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。生物力学使用有关运动的数据和力学科学来回答诸如鸟类如何飞行、人类如何行走等问题。然后，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，包括关于人体运动的数据。这些分析可以带来关于数据来源的新见解和新发现。这个项目将使生物力学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来根据自己的动作创建数据集。然后，他们将使用这些与个人相关的数据集来学习生物力学和数据科学，并全面了解人体运动。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的课程模块，供课堂内和课外学习。该项目将开发和实施一个增强学习平台，使处于不同STEM路径的本科生能够成为数据集，并互动地获取生物力学和数据科学的形式概念和反馈。该项目通过扩大学生与自我生成的运动数据的互动以及特定教学技术和策略的有效性的理解，有可能促进学习科学和教育技术的发展。该项目包括一个教育研究项目，该项目将采用基于设计的两阶段方法。调查问卷将被用来收集有关学生人口统计学的数据，半结构化访谈将被用来更多地了解学生在分析自我运动数据和使用增强现实技术方面的经验，以及他们对生物力学或数据科学的兴趣和自我效能。描述性统计将被用来获得对学生样本的全面了解。重复测量的ANOVA和ANCOVA将被用来分析学生兴趣和自我效能感随时间的变化。该项目得到了美国国家科学基金会改善本科生STEM教育计划：教育和人力资源的支持。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中，通过该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.