Exploring a Structured Summer Camp Curriculum to Foster Middle School Students' Understanding of Machine Learning

• In Event: Computer Science and Computational Thinking

Abstract

This study explores the structure of a summer camp curriculum and the development of middle school students' understanding of machine learning (ML). By analyzing over 30 hours of video footage and students' artifacts, the integration of the curriculum and the development of students' ML understanding were examined using thematic analysis. The findings revealed that the ML curriculum consisted of an introductory module, creation of ML-based artifacts, and a mini project addressing community issues. Moreover, students' ML understanding encompassed recognizing the importance of data quality and quantity, differentiating ML from conventional programming, and understanding prediction and classification as ML outcomes. These findings contribute valuable insights to the ongoing discourse on AI education for teachers and educators.

Authors

• Keunjae Kim, Oklahoma State University

• Haesol Bae, University at Albany - SUNY

• Matthew Brown, Indiana University

• Kyungbin Kwon, Indiana University

• Krista D. Glazewski, North Carolina State University

• Anne Leftwich, Indiana University