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AI Teacher Data Preparation for Analyzing Difficult-to-Predict Behavior of Young Children: Development of practical learning materials for accident prevention and safety measures in early childhood education
PROCEEDING

, , , Shoin University, Japan ; , Iwate University, Japan

Innovate Learning Summit, in Online, United States ISBN 978-1-939797-59-9 Publisher: Association for the Advancement of Computing in Education (AACE)

Abstract

Utilizing AI in a series of studies, this research seeks to devise a solution. To shed light on this, the data collected from “watching scenarios” are divided and organized. A childcare worker education support system was set up to evaluate childcare workers’ “skilled knowledge” awareness of the risk of accidents while viewing video scenarios. Daycare training course instructors extracted both every day and risky scenarios of young children playing in water and confirmed the effectiveness of the evaluation. As a results, research will compare the similarities between the scenarios extracted by AI and those extracted by experienced childcare workers and teaching staff, and the teaching materials will be evaluated. Experiments will be used to assess the extent to which the accuracy of the AI can be improved.

Citation

Tachino, T., Osawa, H., Nozue, A. & Aoyama, K. (2021). AI Teacher Data Preparation for Analyzing Difficult-to-Predict Behavior of Young Children: Development of practical learning materials for accident prevention and safety measures in early childhood education. In T. Bastiaens (Ed.), Proceedings of Innovate Learning Summit 2021 (pp. 559-563). Online, United States: Association for the Advancement of Computing in Education (AACE). Retrieved November 29, 2021 from .