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。这些知识可以改善教学技术，培养多元化和包容性的学生群体，并提高我们国家的数字化水平。犹他州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完成Python编程作业时的学习模式。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控制练习，该项目旨在制定有针对性的干预策略，帮助陷入困境的学生取得成功，同时培养教育研究技能。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，为国家繁荣和福利做出贡献。该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弹性。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绝佳的机会。基于计算机的学习的激增进一步加速了从学生那里收集丰富的颗粒数据。在这个项目中，这些进步被用来进行一个试点项目，重点是加强入门计算机编程的教学。该项目的第一阶段旨在专注于构建一个学生数据集，该数据集将封装学生的编程知识、人口统计学和学术特征以及受控练习。将实施两种创新技术，通过计算思维和认知过程的双镜头来检查学生的编码行为。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行为。将在教员和从业人员中传播这一试点项目的见解，使他们能够制定有效的干预战略。拟议的研究活动是精心设计的，以加强PI的能力。他们将装备PI广泛的知识，技能和专业知识，促进在这个充满活力的领域的专业发展。这个项目是通过与比尔梅林达盖茨基金会，施密特期货，沃尔顿家族基金会的合作伙伴关系支持。该项目也得到了NSF的EDU核心研究STEM教育研究能力建设的支持（ECR：BCSER）计划，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，该奖项反映了NSF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。