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PERC 2022 Abstract Detail Page

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Abstract Title: Content Analysis at scale: using NLP and neural networks to analyze large quantities of student writing about their approach to experimental physics
Abstract Type: Symposium Talk
Abstract: Content Analysis is crucial in PER for understanding and measuring student thinking and behavior. This method is very time-consuming, however, not only in the development of coding schemes but also in the continued application of these coding schemes to growing data sets. Methods from Natural Language Processing (NLP) in conjunction with neural networks allow us to automate the process of applying certain coding schemes to incoming data. I will discuss how we have used these techniques to analyze student writing for the purpose of understanding the evolution of students' approaches to experimental physics over the course of a semester of lab instruction. Furthermore, I will explore how much data is necessary to make use of these tools and what features of coding schemes best lend themselves to automation with machine learning.
Session Time: Parallel Sessions Cluster III
Room: Vandenberg A
Parallel Session: Machine learning methods in PER: Intuition and methodological discussion

Author/Organizer Information

Primary Contact: Rebeckah Fussell
Cornell University