6/15/2022

Bringing artificial intelligence into high school STEM classrooms

Students working in science lab Researchers explore learning opportunities of machine learning in promoting scientific literacy
Artificial intelligence (AI) is woven into the fabric of everyday life and is projected to create 58 million new jobs by 2023. How can we prepare K-12 students for this new reality? Recognizing the importance of these recent developments, New York State has recently approved new Computer Science and Digital Fluency standards for K-12 schools that should be implemented starting in 2024. Yet very little is currently going on in K-12 schools around AI.
 
A new University of Rochester project aims to fill this gap by introducing machine learning, a subfield of AI that gives computers the ability to learn without being explicitly programmed, as a discovery tool for data-driven scientific inquiry in K-12 science, aligned with cross-cutting concepts and scientific practices at the core of the new Next Generation Science Standards and the latest New York State Computer Science and Digital Fluency standards. The “EAGER: Cultivating Scientific Mindsets in Machine Learning Era” is funded by a two-year grant from the National Science Foundation’s Division of Information and Intelligent Systems (IIS).
 
Rochester researchers, led by Zhen Bai (Computer Science faculty) as the principal investigator and including co-principal investigators Michael Daley and Raffaella Borasi (Warner School of Education faculty) and Jiebo Luo (Computer Science faculty), will explore the unique learning opportunities of machine learning in promoting scientific inquiry. Michael Occhino, director of science education outreach for the Warner School’s Center for Professional Development and Education Reform, will also be a senior associate on the project. 
 
The team will create and research a novel programming-free, visual-based machine learning-powered learning environment called Group-It that will allow high school students and teachers with limited mathematical, programming, and data skills to learn basic machine learning concepts and methods and understand patterns hidden in multi-dimensional data in STEM contexts. Group-It will utilize a combination of novel glyph-based data visualization and analogical learning process to mitigate the steep learning curve of machine learning and multi-dimensional pattern discovery for high school learners.
 
Additionally, the team will conduct research activities focused on a co-design approach to include K-12 STEM teachers and data science experts in creating machine learning-powered scientific inquiry activities and evaluating the effectiveness of Group-It in supporting three key learning outcomes for high school students. The learning goals for students are multi-dimensional pattern discovery, machine learning concepts and methods, and pattern-inspired scientific inquiry.
 
“Our goal for this project is to develop the tools and strategies that allow high school STEM classrooms to use machine learning as a common analysis tool, much like today’s calculator, to make sense of complex scientific phenomena and ask big questions ignited by thought-provoking patterns hidden in real-world data,” says Daley. “With Group-It, we will equip and support teachers in creating machine learning-powered scientific inquiry activities for their students. The project can make a broader impact by sharing our scientific research, ideas, and tools with educators in an easy, accessible, and understandable way.”
 
The resulting novel Group-It visual learning environment and machine learning-powered scientific inquiry activities will be publicly available on the project’s website. Resources will also be available to help teachers make the best use of these products and findings from the evaluation study to bring credibility to them. Additionally, research findings from the project will be disseminated through peer-reviewed publications and research conferences, advancing knowledge of the design and pedagogical guidelines of machine learning-powered visual learning environments that minimize cognitive load for novice K-12 AI learners.
 
The project will reach 1,000 high school students from historically marginalized communities in STEM throughout the research activities and engage in outreach in collaboration with the University’s David T. Kearn Center for Leadership and Diversity and urban school districts. Nearly a dozen STEM teachers, who value computing, data-driven, and inquiry-based learning, will be recruited to participate in the research activities. 
 
The researchers foresee the project playing a more significant role in preparing students for projected AI shortages, increasing AI diversity and promoting a more scientific and AI literate society. The project contributes to the National Science Foundation’s missions of promoting inclusion in next-generation STEM education and advancing K-12 AI literacy as a driving force of national prosperity.
 
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Media Contact: Theresa Danylak
tdanylak@warner.rochester.edu
(585) 278-6273 (cell)

Tags: Michael Daley, Michael Occhino, National Science Foundation, Raffaella Borasi, STEM