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Modeling Student Pathways in a Physics Bachelor's Degree Program

Physical review. Physics education research, 2019-01, Vol.15 (1), p.010128, Article 010128 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;info:eu-repo/semantics/openAccess ;ISSN: 2469-9896 ;EISSN: 2469-9896 ;DOI: 10.1103/PhysRevPhysEducRes.15.010128

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  • Title:
    Modeling Student Pathways in a Physics Bachelor's Degree Program
  • Author: Aiken, John M ; Henderson, Rachel ; Caballero, Marcos D
  • Subjects: Bachelors Degrees ; College Science ; Colleges & universities ; Engineering Education ; Machine learning ; Majors (Students) ; Modelling ; Physics ; Prediction models ; Predictive Measurement ; Quality ; State Universities ; Statistical Analysis ; Student Records ; Students ; Undergraduate Students
  • Is Part Of: Physical review. Physics education research, 2019-01, Vol.15 (1), p.010128, Article 010128
  • Description: Physics education research (PER) has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University. Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students' declared gender and ethnicity play a much smaller role relative to the other features included in the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.
  • Publisher: College Park: American Physical Society
  • Language: English;Norwegian
  • Identifier: ISSN: 2469-9896
    EISSN: 2469-9896
    DOI: 10.1103/PhysRevPhysEducRes.15.010128
  • Source: Education Resources Information Center (ERIC)
    NORA Norwegian Open Research Archives
    ProQuest Central
    DOAJ Directory of Open Access Journals
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