Engineering Students' Noncognitive and Affective Factors: Group Differences from Cluster Analysis

Autor/inn/en	Scheidt, Matthew; Godwin, Allison; Berger, Edward; Chen, John; Self, Brian P.; Widmann, James M.; Gates, Ann Q.
Titel	Engineering Students' Noncognitive and Affective Factors: Group Differences from Cluster Analysis
Quelle	In: Journal of Engineering Education, 110 (2021) 2, S.343-370 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Scheidt, Matthew) ORCID (Godwin, Allison) ORCID (Berger, Edward)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1069-4730
DOI	10.1002/jee.20386
Schlagwörter	Engineering Education; Undergraduate Students; Student Characteristics; Differences; Profiles; Cluster Grouping; Psychological Characteristics + Suchen Sie Ihr Suchwort? Ingenieurausbildung; Unterscheiden; Charakterisierung; Profilanalyse; Eingruppierung
Abstract	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. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01