Characterizing Active Learning Environments in Physics

Kelley Anne Commeford

doi:10.17918/00000560

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. This study uses social network analysis and the Classroom Observation Protocol for Undergraduate STEM (COPUS) to characterize six research-based introductory physics curricula. Peer Instruction, Modeling Instruction, ISLE, SCALE-UP, Context-Rich Problems (Minnesota model), and Tutorials in Introductory Physics were investigated. Students in each curriculum were given a survey at the beginning and end of term, asking them to self-identify peers with whom they had meaningful interactions in class. These surveys were turned into classroom social networks. Additionally, we use COPUS to perform live observations of each pedagogy for one week in the middle of the term. Within the network data, every curriculum showed an increase in the average number of student connections from the beginning of the term to the end of term, with the largest increase occurring in Modeling Instruction, SCALE-UP, and Context-Rich Problems. Modeling Instruction was the only curriculum with a significant change in how tightly connected the student network was. Transitivity increased for all curricula except Peer Instruction. From the COPUS observations, the student code profiles look nearly the same for Tutorials, ISLE recitations, and Context-Rich Problems discussion sections. This is likely due to the large resolution of activities that can be coded as "other group activity", suggesting the need for a more detailed observation instrument. When we investigated further using latent profile analysis (LPA), we were able to successfully group COPUS profiles into two and five hidden profiles. With two hidden groups, LPA sorted the observations into interactive lecture-like and other. Five latent profiles successfully sorted observations into interactive lecture-like, Modeling Instruction, ISLE labs, Context-Rich problems labs, and recitation/discussion-like. Latent profile analysis shows great promise for larger studies. We also began an exploratory analysis using exponential random graph models (ERGMs) with the Peer Instruction networks. Exponential random graph models are a family of statistical models used with relational data, like social networks. We discuss preliminary findings using this method for a Peer Instruction class. The best-fit ERGM predicts long "chains" of student edges, such as might arise from students talking along rows in the lecture hall. ERGMs appear to be a promising method for quantifying network topology in active learning classrooms and can be combined with external variables such as COPUS profiles.

Characterizing Active Learning Environments in Physics

Files and links (1)

Abstract

Metrics

Details

Characterizing Active Learning Environments in Physics

Files and links (1)

Abstract

Metrics

Details

Drexel University Social media