Enhancing Undergraduate Learning About Biomechanics and Data Science Through Augmented Reality and Self-motion Data

该项目旨在将学习生物力学与学习数据科学结合起来。生物力学是研究生物的结构和运动的学科。生物力学使用运动数据和力学科学来回答诸如鸟如何飞行和人如何行走之类的问题。这些知识可以用于许多目的，例如设计人工关节和帮助工人避免受伤。数据科学应用人工智能和算法等方法来分析来自任何来源的数据，包括有关人体运动的数据。这些分析可以导致关于数据来源的新见解和新发现。这个项目将使生物力学的学生成为他们将使用数据科学方法分析的运动数据的来源。学生将使用计算机视觉和机器学习从他们自己的动作中创建数据集。然后，他们将使用这些个人相关数据集来学习生物力学和数据科学，并获得对人体运动的全面理解。为了支持数据收集和分析，该项目将开发一个增强学习平台和相关课程模块，用于课堂和课外学习。该项目将开发和实施一个增强学习平台，使不同STEM路径的本科生能够“成为数据集”，并以交互方式获取生物力学和数据科学的正式概念和反馈。该项目有可能通过扩展对学生与自生成运动数据的互动的理解，以及特定教学技术和策略的有效性，来推进学习科学和教育技术。该项目包括一个教育研究项目，将采用两阶段设计为基础的方法。调查问卷将用于收集学生人口统计数据，半结构化访谈将用于通过分析自我运动数据和使用增强现实技术来了解更多关于学生体验的信息，以及他们对生物力学或数据科学的兴趣和自我效能。描述性统计将用于获得学生样本的整体理解。重复测量方差分析和方差分析将用于分析学生兴趣和自我效能感随时间的变化。本项目由美国国家科学基金改进本科STEM教育计划：教育与人力资源资助。IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该项目处于“参与学生学习”轨道，通过该轨道，该项目支持有前途的实践和工具的创建、探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.