Collaborative Research: Understanding and Supporting Student Intrinsic Motivation in STEM Courses

在动机方面的先前工作的基础上，该项目正在收集和分析定量和定性数据，以提高描述和解释工程教育所需的各种本科课程中学生动机的关键特征的能力。本项目旨在通过教师对学生学习动机数据的解读，将这些研究数据与动机理论和课程设计相结合，并对课程进行修订，以增强STEM学生的内在动力。智力优势本研究基于以往的研究，即教师可以通过课程设计决策直接影响学生的学习动机，特别是内在动机。 为了充分利用这种关系的潜力，教师既需要对学生学习动机的类型有更细致的了解，也需要获得更清晰的方法，将理论和经验数据转化为课程水平的见解。 这个项目是衡量学生个人对不同的STEM环境，教学和作业的反应。 这些反应的时间演变是STEM学生动机驱动力的可转移研究和可推广理论发展的焦点。以更细致的方式分析动机数据，按课程活动，学习年份和性别检查动机的一般趋势。 动机是动态的，容易受到频繁的、有时是快速的变化的影响。 该分析使用基于组的聚类技术，以发现不同类型的动机反应的强度，持久性和分布。 本研究采用质性分析的方法来解释动机与学习环境的关系，并阐明动机的性别差异。在多课程分析中使用基于变量和聚类的分析，有助于更好地理解教学设计对有效实践的影响。 更广泛的影响美国国家工程院鼓励我们为STEM毕业生准备“世界所需的工具，因为它将是，而不是像今天这样。“这些工具包括创造力、批判性思维、弹性、灵活性和自我调节。教育研究表明，提高对学习者动机的理解对于促进向这些高水平成果的系统转变非常重要。然而，在基于研究的对学生动机的理解与将这些研究见解应用于日常课堂实践之间仍然存在很大的差距。该项目的产出将对8个参与机构的20多名STEM教师产生直接影响，突出提示不同动机反应和动机转变的活动，解释动机-环境相互作用，并使教师能够使用研究数据做出明智的战略选择，以更好地鼓励自主行为。该项目扩大了STEM教师的人才库，他们可以通过吸引早期职业教师和那些先前参与STEM教育改革有限的教师来做出明智的、数据驱动的决策。

Building on prior work in motivation, this project is collecting and analyzing quantitative and qualitative data to improve the capability to characterize and explain key characteristics of student motivation in diverse undergraduate courses required for engineering education. This project is engaging instructors in the process of interpreting student motivation data, coupling these research data to motivation theory and course design, and developing course revisions aimed at enhancing STEM students' intrinsic drive.Intellectual MeritThis research rests on prior research that shows that instructors can directly influence student motivation, particularly intrinsic motivation, through their course design decisions. To capitalize on the potential of this relationship, instructors need both a more nuanced understanding of the types of student motivations for learning and access to clearer methods for translating theory and empirical data to course-level insights. This project is measuring individual student responses to diverse STEM environments, pedagogies, and assignments. The temporal evolution of these responses is a focal point in the development of transferable research and generalizable theories for STEM student motivational drive.The analysis of motivation data in more nuanced ways examines general trends in motivation by course activity, year of study, and gender. Motivation is dynamic and susceptible to frequent and sometimes rapid change. The analysis uses group-based clustering techniques to discover the strength, persistence, and distribution of different types of motivational responses. It employs qualitative analyses to explain the relationships between motivation and the learning environment and elucidate gendered differences in motivation. Using both variable- and cluster-based analyses in multiple course analyses has promise in developing better understanding of the impact of instructional design on effective practice. BROADER IMPACTSThe National Academy of Engineering exhorts us to prepare STEM graduates with "the tools needed for the world as it will be, not as it is today." Among these tools are creativity, critical thinking, resiliency, flexibility, and self-regulation. Educational research suggests that improved understanding of learner motivation is important to facilitate a systemic shift toward these high-level outcomes. However, a large gap remains between the research-based understanding of student motivation, and the application of those research insights to day-to-day classroom practice. The output from this project will have an immediate impact on over 20 STEM instructors at the 8 participating institutions, by highlighting activities that prompt different motivational responses and motivational shifts, explaining motivation-environment interactions, and by enabling instructors to use research data to make informed and strategic choices to better encourage self-determined behaviors. The project expands the pool of STEM faculty who can make informed, data-driven decisions by engaging early-career faculty and those with limited prior involvement in STEM educational reform.