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This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students with equity of access. A four-day microseminar, titled Swarming Powered by Neuroscience (SPN), was conducted virtually through a combination of presentations and interactive computer game simulations. The SPN microseminar was delivered by subject matter experts in neuroscience, mathematics, multi-agent swarm robotics, and education. The objective of this research was to determine if taking an interdisciplinary approach to high school education would enhance the students learning experiences in fields such as neuroscience, robotics, or computer science. This study found an improvement in student engagement for neuroscience by 16.6%, while interest in robotics and computer science improved respectively by 2.7% and 1.8%. The majority of students (64%) strongly agreed that they enjoyed learning from an interdisciplinary team of experts and 70% strongly agreed that the microseminar emphasized the need to have instruction teams with diverse disciplinary backgrounds. The curriculum materials, developed for the SPN microseminar, can be used by high school teachers to further evaluate interdisciplinary instructions across life and physical sciences and computer science.
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A New Approach to High School Data Science: Set Theory and Logic
Abstract: There is a consensus to introduce data science to secondary schools. However, data science is interdisciplinary in nature and not easy to teach in K−12 settings. We proposed a new approach to integrate mathematics, statistics, and programming—the foundations of data science—for high school students based on set theory and logic. We developed an 8-week data science foundation course and implemented it in a public high school. We conducted semistructured group interviews to collect students’ feedback on the course and the new approach. Students thought the approach could well connect the topics and helped them learn the interdisciplinary content.
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- Award ID(s):
- 2201393
- PAR ID:
- 10519448
- Publisher / Repository:
- International Society of the Learning Sciences
- Date Published:
- ISBN:
- 979-8-9906980-0-0
- Page Range / eLocation ID:
- 2101 to 2102
- 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
- Conference Paper:
- https://doi.org/10.22318/icls2024.492863
