2026 ASEE Annual Conference & Exposition

Background: This work-in-progress paper is a theory-oriented review of the use of person-centered quantitative analysis in STEM education research. Despite ongoing reform efforts, equity remains a central challenge in STEM education, where socioeconomic stratification, structural racism, and gender inequities persist. Traditional variable-centered quantitative methods are effective for identifying average trends but may obscure within-group heterogeneity and reinforce dominant-group norms. Person-centered quantitative analysis offers an alternative by foregrounding individual configurations and subgroup dynamics.
Purpose: The purpose of this review is to synthesize how person-centered quantitative analysis has been applied in STEM education research and how they complement or extend traditional variable-centered analyses. The guiding research question is: In STEM education research, what research questions are addressed using person-centered quantitative analysis, what models are employed, and what key conclusions are drawn?
Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted using Scopus as the primary database. The initial search, using education-focused keywords and exclusion criteria, yielded 156 articles. After applying additional filters, including publication year (2014–2024), language, article type, and relevance to quantitative, empirical STEM education research focused on students, 13 studies were retained for final review.
Results: The reviewed studies primarily used person-centered approaches to identify learner profiles related to motivation, emotion, cognition, and identity, and to examine their associations with engagement, achievement, and career-related outcomes. Latent Profile Analysis and Latent Class Analysis were the most frequently employed models, often integrated with regression, longitudinal designs, or mixed methods. Findings consistently revealed subgroup-specific patterns, including gendered and cultural differences, as well as dynamic transitions in learner profiles over time.
Future Work: The next step of this study is to expand the literature corpus by incorporating additional databases and refining search strategies to capture a broader range of education-focused publications. We will also attend to emerging computational methods that enable more fine-grained analyses, examining how these developments may shape the operationalization of person-centered quantitative analysis.

Keywords: Engineering Education, STEM Education, Quantitative Methods, Person-Centered Analysis, Latent Profile Analysis, Equity in STEM