EAGER: Cultivating Scientific Mindsets in the Machine Learning Era

人工智能(AI)融入了日常生活。目前，人工智能在未来劳动力方面存在巨大的技能缺口。K-12学生的AI学习机会有限，教师的专业发展机会有限，可能会导致劳动力和教育中的AI不公平。本项目通过在K-12 STEM课堂中引入机器学习(ML)作为数据驱动的科学探究的发现工具来解决这些挑战。该项目致力于创建和研究一种新颖的、无编程和基于可视化的基于ML的学习环境。它旨在使数学、编程和数据技能有限的高中生和教师能够发现复杂的科学现象，并从隐藏在现实世界数据中的发人深省的模式中提出重大问题。研究人员将在整个研究活动中纳入STEM边缘背景的高中生，并与罗切斯特大学大卫·T·卡恩斯多样性和领导力中心合作开展外联活动。该项目将有助于NSF促进纳入下一代STEM教育的使命，并将K-12人工智能扫盲作为国家繁荣的推动力。研究人员将开展三项协同研究活动：(1)创建基于ML的可视化学习环境，利用基于新字形的数据可视化和类比学习过程相结合，缓解高中学习者ML和多维模式发现的陡峭学习曲线；(2)采用共同设计的方法，让K-12 STEM教师和数据科学专家参与创建基于ML的科学探究活动；(3)反复评估新的学习环境在支持高中生三个关键学习目标方面的有效性：多维模式发现，围绕聚类和分类的ML概念和方法，以及通过提问、假设生成、解释和论证进行模式启发的科学探究。这个项目的发现将促进我们对ML支持的视觉学习环境的设计和教学指导方针的了解，以最大限度地减少K-12人工智能初学者的认知负荷。由此产生的新颖的学习环境和ML支持的科学探究活动将公开可用。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。

Artificial Intelligence (AI) is woven into the fabric of everyday life. There is currently an enormous skills gap in AI for the future workforce. Limited AI learning opportunities among K-12 students and professional development opportunities for teachers may lead to AI inequity in the workforce and education. This project addresses these challenges by introducing Machine Learning (ML) as a discovery tool for data-driven scientific inquiry in K-12 STEM classroom. The project focuses on creating and studying a novel programming-free and visual-based ML-powered learning environment. It aims to enable high school students and teachers with limited mathematical, programming and data skills to discover complex scientific phenomena and ask big questions from thought-provoking patterns hidden in real-world data. Researchers will include high school students from marginalized backgrounds in STEM throughout the research activities and engage in outreach in collaboration with the David T. Kearns Center for Diversity and Leadership at the University of Rochester. This project will contribute to NSF’s missions on promoting inclusion in next-generation STEM education, and advance K-12 AI literacy as a driving force of national prosperity. Researchers will carry out three synergistic research activities: (1) creating a ML-powered visual learning environment that utilizes a combination of novel glyph-based data visualization and analogical learning process to mitigate the steep learning curve of ML and multi-dimensional pattern discovery for high school learners; (2) adopting a co-design approach to include K-12 STEM teachers and data science experts in creating ML-powered scientific inquiry activities; and (3) iteratively evaluating the effectiveness of the new learning environment in supporting three key learning goals for high school students: multi-dimensional pattern discovery, ML concepts and methods around clustering and classification, and pattern-inspired scientific inquiry through question asking, hypothesis generation, explanation and argument. Findings of this project will advance our knowledge on the design and pedagogical guidelines of ML-powered visual learning environments that minimize cognitive load for novice K-12 AI learners. The resulting novel learning environment, and ML-powered scientific inquiry activities will be made publicly available.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.