Learning, Perception, and Embodiment
My research intersects core areas of the learning sciences (i.e., cognitive and educational psychology, mixed methods) to study student reasoning and learning in mathematics. Informed by theories of perceptual learning and embodied cognition, I investigate how the smallest details of students' learning environments can shape performance and learning in early algebra, ultimately aiming to provide recommendations to improve online learning and technology-augmented environments. I apply a range of quantitative methods to study the impact of perceptual (i.e., visual and auditory) features in instructional materials on student learning and performance in online environments.
In my second line of research, I enjoy collaborating with data scientists to explore how methods (e.g., machine learning, multi-level modeling) across the learning sciences can and should be used in different contexts.
Student Behavior, Games, and Computational Thinking
I have also studied the physical behaviors and gestures demonstrated by college and elementary learners during hands-on tasks and games with Dr. Ivon Arroyo at the University of Massachusetts-Amherst. Measurement is an area that many students struggle with in elementary school and through interview sessions, I was able to code and analyze the actions, speech, and gestures that college and elementary students exhibit while estimating the dimensions of different objects. By doing so, I identified behaviors that indicate different types of, or gaps in, conceptual knowledge that might be helpful for assessing and assisting students in the future.
This research sparked questions about how machine learning techniques and natural language processing may be able to provide more insights into the connection between student behavior and cognition from such a rich dataset. With a a team of computer and data scientists, we recently published this process and our thoughts on blending embodied design and learning analytics.
Over the past four years, I have also worked closely with the project team to develop and research the Wearable Learning Cloud Platform (WLCP). The WLCP is a free online website for students to play educational games with their mobile devices and also for students to design and program their own games. The goal is for students to develop content knowledge while playing games and to develop computational thinking skills during the game creation process. Most recently, our team led a professional development program for teachers to pilot our curriculum for the WLCP. Read more here
To further this work and advance research on computational thinking (CT), two graduate colleagues and I led a panel session at the virtual Learning Science Graduate Student Conference to discuss the ways in which we can define, measure, and teach CT in K-12 education. Watch our panel on Computational Thinking!