Project CLASSIFIES: Common Language Assessment in Studying Statistics with Instructional Feedback and Increased Enrollment Scalability

项目分类：具有教学反馈和提高招生可扩展性的统计研究中的通用语言评估

• 批准号: 2236150
• 负责人: Matthew Beckman
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2023-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236150&HistoricalAwards=false
• 关键词: Project; CLASSIFIES; Common; Language; Assessment

This project aims to serve the national interest by developing new tools to support instructors' assessment of written problems in large enrollment statistics courses. Writing is particularly crucial in statistics, and short answer questions provide students with important opportunities to interpret problems and consider multiple paths to a correct solution. Because students must formulate their answers in their own words, short answer questions are more robust for authentic assessment of student competencies and misconceptions than other short-form problems, such as multiple choice or true/false questions. For all assessments, timely feedback is well established as important for learning. Unfortunately, providing timely, quality feedback on student writing can be challenging in large classes. This can lead to assessment methods being poorly aligned with learning goals in the courses that reach the most students. This Level 1 project in the Engaged Student Learning track of the IUSE: EDU program addresses this long-standing obstacle to the effective use of free-form questions in large-enrollment classes. The project will develop, test, and refine a grading support platform that combines human and machine assessment. Specifically, the tools developed will sort students' solutions to short-answer problems into one of three categories: essentially correct, partially correct, or incorrect. Partially correct solutions are then sent to the course instructor for further review. The project engages partner institutions throughout the country to collect diverse data on statistics question prompts and student answers. The project-developed tools will give students more opportunities to strengthen their reasoning skills through exposure to short-answer problems and will give instructors a tool to monitor learning more closely.This project’s goal is to advance understanding and study effectiveness of innovative tools that pair instructor expertise with natural language processing (NLP) techniques to provide students with rich feedback. Integrating the project platform into introductory statistics courses will allow for the frequent use of short-answer tasks in large enrollment statistics classes. Large-enrollment course instructors will be able to provide detailed feedback much like students might expect in a class with 25-30 students. The project utilizes a collaboration of prominent experts in NLP, statistics education, and assessment to pursue the three aims of 1) developing an interactive, scalable, human/software partnership, 2) field-testing the methods across diverse student populations, and 3) creating open access resources of software, assessment tasks, and student data to foster future research on free-form formative assessments. The project will utilize a mixed-methods evaluation plan to assess progress towards key goals and the project-developed platform will be made freely available to interested users. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students.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.

该项目旨在通过开发新工具来支持教师评估大型招生统计课程中的书面问题，从而为国家利益服务。在统计学中，写作尤其重要，简答题为学生提供了解释问题和考虑多种途径获得正确解决方案的重要机会。因为学生必须用自己的话来表述答案，简答题比其他短答题（如选择题或是非题）更能真实地评估学生的能力和误解。对于所有的评估，及时的反馈对于学习是非常重要的。不幸的是，在大班教学中，为学生的写作提供及时、高质量的反馈是很有挑战性的。这可能导致评估方法与大多数学生接触的课程的学习目标不一致。这是IUSE: EDU项目“参与式学生学习”项目的第一级项目，它解决了在大规模招生课程中有效使用自由形式问题的长期障碍。该项目将开发、测试和完善一个结合人类和机器评估的评分支持平台。具体来说，开发的工具将把学生对简答问题的解决方案分为三类：基本正确、部分正确或不正确。部分正确的解决方案，然后发送给课程教师进一步审查。该项目与全国各地的合作机构合作，收集有关统计、问题提示和学生答案的各种数据。该项目开发的工具将使学生有更多的机会通过接触简答题来加强他们的推理能力，并将为教师提供一种更密切地监控学习的工具。该项目的目标是促进理解和研究创新工具的有效性，这些工具将教师的专业知识与自然语言处理（NLP）技术相结合，为学生提供丰富的反馈。将项目平台集成到统计入门课程中，将允许在大量注册的统计课程中频繁使用简答任务。大规模招生的课程讲师将能够提供详细的反馈，就像学生在25-30名学生的课堂上所期望的那样。该项目利用NLP、统计、教育和评估领域的杰出专家的合作来实现三个目标：1)发展一个互动的、可扩展的人/软件伙伴关系；2)在不同的学生群体中实地测试方法；3)创建软件、评估任务和学生数据的开放获取资源，以促进未来对自由形式形成性评估的研究。该项目将采用混合方法评估计划来评估实现关键目标的进展情况，项目开发的平台将免费提供给感兴趣的用户。NSF IUSE: EDU项目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。