Learning Analytics for Process-driven Computer Programming Assignments

流程驱动的计算机编程作业的学习分析

• 批准号: 2321304
• 负责人: Hamid Karimi
• 金额: $ 34.98万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321304&HistoricalAwards=false
• 关键词: Learning; Analytics; Process; driven; Computer

To develop student skills in computer programming, it is essential to understand how students approach computer programming assignments. This knowledge can improve teaching techniques, foster a diverse and inclusive student body, and enhance our Nation's digital proficiency. This project at Utah State University seeks to investigate students' keystroke patterns as they work on Python programming assignments in introductory computer programming courses. This innovative approach provides educators with actionable insights that could improve computer science education. By analyzing academic and demographic data, as well as controlled exercises, this project aims to develop targeted intervention strategies that help struggling students succeed, while also developing education research skills. The ultimate goal is to promote scientific advancement and strengthen the Nation's computer science expertise, contributing to national prosperity and welfare. This project has far-reaching implications, potentially impacting not just current students but also our Nation's digital capabilities and resilience in the years to come.The rapid advancements in educational technology have presented an exceptional opportunity to collect intricate details about students' learning processes across various activities. The surge in computer-based learning has further accelerated the collection of rich, granular data from students. In this project, these advancements are leveraged to conduct a pilot project focused on enhancing the pedagogy of introductory computer programming. The project's first phase seeks to focus on the construction of a student dataset that will encapsulate student programming keystrokes, demographic and academic characteristics, as well as controlled exercises. Two innovative techniques to examine student coding behavior through the dual lenses of computational thinking and cognitive processes will be implemented. This project aims to culminate in the development of an advanced and interpretative machine learning model that will be able to predict student outcomes and detect plagiarism. The insights from this pilot project will be disseminated among instructors and practitioners, empowering them to formulate effective intervention strategies. The proposed research activities are meticulously designed to bolster the PI's capacity. They will equip the PI with broad knowledge, skills, and expertise, fostering professional development in this dynamic field. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This project is also supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research in the core areas of STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development.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.

为了培养学生的计算机编程技能，了解学生如何处理计算机编程作业是至关重要的。这些知识可以提高教学技巧，培养多元化和包容性的学生群体，并提高我们国家的数字能力。犹他州立大学的这个项目旨在调查学生在计算机编程入门课程中处理Python编程作业时的击键模式。这种创新的方法为教育工作者提供了可操作的见解，可以改善计算机科学教育。通过分析学术和人口统计数据，以及对照练习，该项目旨在制定有针对性的干预策略，帮助困难的学生取得成功，同时也培养教育研究技能。最终目标是促进科学进步，加强国家的计算机科学专业知识，为国家的繁荣和福利做出贡献。这个项目具有深远的影响，不仅可能影响当前的学生，还可能影响我们国家未来几年的数字能力和应变能力。教育技术的快速发展为收集学生在各种活动中学习过程的复杂细节提供了难得的机会。计算机学习的激增进一步加速了从学生那里收集丰富而细致的数据。在这个项目中，利用这些进步来开展一个试点项目，重点是加强计算机编程入门教学。该项目的第一阶段旨在重点构建一个学生数据集，该数据集将封装学生编程击键、人口统计和学术特征，以及受控练习。通过计算思维和认知过程的双重视角来检验学生编码行为的两种创新技术将被实施。该项目旨在最终开发一种先进的解释性机器学习模型，该模型将能够预测学生的成绩并检测剽窃。这项试验计划的见解将会在教员和执业人员之间传播，使他们能够制订有效的干预策略。拟议的研究活动经过精心设计，以加强PI的能力。他们将为PI提供广泛的知识，技能和专业知识，促进这个充满活力的领域的专业发展。该项目得到了比尔和梅林达·盖茨基金会、施密特期货和沃尔顿家族基金会的合作支持。该项目还得到了美国国家科学基金会EDU STEM教育研究核心研究能力建设（ECR: BCSER）项目的支持，该项目旨在培养研究人员在STEM学习和学习环境的核心领域开展高质量STEM教育研究的能力，扩大STEM领域的参与，以及STEM劳动力的发展。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。