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Engineering students' noncognitive and affective factors: Group differences from cluster analysis
Author(s) -
Scheidt Matthew,
Godwin Allison,
Berger Edward,
Chen John,
Self Brian P.,
Widmann James M.,
Gates Ann Q.
Publication year - 2021
Publication title -
journal of engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.896
H-Index - 108
eISSN - 2168-9830
pISSN - 1069-4730
DOI - 10.1002/jee.20386
Subject(s) - mindset , psychology , set (abstract data type) , diversity (politics) , cluster (spacecraft) , cluster analysis , engineering education , function (biology) , mathematics education , social psychology , computer science , engineering , artificial intelligence , sociology , mechanical engineering , evolutionary biology , anthropology , biology , programming language
Background Noncognitive and affective (NCA) factors (e.g., belonging, engineering identity, motivation, mindset, personality, etc.) are important to undergraduate student success. However, few studies have considered how these factors coexist and act in concert. Purpose/Hypothesis We hypothesize that students cluster into several distinct collections of NCA factors and that identifying and considering the factors together may inform student support programs and engineering education. Design/Method We measured 28 NCA factors using a survey instrument with strong validity evidence. We gathered responses from 2339 engineering undergraduates at 17 U.S. institutions and used Gaussian mixture modeling (GMM) to group respondents into clusters. Results We found four distinct profiles of students in our data and a set of unclustered students with the NCA factor patterns varying substantially by cluster. Correlations of cluster membership to self‐reported incoming academic performance measures were not strong, suggesting that students' NCA factors rather than traditionally used cognitive measures may better distinguish among students in engineering programs. Conclusions GMM is a powerful technique for person‐centered clustering of high‐dimensional datasets. The four distinct clusters of students discovered in this research illustrate the diversity of engineering students' NCA profiles. The NCA factor patterns within the clusters provide new insights on how these factors may function together and provide opportunities to intervene on multiple factors simultaneously, potentially resulting in more comprehensive and effective interventions. This research leads to future work on both student success modeling and student affairs–academic partnerships to understand and promote holistic student success.