Broadening the Use of Learning Analytics in STEM Education Research

扩大学习分析在 STEM 教育研究中的应用

• 批准号: 2321129
• 负责人: Ryan Baker
• 金额: $ 49.99万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321129&HistoricalAwards=false
• 关键词: Broadening; Use; Learning; Analytics; STEM

Learning Analytics (LA) has emerged over the past 15 years as an interdisciplinary practice that applies computational methods from data science and artificial intelligence (AI) to better understand and improve student learning. However, professional development and training opportunities -- particularly for scholars from underrepresented groups -- have not kept pace with the growing demand for STEM education researchers prepared to use these methods. To address this issue, the Institute on Broadening the Use of Learning Analytics in STEM Education Research will provide participating scholars with ongoing training and support in using cutting-edge techniques for their STEM education research. The project team will also develop easy-to-use and adaptable curriculum materials that participants can use to provide training for faculty and students at their home institutions. All curriculum materials will be published on a freely-available website so that education researchers and practitioners everywhere can benefit, not just those directly involved in the program. The first year of the Institute will focus on creating a modular and adaptable curriculum composed of 25-30 instructional modules that can be assembled into learning opportunities ranging from short duration workshops or webinars to multiple distinct semester-long courses. Years 2 and 3 will focus on training two cohorts of STEM education researchers (50 participants in total) to learn from, and teach with, curriculum materials developed during Year 1. Each cohort will attend a 5-day, in-person summer training institute and participate in online workshops during the academic year focused on self-selected methods and topics (e.g., large language models, supervised machine learning, network analysis) relevant to participants’ research interests. In addition, the project team will provide participants with ongoing training and support to pilot instructional materials with faculty or students at their home institutions throughout the year. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This project is also supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research in the core areas of STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development.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.

学习分析（LA）在过去的15年中已经成为一种跨学科的实践，应用数据科学和人工智能（AI）的计算方法来更好地理解和改善学生的学习。然而，专业发展和培训机会-特别是对于来自代表性不足群体的学者-没有跟上对准备使用这些方法的STEM教育研究人员日益增长的需求。为了解决这个问题，在STEM教育研究中扩大学习分析的使用研究所将为参与的学者提供持续的培训和支持，以使用尖端技术进行STEM教育研究。该项目小组还将开发易于使用和适应性强的课程材料，供参与者在本国机构为教师和学生提供培训。所有的课程材料都将在一个免费的网站上发布，以便世界各地的教育研究人员和实践者都能受益，而不仅仅是那些直接参与该计划的人。该研究所的第一年将专注于创建一个模块化和适应性强的课程，由25-30个教学模块组成，这些模块可以组装成从短期研讨会或网络研讨会到多个不同的学期课程的学习机会。第二年和第三年将重点培训两批STEM教育研究人员（共50名参与者），以学习和教授第一年开发的课程材料。每个队列将参加为期5天的暑期培训机构，并在学年期间参加在线研讨会，重点是自选方法和主题（例如，大型语言模型，监督机器学习，网络分析）与参与者的研究兴趣相关。此外，项目小组将为参与者提供持续培训和支持，以便全年在其所在机构与教师或学生一起试用教学材料。 这个项目是通过与比尔梅林达盖茨基金会，施密特期货，沃尔顿家族基金会的合作伙伴关系支持。该项目也得到了NSF的EDU核心研究STEM教育研究能力建设的支持（ECR：BCSER）计划，旨在培养研究人员在STEM学习和学习环境的核心领域开展高质量STEM教育研究的能力，扩大STEM领域的参与，该奖项反映了NSF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。