课题基金 / 基金详情

I-Corps: Digital Platform for Informal Learning Experiences to Encourage Curiosity in STEM Career Paths

I-Corps: Digital Platform for Informal Learning Experiences to Encourage Curiosity in STEM Career Paths
I-Corps:提供非正式学习体验的数字平台,鼓励对 STEM 职业道路的好奇心
批准号:
2031900
负责人:
Mary Lou Maher
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-01 至 2021-07-31

项目摘要

项目成果

Mary Lou Maher的其他基金

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中文摘要
翻译
这个I-Corps项目更广泛的影响/商业潜力是开发一个数字平台,改变幼儿感知和参与STEM职业探索的方式。 促进STEM教育的因素,特别是对代表性不足的群体,尚未纳入个性化的学习计划。 这项技术将使用人工智能来开发定制的教育计划。这个I-Corps项目的基础是开发和评估一种新的方法,通过将惊喜模型与用户偏好模型相结合来生成推荐,以提供激发好奇心的推荐。拟议的技术是一个数字平台,使学生能够追求STEM学习和职业。该项目将结合人工智能和非正式学习实践,以个性化的方式推荐学习模块。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact/commercial potential of this I-Corps project is the development of a digital platform that transforms the way young children perceive and engage in STEM career exploration. Factors promoting STEM education, particularly for under-represented groups, have not been incorporated into personalized learning plans. This technology will use artificial intelligence to develop customized educational plans. This I-Corps project is based on the development of the development and evaluation of a new approach to generate recommendations by combining a model of surprise with a model of user preference to deliver curiosity-inspiring recommendations. The proposed technology is a digital platform that empowers students to pursue STEM learning and careers. This project will incorporate artificial intelligence and informal learning practices to recommend learning modules on a personalized basis.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.
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会议论文
Conference: NSF Workshop: Expanding Capacity and Diversity in AI Education
Examining the Effects of Course Climate, Active Learning, and Intersectional Identities on Undergraduate Student Success in Computing
EAGER: An Interactive Learning Analytics Framework based on a Student Sequence Model for understanding students, retention, and time to graduation
Collaborative Research: Developing a Systemic, Scalable Model to Broaden Participation in Middle School Computer Science
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