Engineering students' noncognitive and affective factors: Group differences from cluster analysis

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.

Engineering students' epistemic affect and meta‐affect in solving ill‐defined problems

“…In engineering, meta-affect in the form of regulation-for example, through mindfulness (Scheidt et al, 2021)-has been studied, but our results also illustrate other types of meta-affect experienced by engineering students. Seven of the 11 interviews explicitly had utterances that were coded as meta-affective, ranging from affective regulation of various types to reporting hypothetical emotions to productive meta-affect, where negative emotions were reframed or experienced positively.…”

Effect of Emotional Intelligence and Demographic Characteristics on Psychological Capital among Chemical Engineering Students

“…In conclusion, past studies might have postulated the effects of EI and demographic characteristics on PsyCap (Figure 1), but there are limited studies on the extension of PsyCap across engineering education. 43 Hence, the present study attempts to address this knowledge gap. Following were the research questions of this study:…”

Adaptive density peak clustering algorithm combined with sparse search

“…In recent years, many different clustering algorithms have been proposed [2] . As one of the key technologies to deal with big data, they have been more and more widely used in digital image processing [3] , computer science [4][5] , species category analysis [6][7] , and other fields.…”