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

Scheidt, Matthew; Godwin, Allison; Berger, Edward J.; Chen, John; Self, Brian P.; Widmann, James M.; Gates, Ann Q.

doi:10.1002/jee.20386

Cited by 15 publications

(9 citation statements)

References 152 publications

(202 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Major Findingsmentioning

confidence: 61%

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

Swenson,

Treadway,

Beranger

2024

J of Engineering Edu

View full text Add to dashboard Cite

BackgroundReal‐world engineering problems are ill‐defined and complex, and solving them may arouse negative epistemic affect (feelings experienced within problem‐solving). These feelings fall into sequenced patterns (affective pathways). Over time, these patterns can alter students' attitudes toward engineering. Meta‐affect (affect or cognition about affect) can shape or reframe affective pathways, changing a student's problem‐solving experience.Purpose/Hypothesis(es)This paper examines epistemic affect and meta‐affect in undergraduate students solving ill‐defined problems called open‐ended modeling problems (OEMPs), addressing two research questions: What epistemic affect and transitions between different affective states do students report? And, how does meta‐affect shape students' affective experiences?Design/MethodWe examined 11 retrospective interviews with nine students performed across two semesters in which students completed OEMPs. Using inductive and deductive coding with discourse analysis, we systematically searched for expressions conveying epistemic affect and for transitions in affect; we performed additional deductive coding of the transcripts for meta‐affect and synthesized these results to formulate narratives related to affect and meta‐affect.ResultsTogether, the expressions, transitions, and meta‐affect suggest different types of student experiences. Depending on their meta‐affect, students either recounted experiences dominated by positive or negative affect, or else they experienced negative emotions as productive.ConclusionsIll‐defined complex problems elicit a wide range of positive and negative emotions and provide opportunities to practice affective regulation and productive meta‐affect. Viewing the OEMPs as authentic disciplinary experiences and/or the ability to view negative emotions as productive can enable overall positive experiences. Our results provide insight into how instructors can foster positive affective pathways through problem‐scaffolding or their interactions with students.

show abstract

Section: Major Findingsmentioning

confidence: 61%

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

Swenson,

Treadway,

Beranger

2024

J of Engineering Edu

View full text Add to dashboard Cite

show abstract

“…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:…”

Section: Purpose Of the Studymentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

Current engineering education should develop the noncognitive abilities of students. Psychological capital (PsyCap) is defined as an individual's strengths of self-efficacy, optimism, hope, and resiliency. PsyCap is an essential noncognitive ability that influences student mental health and academic performance, yet limited research has been done to identify predictors of PsyCap. These predictors might inform the key to developing PsyCap. This study examined the levels of PsyCap and emotional intelligence (EI) among final-year chemical engineering students and investigated the effects of EI and demographic characteristics on PsyCap among them. This quantitative study involved 99 final-year chemical engineering students from a publicly funded university in Malaysia. The Trait Emotional Intelligence Questionnaire�Short Form (TEIQue-SF) and the Psychological Capital Questionnaire (PCQ) were used, and respondents' demographic characteristics (gender, age, ethnicities, household income, and preuniversity education) were surveyed. Multiple linear regression was used to predict the effects of EI and demographic variables on PsyCap. The five domains of EI (well-being, self-control, emotionality, sociability, and global trait) ranged from 4.36/7.00 to 5.01/7.00, and the four domains of PsyCap (self-efficacy, optimism, hope, and resiliency) ranged from 4.19/6.00 to 4.45/6.00. Next, EI significantly predicted PsyCap while demographic characteristics did not. The study found that the PsyCap of the final-year chemical engineering students was at a moderate level. Thus, specific interventions based on EI are beneficial to developing PsyCap in coping with challenges in their engineering studies and future careers.

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

Adaptive density peak clustering algorithm combined with sparse search

Duan

Wei

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the advantages of few parameters and the ability to deal with clusters of arbitrary shape, the density peak clustering algorithm has attracted wide attention since it came out. However, the algorithm has problems such as high time complexity, poor clustering effect on complex data sets, and the need to manually select cluster centers. Aiming at the above shortcomings, an improved density peak clustering algorithm is proposed. Combined with the sparse search algorithm, the calculation of the similarity between each point and its nearest neighbor is simplified, and the problem of the high time complexity of the algorithm is overcome. A new local density definition method is adopted to make the density of data points better reflect the spatial structure of data distribution and to improve the clustering accuracy of the algorithm. Finally, a strategy for automatically selecting cluster centers is proposed to improve the adaptability of the algorithm. The algorithm is used to compare with the other improved algorithm on artificial data sets and real data sets. The experimental results show that the proposed algorithm can show a better clustering effect and can quickly and accurately identify various complex clusters.

show abstract

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

Cited by 15 publications

References 152 publications

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

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

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

Adaptive density peak clustering algorithm combined with sparse search

Contact Info

Product

Resources

About