Background and Context: The study was conducted in a special
education classroom in an elementary school with multilingual
Latine students, utilizing a computer science curriculum focused
on community-based environmental literacy.
Objective: This study explores the experiences of diverse elementary
students with disabilities in learning computer programming
and identifies instructional strategies that enhance their learning
within a culturally sustaining curriculum.
Method: An exploratory case study approach was used to examine
students’ learning experiences and teachers’ instructional strategies
during curriculum implementation.
Findings: Students who typically did not engage with peers collaborated
effectively, and those with behavioral and performance
difficulties exhibited heightened engagement. Instructional strategies
included multisensory engagement and connecting environmental
and computational concepts to real-life situations.
Implications: The result underscore how a culturally sustaining
computer science curriculum can empower diverse students, foster
inclusivity, and leverage their strengths through effective teaching
practices.
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Is Elementary AI Education Possible?
As artificial intelligence (AI) technology becomes increasingly pervasive, it is critical that students recognize AI and how it can be used. There is little research exploring learning capabilities of elementary students and the pedagogical supports necessary to facilitate students’ learning. PrimaryAI was created as a 3rd-5th grade AI curriculum that utilizes problem-based and immersive learning within an authentic life science context through four units that cover machine learning, computer vision, AI planning, and AI ethics. The curriculum was implemented by two upper elementary teachers during Spring 2022. Based on pre-test/post-test results, students were able to conceptualize AI concepts related to machine learning and computer vision. Results showed no significant differences based on gender. Teachers indicated the curriculum engaged students and provided teachers with sufficient scaffolding to teach the content in their classrooms. Recommendations for future implementations include greater alignment between the AI and life science concepts, alterations to the immersive problem-based learning environment, and enhanced connections to local animal populations.
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- Award ID(s):
- 1934153
- PAR ID:
- 10447598
- Date Published:
- Journal Name:
- Proceedings of the 54th ACM Technical Symposium on Computer Science Education
- Page Range / eLocation ID:
- 1364 to 1364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3545947.3576308
