Healthcare Data Science, Artificial Intelligence, and Machine Learning: Exploring Context-Based Learning for High School Students (Poster 46)

• 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 reports a study of how context-based learning can support engagement and learning about data science, artificial intelligence (AI), and machine learning (ML) among high school students with different interests and needs. Data from interviews with students and teachers about their perspectives were collected and analyzed after they had enacted a healthcare context-based curriculum consisting of in-class activities and out-of-class datathons. Findings reveal how curriculum can incorporate key features of context-based learning (setting, behavioral environment, and specific disciplinary language) to help students with varied academic and vocational aspirations and background knowledge understand key concepts and practices, the impact of these fields, and their relevance to personally and socially meaningful domains like healthcare.

Authors

• Anushree D. Bopardikar, Technical Education Research Centers

• Michael Patrick Cassidy, TERC

• Angela Gardiner, TERC

• Elizabeth Price, Aecern

• Luk Hendrik, Aecern

• Brandy Jackson, Aecern

• Leo Anthony Celi, Massachusetts Institute of Technology

• Kathryn Jessen Eller, EBEC