The National Academy of Engineering identified advancing personalized learning as one of its 14 Grand Challenges for Engineering. To make progress on this challenge, a research team with expertise in cognitive and learning sciences, physics education, computer systems, bio-sensing, and machine learning will work together to bring advanced data acquisition and processing technologies to support rapid testing and evaluation of cognitive and learning science hypotheses. The project will use a convergent approach to advance the study of active learning and the role of stress in classes using active learning in introductory physics. The effort is centered on two objectives. The first is to develop technology to build a flexible framework for creating instruments to measure stress for use in introductory college physics. The second is to pilot studies to develop a joint cognitive and learning science-based understanding of the role of stress and the mechanisms for learning in the context of multi-perspective conversations (MPCs) among students in introductory physics. While MPCs are known to play an important role in developing conceptual understanding in disciplines such as physics, little is understood concerning the impact of stimulation and stress during MPCs. Because dozens of MPCs occur simultaneously in a classroom, they are challenging to record and study. The automated transcript and machine learning tools developed by this project will allow for the collection and study of significantly more conversations. Cognitive science lab experiments using advanced bio-sensors will study stress and learning initially in a controlled setting. Subsequent blending of biosensor and audio/video data will allow the issues of stress and learning to be studied for the first time in a larger classroom environment. One of the key broader outcomes of the project will be the construction of an interdisciplinary convergent team. Activities will focus around building shared vocabulary, cross-disciplinary meetings, public workshops and the training of future convergent researchers through graduate courses and participation in this project. The award is supported by funding from OIA, EHR, ENG, and SBE.
The technology development efforts are divided into two aspects each with an interdisciplinary team of cognitive scientists, learning scientists and engineers. The Aspect I research team will develop advanced bio-sensors to measure stress through unobtrusive wearable patches and classroom cortisol sensors. These sensors will be used in the cognitive science lab to study the effects of stress in learning by measuring attention, immediate memory, and the ability to construct knowledge schemas. In Aspect II, machine learning methods based on topological data analysis will be developed that combine audio/video recording and the biosensors data to allow learning and cognitive scientists to study active learning environments at scale. The focus of these pilot efforts will be on the study of multi-perspective conversations (MPCs) in an introductory physics classroom. Flexible framework for instrumenting learning environments, complete with stress sensors and automated transcripts and machine learning tools, will be tested first with a small number of concurrent MPCs and then later at classroom scale. Researchers will verify the technology and begin investigating the relationship between MPC and learning outcomes, the conditions that foster MPCs between students, and the role of stress in shaping cognitive processes underlying MPCs. The thread-based sensors will use novel materials and are non-obtrusive and wearable and wireless. The use of topological data analysis (TDA) will provide a flexible machine learning framework that can grow and adapt both with the size of the data sets and their heterogeneity. These technologies will be integrated and deployed in a classroom-scale system by computer systems researchers.
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.
