Collaborative Research: Using Machine Learning to Improve Visual Problem-Solving in Chemistry Education

协作研究：利用机器学习提高化学教育中的视觉问题解决能力

• 批准号: 2235790
• 负责人: Sarah Hansen
• 金额: $ 10.79万
• 依托单位: Columbia 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: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235790&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; Machine; Learning

This project aims to serve the national interest by increasing student success and confidence in solving chemistry problems, which is of vital importance to encouraging students to pursue STEM degrees. Chemistry learning materials are filled with a variety of visual information (chemical symbols, mathematical representations, graphical information and text) that students must understand and process in order to solve problems. Knowing where and how to look at this visual information is critical to forming new ideas, recalling required knowledge, and performing the steps necessary for problem solving. Machine learning will be utilized to investigate the link between where a student looks and their resulting success in solving a problem by tracking their viewing behavior with a novel eye-tracking system. The resulting software will support students during the learning process by providing real-time feedback if they are not viewing relevant features of a problem. The project team aims to expand to additional STEM disciplines once the benefits in chemistry education has been demonstrated. As a result, this project has the potential to provide a more accessible and high-tech method for carrying-out STEM education research while also having a substantial impact on student learning.This cross-institution collaboration aims to engage in foundational work to create an intelligent tutoring system that uses webcam eye-tracking data and artificial intelligence to provide chemistry learners with real-time feedback during problem solving activities. The study will be executed in three phases: 1) traditional screen-based eye tracking methods will be compared to webcam-based eye tracking methods, 2) data collected in phase one will be used to train and evaluate a machine learning model to predict student outcomes, and 3) the team will investigate the ability of early cues to change the viewing patterns of students actively engaged in problem solving. Through the systematic development of a machine learning model that uses eye tracking to predict achievement outcomes of problem solving in chemistry, the development of a feedback model for early intervention is possible. This individualized feedback has the potential to support students to engage with problem solving in more productive ways, build confidence, and provide valuable insights into how students solve problems. By providing immediate, individualized feedback, this research has the potential to support a large number of students in a chemistry problem solving context, including those who may not exhibit traditional help-seeking behavior. Further, comparing web-cam eye tracking to traditional monitor-based approaches has the potential to provide the chemistry education community with a valuable new tool for engaging in research. Findings from this project will be shared through conferences and publications for undergraduate STEM educators as well as to specific communities interested in eye-tracking methods. The NSF IUSE: EDU 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.

该项目旨在通过提高学生解决化学问题的成功率和信心来服务于国家利益，这对鼓励学生攻读STEM学位至关重要。化学学习材料充满了各种视觉信息（化学符号，数学表示，图形信息和文本），学生必须理解和处理，以解决问题。知道在哪里以及如何查看这些视觉信息对于形成新的想法，回忆所需的知识以及执行解决问题所需的步骤至关重要。机器学习将被用来调查学生看的地方和他们在解决问题时的成功之间的联系，通过一个新的眼动跟踪系统跟踪他们的观看行为。由此产生的软件将支持学生在学习过程中提供实时反馈，如果他们没有看到一个问题的相关功能。该项目小组的目标是扩大到其他STEM学科，一旦在化学教育的好处已经证明。因此，该项目有可能为开展STEM教育研究提供一种更容易获得的高科技方法，同时也对学生的学习产生重大影响。这项跨机构合作旨在参与基础工作，创建一个智能辅导系统，该系统使用网络摄像头眼动跟踪数据和人工智能，为化学学习者在解决问题的活动中提供实时反馈。该研究将分三个阶段进行：1）传统的基于屏幕的眼动跟踪方法将与基于网络摄像头的眼动跟踪方法进行比较，2）第一阶段收集的数据将用于训练和评估机器学习模型，以预测学生的结果，3）团队将调查早期线索改变学生积极参与解决问题的观看模式的能力。通过系统地开发使用眼动跟踪来预测化学问题解决的成就结果的机器学习模型，可以开发用于早期干预的反馈模型。这种个性化的反馈有可能支持学生以更富有成效的方式解决问题，建立信心，并为学生如何解决问题提供有价值的见解。通过提供即时的，个性化的反馈，这项研究有可能支持大量的学生在化学问题解决的背景下，包括那些可能不会表现出传统的求助行为。此外，将网络摄像头眼动跟踪与传统的基于监视器的方法进行比较，有可能为化学教育界提供一种有价值的新工具，用于从事研究。 该项目的研究结果将通过会议和出版物分享给本科STEM教育工作者以及对眼动追踪方法感兴趣的特定社区。NSF IUSE：EDU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。