This project aims to combine learning about biomechanics with learning about data science. Biomechanics is the study of the structure and movement of living things. Biomechanics uses data about motion and the science of mechanics to answer questions such as how birds fly and how people walk. This knowledge can then be used for many purposes, such as designing artificial joints and helping workers avoid injuries. Data science applies methods such as artificial intelligence and algorithms to analyze data from any source, including data about human motion. These analyses can lead to new insights and new discoveries about the source of the data. This project will enable biomechanics students to become the source of the movement data that they will analyze using data science methods. Students will use computer vision and machine learning to create datasets from their own movements. They will then use these personally relevant datasets to learn both biomechanics and data science, and to gain a comprehensive understanding of human motion. To support the data collection and analysis, the project will develop a platform for augmented learning and associated curriculum modules for both in-class and out-of-class learning.
This project will develop and implement an augmented learning platform that will enable undergraduate students in different STEM paths to "be the dataset" and interactively acquire formal concepts and feedback in biomechanics and data science. The project has the potential to advance learning sciences and education technology by expanding the understanding of students' interactions with self-generated motion data, as well as the effectiveness of the specific instructional technologies and strategies. The project includes an educational research project that will employ a two-phase design-based approach. Questionnaires will be used to collect data about student demographics, and semi-structured interviews will be used to learn more about student experiences with analyzing self-motion data and using augmented reality technologies, as well as their interests and self-efficacy in biomechanics or data science. Descriptive statistics will be used to acquire an overall understanding of the student sample. Repeated measures ANOVA and ANCOVA will be used to analyze changes in student interests and self-efficacy over time. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources. The IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the program supports the creation, exploration, and implementation of promising practices and tools.
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.
