Integrating Artificial Intelligence Into a Digital Platform: A Machine Learning Rubric for Students’ Proportional Reasoning (Poster 48)

• In Event: AERA Poster Session Sunday 1:30 p.m.
  In Poster Session: Machine Learning Approaches to Enhancing Learning Environments With Artificial Intelligence

Abstract

This paper presents a novel approach to integrating artificial intelligence into a digital collaborative platform for a problem-based mathematics curriculum. Using supervised machine learning techniques, we explore how computers can learn about middle school students’ proportional reasoning. Specifically, we focus on a rubric that can be used to train computers to diagnose and track evidence of students’ proportional reasoning over time. We report on an artificial intelligence application in mathematics education, the development of a machine learning rubric, and its focus on three aspects: (1) students’ proportional reasoning approach, (2) mathematical representations used, and (3) solution strategies employed. Finally, we discuss our next steps and the significance of our work in improving mathematics teaching and learning.

Authors

• Alden Jack Edson, Michigan State University

• Taren Going, Michigan State University

• Sunyoung Park, Michigan State University

• Leslie Bondaryk, The Concord Consortium