This project conducts interdisciplinary research to advance understanding of embodied learning as it applies to STEM topics across a range of current technology-based learning environments (e.g., desktop simulations, interactive whiteboards, and 3D interactive environments). The project builds on extensive research, including prior work of the PIs, regarding both embodied learning and statistical learning. The PIs describe embodied learning as engaging the neuromuscular systems of learners as they interact with the world around them visually, aurally, and kinesthetically in order to construct new knowledge structures. Statistical learning is described as the ability to learn, often without intent, which sequences of stimuli are consistent with a set of rules. An example of statistical learning is pattern recognition, which is central to mastery of complex topics in many STEM disciplines including physics and mathematics.
The project has two central research questions: How are student knowledge gains impacted by the degree of embodied learning and to what extent do the affordances of different technology-based learning environments constrain or support embodied learning for STEM topics? To investigate these questions, the PIs are conducting three series of experiments in five phases using two physics topics. The first four phases are developmental and the final phase implements and assesses the two modules in schools (20 plus teachers, 700 plus K-12 students) in Arizona and New York (15 total sites, 10 plus public schools, minimum one Title I school).
The aim of this project is to meld these two research trajectories to yield two key outcomes: 1) basic research regarding embodiment and statistical learning that can be applied to create powerful STEM learning experiences, and 2) the realization of exemplary models and principles to aid curriculum and technology designers in creating learning scenarios that take into account the level of embodiment that a given learning environment affords.
