Please use this identifier to cite or link to this item:
|Title:||Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum|
Yoder, Michael Miller
Rosé, Carolyn P.
|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|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.