Designing Interest-Driven Learning Environments to Teach Machine Learning to Adolescents

• In Event: AERA Roundtable Session Thursday 8:00 am Four Seasons Ballroom 1
  In Roundtable Session: Teaching STEM Using Technology (Table 9)

Abstract

There is a need for Artificial Intelligence (AI) curricula that are developmentally appropriate and appeal to students’ interests (Lee and Perret, 2022). Employing a Project-based Learning framework (Krajcik and Shin, 2014) we designed a unit on Machine Learning (ML) using Google Teachable Machine for early adolescents. The curriculum incorporates interest-driven practices that connect to students’ everyday life (e.g., sports, music). We implemented this in a pilot study with three participants and documented their learning progress. The findings suggest that the curriculum produces quality learning outcomes including improved knowledge about ML and the process of training ML models.

Authors

• Manav Sharma, University of Miami

• Ji Shen, University of Miami