Integrated Learning of Machine Learning and Chemistry: Fostering High School Students' Science Motivation and Interdisciplinary Understanding

• In Event: TIL- Technology to Support Learning

Short Description

This mixed method case study explores high school students’ science motivation and interdisciplinary understanding after they experienced Machine Learning activities in the context of chemistry class for five days (90 minutes per day). The findings demonstrated that students’ science motivation increased significantly after the curriculum intervention. Additionally, this study showed that the interdisciplinary curriculum approach allowed students to see the value of ML practices in science contexts and consider them holistically for solving real-world problems.

Authors

• Contributor: Jeanne McClure, North Carolina State University

• Contributor: Cansu Tatar, North Carolina State University

• Contributor: shiyan jiang, North Carolina State University

• Contributor: Yang Zhang, North Carolina State University