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The students are unstable! Cluster analysis of motivation and early implications for educational research and practice

机译:学生们不稳定!动机和对教育研究与实践的早期启示的聚类分析

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The type of motivation that a student evinces can shape such outcomes as academic engagement, performance, and satisfaction. In this paper we explore student motivation using cluster analysis, a quantitative method that has been proven useful in examining group-based motivational profiles within academic settings. We apply a range of clustering techniques to a data set comprising motivational survey responses of undergraduate engineering students at three different colleges. We confirm the outcomes of previous clustering analyses, revealing the existence of different student motivational profiles in academic settings, and we offer several new perspectives on engineering student motivation. First, different cluster analysis methods describe similar trends in motivational profiles, but divergent judgements in the distribution of students into these profiles. Second, examination of longitudinal data illustrates that students' endorsements of the various motivations fluctuate significantly, and sometimes dramatically, within the duration of a single course. This challenges the "they either have it or they don't" concept of motivation, and highlights the importance of understanding of students' situational motivational responses to classroom activities.
机译:学生所表现出的动机可以影响诸如学习投入,表现和满意度之类的结果。在本文中,我们使用聚类分析来探索学生的动机,这是一种定量方法,已被证明可用于检查学术环境中基于小组的动机概况。我们将一系列聚类技术应用于数据集,该数据集包含来自三所不同学院的工程学专业学生的动机调查反馈。我们确认了先前的聚类分析的结果,揭示了学术环境中不同学生动机的存在,并且我们提供了关于工程学生动机的一些新观点。首先,不同的聚类分析方法描述了动机配置文件中的相似趋势,但是在将学生分配到这些配置文件中时存在不同的判断。其次,对纵向数据的检查表明,学生对各种动机的认可在单门课程的持续时间内会出现很大的波动,有时甚至是剧烈的波动。这挑战了“有或没有”的动机概念,并突出了理解学生对课堂活动的情境动机反应的重要性。

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