Collaborative Research: Machine Learning for Student Reasoning during Challenging Concept Questions

协作研究：机器学习在挑战性概念问题中帮助学生推理

• 批准号: 2226553
• 负责人: Milo Koretsky
• 金额: $ 17.83万
• 依托单位: Tufts 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: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226553&HistoricalAwards=false
• 关键词: Collaborative; Research; Machine; Learning; Student

Artificial Intelligence (AI), and more specifically, language models, have been drastically changing how students and instructors think about learning and assessment. While there are legitimate concerns about how the use of these tools could be detrimental to learning, this research project aims to leverage language models to better prepare engineering learners of the 21st Century. The research project will use modern AI and machine learning (ML) tools to automate analysis of student-written responses to challenging concept questions. These qualitative questions are often used in large STEM classes to support active learning pedagogies; they require minimum calculations and focus on the application of underlying physical and chemical phenomena to various situations. With previous NSF funding, we have developed the Concept Warehouse (NSF DUE 1023099, 1821439, 2135190), a classroom response system where students provide written justifications to concept questions. Providing written justifications targets development of reasoning and sense-making skills in students and can also better prepare them for discussions with peers resulting in broader effectiveness of active learning pedagogies. However, expository prose also presents a daunting amount of information for instructors to process. In this project, we will leverage recent advancements in machine learning tools and natural language processing technologies to develop automated processes to analyze student-written justifications to challenging concept questions.This project will join engineering education researchers at Tufts University and AI/ML researchers at the University of Massachusetts Lowell. We will focus on the following research questions: (1) Based on human coding, what ideas do students use in explaining challenging concept questions in statics? How do these vary among challenging concept questions studied? (2) How well can Transformer-based ML models replicate the coding done by the human coders? For isomorphic question pairs, how well do ML models trained on the first question’s explanations perform on the second question? More generally, can ML-based coding based on one question be applied successfully to code the data for other questions, and what are the limits to this generalizability? We will complete three research tasks: (1) data collection of written responses for the same concept questions from nine or more engineering statics instructors at different institutions; (2) manual coding of a subset of students’ written explanations, and (3) developing and evaluating ML coding methods, followed by ML coding of the complete set of collected written explanations. While the project focuses on engineering statics, it is expected that findings will transfer to challenging questions in other engineering and science topics. Ultimately, successful implementation of machine learning will support learning and instruction of challenging concepts. Expected outcomes include a developing understanding of advantages and disadvantages of different ML approaches including their accuracy, determination of minimum data size requirements to apply the algorithms, and the ability to transfer learning from one question to isomorphic questions that require similar reasoning patterns. For instructors, data generated can provide real-time information about the different ways students are reasoning with examples of common cases. For engineering education researchers, characterizing explanations in different settings will support investigations of how student thinking relates to instructional practices and environments.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.

人工智能（AI），更具体地说的是语言模型，一直在彻底改变学生和讲师对学习和评估的思考方式。尽管对这些工具的使用有合理的担忧，但该研究项目旨在利用语言模型来更好地准备21世纪的工程学习者。该研究项目将使用现代的AI和机器学习（ML）工具来自动对学生写的回答分析以挑战概念问题。这些定性问题通常在大型STEM类中用于支持主动学习教学。他们需要最低限度的计算，并专注于将潜在的物理和化学现象应用于各种情况。有了以前的NSF资金，我们已经开发了概念仓库（NSF Dudure 1023099，1821439，2135190），这是一种教室响应系统，学生为概念问题提供书面理由。提供的书面理由是针对学生推理推理和感知技能的发展，也可以更好地为与同龄人讨论，从而使积极学习教学的有效性更广泛。但是，说明性散文还提出了大量的信息供讲师处理。在该项目中，我们将利用机器学习工具和自然语言处理技术的最新进步来开发自动化流程，以分析学生写的理由来挑战概念问题。该项目将加入Tufts University的工程教育研究人员和Massachusetts Lowell大学的AI/ML研究人员。我们将重点介绍以下研究问题：（1）基于人类编码，学生在解释静态概念问题中使用哪些想法？这些挑战概念问题在研究研究中有何不同？ （2）基于变压器的ML模型如何复制人类编码人员完成的编码？对于同构问题对，对第二个问题的解释训练的ML模型如何在第二个问题上表现出来？更一般而言，是否可以成功地将基于一个基于一个问题的基于ML的编码用于其他问题编码数据，并且该概括性的限制是什么？我们将完成三个研究任务：（1）来自不同机构的九名或更多工程静态教师的同一概念问题的书面响应的数据收集； （2）一部分学生书面解释的手册编码，以及（3）开发和评估ML编码方法，然后是ML编码完整的一组收集的书面解释。尽管该项目着重于工程静态，但预计发现将转移到其他工程和科学主题中的问题。最终，机器学习的成功实施将支持挑战概念的学习和指导。预期的结果包括对不同ML方法的优势和缺点的发展，包括其准确性，确定最低数据大小要求应用算法的需求以及从一个问题转移学习到需要类似推理模式的同构问题的能力。对于讲师而言，生成的数据可以提供有关学生在推理不同案例示例的不同方式的实时信息。对于工程教育的研究人员，表征不同环境中的解释将支持对学生对教学实践和环境相关的思考的调查。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响评估标准来通过评估来表现出珍贵的支持。