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Creating pathways that stimulate high school learners’ interest in advanced topics with the goal of building a diverse, gender-balanced, future-ready workforce is crucial. To this end, we present the curriculum of a new, high school computer science course under development called Computer Science Frontiers (CSF). Building on the foundations set by the AP Computer Science Principles course, we seek to dramatically expand access, especially for high school girls, to the most exciting and emerging frontiers of computing, such as distributed computation, the internet of things (IoT), cybersecurity, and machine learning. The modular, open-access, hands-on curriculum provides an engaging introduction to these advanced topics in high school because currently they are accessible only to CS majors in college. It also focuses on other 21st century skills required to productively leverage computational methods and tools in virtually every profession. To address the dire gender disparity in computing, the curriculum was designed to engage female students by focusing on real world application domains, such as climate change and health, by including social applications and by emphasizing collaboration and teamwork. Our paper describes the design of curricular modules on Distributed Computing, IoT/Cybersecurity, and AI/Machine Learning. All project-based activities are designed to be collaborative, situated in contexts that are engaging to high school students, and often involve real-world world data. We piloted these modules in teacher PD workshops with 8 teachers from North Carolina, Tennessee, Massachusetts, Pennsylvania, and New York who then facilitated virtual summer camps with high school students in 2020 and 2021. Findings from teacher PD workshops as well as student camps indicate high levels of engagement in and enthusiasm for the curricular activities and topics. Post-intervention surveys suggest that these experiences generate student interest exploring these ideas further and connections to areas of interest to students.
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Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving
Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and the challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a “codeless” and free ML neural network building software, Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on the color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to students’ daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.
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- Award ID(s):
- 2246548
- PAR ID:
- 10526055
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- Journal of Chemical Education
- Volume:
- 101
- Issue:
- 2
- ISSN:
- 0021-9584
- Page Range / eLocation ID:
- 675-681
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.jchemed.3c00589
