Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Matthew Hagaman

dblp:371/6102 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
K-12 AI education
0.812024
Foundations of Autonomous Vehicles: A Curriculum Model for Developing Competencies in Artificial Intelligence and the Internet of Things for Grades 7-10 · AAAI 2024
YearPublicationVenuePosition
2024 Foundations of Autonomous Vehicles: A Curriculum Model for Developing Competencies in Artificial Intelligence and the Internet of Things for Grades 7-10
abstract
A few states (e.g., Maryland, Georgia, and Florida) have initiated efforts to incorporate artificial intelligence outcomes in K-12 education but others are still relying on informal spaces for learning and literacy in this area. In this manuscript, we share the curriculum and content of an informal effort focused on students in grades 7-10. We combined artificial intelligence competencies with Internet of Things skills to enable meaningful learning covering all Five Big Ideas in AI. In our one-week summer camp, students experimented with perceptions by working with vision, infrared, and ultrasonic sensors. They learned about representation through work with neural network playgrounds. Students engaged in supervised learning of an image processing model and used the model to control the actions of a robot car. Natural interactions and societal impacts were assessed as students observed the robot car's behavior. Results demonstrate that our curriculum was successful in achieving its objectives. Excluding the robot car kit, the curriculum was created using free platforms and tools. This program could be replicated in informal settings by any educator or collaborator with a computer science background. This paper describes our summer camp curriculum, its components and their implementation, the lessons learned, and potential future enhancements.
Elham Buxton, Elahe Javadi, Matthew Hagaman
AAAI3