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Abstract Title: Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics
Abstract Type: Contributed Poster Presentation
Abstract: Research characterizing common student ideas about particular physics topics has significantly impacted university-level physics teaching by providing knowledge that supports instructors to target their instruction and by informing curriculum development. In this work, we utilize a Natural Language Processing algorithm (Latent Dirichlet Allocation, or LDA) to identify distinct student ideas in a set of written responses to a conceptual physics question, with the goal of significantly expediting the process of characterizing student ideas. We preliminarily test the LDA approach by applying the algorithm to a collection of introductory physics student responses to a conceptual question about circuits, specifically attending to whether it is useful for characterizing instructionally-relevant student ideas. We find that for a large enough collection of student responses (N ≈ 500), LDA can be useful for characterizing the ideas students used to answer conceptual physics questions. We discuss some considerations that researchers may take into account as they interpret the results of the LDA algorithm for characterizing student's physics ideas.
Session Time: Poster Session 2
Poster Number: II-14
Contributed Paper Record: Contributed Paper Information
Contributed Paper Download: Download Contributed Paper

Author/Organizer Information

Primary Contact: Jon M. Geiger
Seattle Pacific University
Seattle, WA 98119
Phone: 7146556197
and Co-Presenter(s)
Lisa M. Goodhew (she/her), Seattle Pacific University
Tor Ole B. Odden (he/him), University of Oslo