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Title: Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum
Authors: Tatar, Cansu
Yoder, Michael Miller
Coven, Madeline
Wiedemann, Kenia
Chao, Jie
Finzer, William
Jiang, Shiyan
Rosé, Carolyn P.
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Tatar, C., Yoder, M. M., Coven, M., Wiedemann, K., Chao, J., Finzer, W., Jiang, S., & Rosé, C. P. (2021). Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.
Abstract: In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies.
Appears in Collections:ISLS Annual Meeting 2021

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