STEM Ed PRF: Understanding and Improving Undergraduate Computer Science Regulation, Performance, and Motivation Using Digital Traces and Technologies

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。这个博士后研究项目将侧重于利用教育数据挖掘来了解计算机科学入门课程的本科生如何利用他们的动机资源并调节学习。本研究旨在探讨学生动机与自我调节学习的互动方式。初步的研究结果有望为及时学习干预的创建提供信息，旨在改善学习模式。该项目还旨在通过提高学生的学习和动机来增加女性和有色人种学生在计算机科学领域的参与度，这可能会导致该领域更大的坚持率和学位获得率。学生的学习是一个动态的、特定情境的过程。目前旨在促进STEM坚持和表现的干预措施是在广泛的背景下捕捉学生的学习，而不是在学生参与课程作业的那一刻。本项目旨在通过多种数据来源，包括数字跟踪数据，更准确、全面地了解大学生在计算机科学专业的学习情况。这些数据有可能捕捉到学生学习和动机的动态本质。博士后选择将她的工作定位在“自我调节学习的元认知和情感模型”的框架内（Efklides, 2011）。然后，该研究员将在任务和个人层面探索动机、行为和学习之间的联系，并为考虑动机和自我调节学习之间的相互作用的基础理论提供新的见解。此外，本项目将使用A/B方法来测试作为项目一部分创建的干预措施，该干预措施侧重于及时、自我调节的学习支持，以改善学习模式。动机和自我调节学习信息的A/B测试可能会对构式和表现之间的联系提出更强有力的主张。将这些方法结合在同一个项目中，对于计算机科学本科学生有效地使用学习分析具有变革性的潜力。该项目响应STEM教育博士后研究奖学金（STEM Ed PRF）计划，旨在提高STEM， STEM教育，教育和相关学科的近期博士的研究知识，技能和实践，以促进他们从事基础和应用研究的准备，从而推进该领域的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This postdoctoral fellowship research project will focus on leveraging educational data mining to understand how undergraduate students in introductory Computer Science courses draw on their motivational resources and regulate learning. The study seeks to explore ways in which students’ motivation interacts with self-regulated learning. Initial findings are then expected to inform the creation of a just-in-time learning intervention designed to improve study patterns. This project also aims to increase participation for women and students of color in Computer Science by enhancing student learning and motivation which may result in greater rates of persistence and degree attainment in the field. Student learning is a dynamic and context-specific process. Current interventions designed to promote persistence and performance in STEM capture student learning in broad contexts rather than in the moment students engage with their coursework. This project aims to gain a more accurate and comprehensive view of undergraduates’ learning in Computer Science using multiple data sources, including digital trace data. These data have the potential to capture the dynamic nature of student learning and motivation. The postdoctoral fellow has chosen to position her work within the framework of the “Metacognitive and Affective Model of Self-Regulated Learning” (Efklides, 2011). The fellow will then explore connections between motivation, behavior, and learning at both the task and person level and contribute new insights into that foundational theory that consider the interplay between motivation and self-regulated learning across levels of interaction. In addition, this project will use A/B methods to test an intervention created as part of the project that focuses on just-in-time, self-regulated learning support to improve study patterns. The A/B testing of motivation and self-regulated learning messages may allow for stronger claims to be made about the links between constructs and performance. Combining these methods within the same project has transformative potential for the effective use of learning analytics by Computer Science undergraduate students. The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.