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Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence, a field of computer science, has with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore how to introduce AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios that enable students to gain experience with AI applied toward solving real-world life-science problems.
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“I Think We Should...”: Analyzing Elementary Students’ Collaborative Processes for Giving and Taking Suggestions
Collaboration plays an essential role in computer science. While there is growing recognition that learners of all ages can benefit from collaborative learning, little is known about how elementary age children engage in collaborative problem solving in computer science. This paper reports on the analysis of a dataset of elementary students collaborating on a programming project. We found that children tend to make several different types of suggestions. In turn, their partners address those suggestions in different ways such as by implementing them directly in code or by replying through dialogue. We observe that students regularly accept or reject suggestions without explanation or explicit acknowledgement and that it is often unclear whether they understand the substance of the suggestion. These behaviors may inhibit the development of a shared understanding between the partners and limit the value of the collaborative process. These results can inform instructional practice and the development of new adaptive tools that facilitate productive collaborative problem solving in computer science.
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- Award ID(s):
- 1721160
- PAR ID:
- 10062938
- Date Published:
- Journal Name:
- Proceedings of the Annual SIGCSE Conference on Innovation and Technology in Computer Science Education
- ISSN:
- 1931-0536
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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