Measuring Upper-Elementary Students' Understanding of AI Concepts: A Rasch Model Analysis (Poster 8)

• In Event: Thursday Poster Session 12:40 pm
  In Poster Session: Engineering and Computer Science Education Posters

Abstract

We sought to validate a computer vision sub-scale of an artificial intelligence achievement instrument used to assess students’ AI knowledge. We discuss the item creation and selection process of the final 7 items and present a hypothetical learning trajectory. We examine the data fit of the Rasch model, determine the computer vision abilities among a sample of 98 upper-elementary students through the test, and examine the presence of test items that functioned differently for gender and grade level of the students. Our study provides a reliable assessment instrument, which is a step in the direction to contribute to the establishment of learning trajectories and guiding standards for younger students in the AI for K-12 field.

Authors

• Srijita Chakraburty, Indiana University

• Jiyoung Kim, California State Polytechnic University - Pomona

• Krista D. Glazewski, North Carolina State University

• Cindy E. Hmelo-Silver, Indiana University

• Anne Leftwich, Indiana University

• Vanessa Johnson, Indiana University

• Bradford Mott, North Carolina State University

• James C. Lester, North Carolina State University