2026-09-19T13:32:44Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1398632022-02-08T03:01:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Breazea, Cynthia Lynn
Hoekstra, Chessa Nicole
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-02-07T15:09:21Z
2022-02-07T15:09:21Z
2021-09
2021-11-03T19:25:27.174Z
https://hdl.handle.net/1721.1/139863
As AI becomes prevalent in society, AI education and AI literacy are also increasingly important fields. Reinforcement learning (RL) plays and will continue to play an intense role in many systems, including online advertising, self-driving cars, and personalized tutoring. There is a corresponding need for education of RL. In my thesis work, I developed activities and materials that teach RL to middle and high school students who don’t necessarily have backgrounds in computer science or AI. Then, I evaluated these materials by teaching a series of pilot workshops. The project had two main goals: effectively educating the students in the workshops, and contributing insights to the ongoing research of AI education. In this thesis, I will go over the curriculum design, as well as the analysis of each pilot.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Learning from Experience: An Interactive and Ethical Curriculum for Teaching Reinforcement Learning
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f9fd0106-f2a9-426c-9d9f-2f6904e3f34e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Learning from Experience: An Interactive and Ethical Curriculum for Teaching Reinforcement Learning</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2021-09</PublicationDate>
<Authors>
<Author>
<DisplayName>Hoekstra, Chessa Nicole</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>As AI becomes prevalent in society, AI education and AI literacy are also increasingly important fields. Reinforcement learning (RL) plays and will continue to play an intense role in many systems, including online advertising, self-driving cars, and personalized tutoring. There is a corresponding need for education of RL. In my thesis work, I developed activities and materials that teach RL to middle and high school students who don’t necessarily have backgrounds in computer science or AI. Then, I evaluated these materials by teaching a series of pilot workshops. The project had two main goals: effectively educating the students in the workshops, and contributing insights to the ongoing research of AI education. In this thesis, I will go over the curriculum design, as well as the analysis of each pilot.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>