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

机译：工程学生的非认知和情感因素：与集群分析的差异

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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 2,339 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.
机译：背景技术非认知和情感（NCA）因素（例如，属于，工程身份，动机，心态，人格等）对本科生成功非常重要。然而，很少有研究考虑过这些因素如何共存和行动.Purpose /假设我们假设学生聚集成几个不同的NCA因素集合，并将其识别和考虑在一起的因素可以告知学生支持计划和工程教育.Design/Method我们使用具有强大有效证据的调查仪器测量了28个NCA因素。我们收集了17个美国机构的2,339个工程大学生的回复，并使用高斯混合模拟（GMM）将受访者分组为集群。我们发现我们的数据中的四个不同的学生曲线以及一系列未受欢迎的学生，NCA因子模式大大变化簇。集群成员资格与自我报告的入境学术绩效措施的相关性并不强劲，表明学生的NCA因素而不是传统上使用的认知措施可能会更好地区分工程计划中的学生。GMM是一种强大的技术，用于高的人为中心聚类的强大技术 - 二维数据集。本研究中发现的四个不同学生的群集阐述了工程学生的NCA配置文件的多样性。集群内的NCA因子模式提供了关于这些因素如何运作的新见解，并在同时介入多因素的机会，可能导致更全面且有效的干预措施。这项研究导致了未来的学生成功建模和学生事务学术伙伴关系，以了解和促进整体学生的成功。

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