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For a growing class of prediction problems, big data and machine learning analyses can greatly enhance our understanding of the effectiveness of public investments and public policy. However, the outputs of many machine learning models are often abstract and inaccessible to policy communities or the general public. In this article, we describe a hands-on teaching case that is suitable for use in a graduate or advanced undergraduate public policy, public affairs or environmental studies classroom. Students will engage on the use of increasingly popular machine learning classification algorithms and cloud-based data visualization tools to support policy and planning on the theme of electric vehicle mobility and connected infrastructure. By using these tools, students will critically evaluate and convert large and complex datasets into human understandable visualization for communication and decision-making. The tools also enable user flexibility to engage with streaming data sources in a new creative design with little technical background.
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Integrating Mathematics and Educational Robotics: Simple Motion Planning
This paper shows how students can be guided to integrate elementary mathematical analyses with motion planning for typical educational robots. Rather than using calculus as in comprehensive works on motion planning, we show students can achieve interesting results using just simple linear regression tools and trigonometric analyses. Experiments with one robotics platform show that use of these tools can lead to passable navigation through dead reckoning even if students have limited experience with use of sensors, programming, and mathematics.
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- PAR ID:
- 10114870
- Date Published:
- Journal Name:
- Proceedings of the 10th International Conference on Robotics in Education, RiE 2019
- Page Range / eLocation ID:
- 262--269
- 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/978-3-030-26945-6_23
