorrect problem-solving responses, to develop ways to test for student knowledge and to personalize instruction to student needs. The logs of student responses can capture answers, but they fail to capture critical information about what is happening during pauses between student interactions with the software. This project, led by a team of researchers at Arizona State University and Worcester Polytechnic Institute, will explore the use of measurements of brain activity from lightweight brain sensors alongside student log data to understand important mental activities during learning. The study will examine developmental math learning in college and community college students using the ASSISTments intelligent tutoring system. Using brain imaging, the project team will examine whether students are thinking deeply about the problem or mind-wandering during pauses in the learning tasks and use the combined log and brain data to make predictions about learning outcomes. This work will build a foundation for new methods of combining neuroimaging, machine learning, and personalized learning environments. With a better understanding of when and how learning occurs during pauses in tutoring system use, learning technology researchers and developers will be able to create adaptive interventions within tutoring systems that are better personalized to the needs of the individual. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This project has of three goals: 1) Integrating multiple data streams for the creation of an interdisciplinary corpus; 2) Detecting real-time changes in cognitive states during pauses in log data; and 3) Predicting learning outcomes from brain-based and log-based inferences of cognitive states. In addressing these goals, the team will collect brain data, using functional near-infrared spectroscopy neuroimaging, and behavioral data from controlled, well-understood tasks related to rule learning and mind wandering and from authentic learning tasks. Cognitive neuroscience research involving recordings of brain activity traditionally requires paradigms with highly constrained stimuli, timing, and task requirements, whereas research in complex real-world environments such as tutoring systems rarely align with these paradigms. Features of the brain activity during the cognitive tasks will be used to make inferences about student cognition during authentic learning tasks. In addition, brain features will be combined with log data features to create machine learning models that make accurate predictions of student robust learning outcomes, to be assessed using a posttest given after students use the interactive learning environment. Contributions of this project to STEM learning will include improved understanding of how students build knowledge in response to instructional events within digital learning environments, the construction of better predictive models of when students learn from the use of personalized learning environments, and a mapping between learning processes and the length and context of pauses. This project will also contribute to understandings of how to combine analyses of neuroimaging data and log data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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