Improving the Quality of Teaching Assistant Feedback to Undergraduate Students in Introductory Computer Science Courses

提高本科生计算机科学入门课程助教反馈质量

• 批准号: 2044279
• 负责人: Amy Cook
• 金额: $ 29.93万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044279&HistoricalAwards=false
• 关键词: Improving; Quality; Teaching; Assistant; Feedback

This project aims to serve the national interest by investigating the integration of existing submission and feedback delivery systems with teaching assistant (TA) training. Specifically, this work will explore the use of natural language processing techniques to provide feedback to TAs as they provide written feedback about students’ work in introductory computer science courses. The project will investigate different ways to train TAs with direct instruction on effective feedback practices and culturally responsive teaching methods. This work has the potential to address the growing need for TAs to provide high-quality feedback, as computing class sizes are growing faster than faculty hiring. Low-quality feedback has been shown to negatively impact student success and willingness to continue in the discipline. Thus, improving the quality of TA feedback in computing courses could help more students to successfully enter the computing workforce.The integrated submission and TA training system will consist of a server that communicates with an Integrated Development Environment via custom-built plug-ins to facilitate in-class communication between students, instructors, and TAs. Human-computer-interaction techniques will be used to design unobtrusive dashboards that will enable instructors to efficiently monitor TA feedback. The project’s research plan will investigate how the new training system changes TA perspectives, alters feedback practices, and affects undergraduate learning. The project will also investigate strategies for data visualization that enable efficient monitoring of TA performance. The deliverables will include a series of TA training modules, a dataset of TA feedback samples together with student ratings, a natural language processing classifier that recognizes helpful or unhelpful feedback, and a dashboard that shows TA feedback performance to instructors. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.

该项目旨在通过调查将现有的提交和反馈交付系统与助教培训相结合来服务于国家利益。具体来说，这项工作将探讨使用自然语言处理技术，以提供反馈给助教，因为他们提供书面反馈学生的工作在介绍计算机科学课程。该项目将研究培训助教的不同方法，直接指导有效的反馈做法和文化上敏感的教学方法。这项工作有可能解决助教提供高质量反馈的日益增长的需求，因为计算班级规模的增长速度超过了教师招聘的速度。低质量的反馈已被证明会对学生的成功和继续学习的意愿产生负面影响。因此，提高计算机课程中助教反馈的质量可以帮助更多的学生成功地进入计算机劳动力市场。集成的提交和助教培训系统将包括一个服务器，该服务器通过定制的插件与集成开发环境进行通信，以促进学生、教师和助教之间的课堂交流。 人机交互技术将用于设计不显眼的仪表板，使教师能够有效地监测TA的反馈。该项目的研究计划将调查新的培训系统如何改变助教的观点，改变反馈的做法，并影响本科生的学习。该项目还将研究数据可视化策略，以便有效监测技术援助绩效。交付的成果将包括一系列TA培训模块，TA反馈样本数据集以及学生评分，识别有用或无用反馈的自然语言处理分类器，以及向教师显示TA反馈表现的仪表板。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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。

项目成果

A Practical Strategy for Training Graduate CS Teaching Assistants to Provide Effective Feedback

培训研究生计算机科学助教提供有效反馈的实用策略

• DOI: 10.1145/3587102.3588776
• 发表时间: 2023
• 期刊: ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education
• 作者: Zaman, Alina;Cook, Amy;Phan, Vinhthuy;Windsor, Alistair
• 通讯作者: Windsor, Alistair