Characterizing active learning environments in physics using latent profile analysis

TL;DR

This study uses latent profile analysis on COPUS observations to classify and differentiate various active learning styles in physics education, moving beyond passive lecture comparisons.

Contribution

It introduces a novel application of latent profile analysis to categorize active learning environments in physics based on observational data.

Findings

Two profiles distinguish interactive lecture-like environments from others.

Five profiles successfully classify specific active learning styles.

The method provides a new way to analyze and compare pedagogical approaches.

Abstract

The vast majority of research involving active learning pedagogies uses passive lecture methods as a baseline. We propose to move beyond such comparisons to understand the mechanisms that make different active learning styles unique. Here, we use COPUS observations to record student and instructor activities in six known styles of active learning in physics, and use latent profile analysis to classify these observations. Latent profile analysis using two profiles successfully groups COPUS profiles into interactive lecture-like and other. Five latent profiles successfully sorts observations into interactive lecture-like, Modeling Instruction, ISLE labs, Context-Rich problems labs, and recitation/discussion-like. This analysis serves as a proof of concept, and suggests instructional differences across pedagogies that can be further investigated using this method.