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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。这个博士后奖学金研究项目将专注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和调节学习。本研究旨在探索学生的学习动机与自主学习的互动方式。预计初步发现将为创建旨在改善学习模式的即时学习干预提供参考。该项目还旨在通过加强学生的学习和积极性，提高妇女和有色人种学生在计算机科学领域的参与度，从而提高该领域的坚持率和学位获得率。学生的学习是一个动态的、特定于环境的过程。目前的干预措施旨在促进STEM的坚持性和表现，捕捉到学生在广泛的背景下学习，而不是在学生参与课程学习的那一刻。该项目旨在利用包括数字痕迹数据在内的多种数据源，更准确、更全面地了解本科生在计算机科学方面的学习情况。这些数据有可能捕捉到学生学习和动机的动态本质。这位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”(Efklides，2011)的框架内。然后，这位研究员将探索任务和个人层面上的动机、行为和学习之间的联系，并为这一基础理论提供新的见解，该基础理论考虑了动机和自我调节学习之间的相互作用。此外，该项目将使用A/B方法来测试作为该项目的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。对动机和自我调节学习信息的A/B测试可能会让我们对结构和表现之间的联系做出更有力的断言。将这些方法结合在同一个项目中，对于计算机科学本科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，该计划旨在提高STEM、STEM教育、教育和相关学科的新近博士的研究知识、技能和实践，以促进他们从事基础和应用研究，促进该领域知识的进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.