Motivation Profiles of Engineering Doctoral Students and Implications for Persistence

2019 IEEE Frontiers in Education Conference (FIE) Pub Date : 2019-10-01 DOI:10.1109/FIE43999.2019.9028565

H. Perkins, Marissa A. Tsugawa-Nieves, M. Bahnson, D. Satterfield, Mackenzie Parker, Adam Kirn, C. Cass

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Abstract

The purpose of this full-length research paper is to explore the motivation profiles of engineering doctoral students (EDS) and their effects on student persistence. A Latent Profile Analysis (LPA) identified five profiles across four constructs from the Future Time Perspective (FTP) framework, with three straightforward profiles (Low, Average, and High) and two mixed profiles (Low Connectedness and Low Multiplicity). Two between-subjects ANCOVAs were run to test for differences in difficulty ascertaining degree progress (DADP) and intentions to persist (IP). DADP differed significantly by profile assignment, F(4,1137) = 21.38, p <.001, Partial-eta squared =.07, as did IP, F(4,1136) = 12.26, p <.001, Partial-eta squared =.04. This indicates that there are distinct motivation profiles among EDS with implications for student progress and persistence. Differences between the five profiles and their effect on DADP and IP will be discussed in further detail, along with recommendations for faculty and advisors.

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工科博士生的动机概况及其对坚持的启示

本研究旨在探讨工科博士生的学习动机及其对学生坚持学习的影响。潜在特征分析(LPA)从未来时间视角(FTP)框架中确定了四种结构中的五种特征，其中三种直接特征(低、平均和高)和两种混合特征(低连通性和低多样性)。使用两个受试者间ANCOVAs来测试确定程度进展(DADP)和坚持意图(IP)的难度差异。不同剖面分配的DADP差异显著，F(4,1137) = 21.38, p <。001，偏平方=。07, IP也一样，F(4,1136) = 12.26, p <。001，偏平方=。04。这表明在EDS学生中存在不同的动机特征，对学生的进步和坚持有影响。五种概况之间的差异及其对DADP和IP的影响将进一步详细讨论，以及对教师和顾问的建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE Frontiers in Education Conference (FIE)

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