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

该项目旨在通过提高学生在解决化学问题方面的成功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这些信息才能解决问题。知道在哪里以及如何查看这些视觉信息对于形成新想法、回忆所需知识以及执行解决问题的必要步骤至关重要。机器学习将被用来研究学生看哪里和他们最终成功解决问题之间的联系，通过一种新颖的眼球追踪系统跟踪他们的观看行为。如果学生没有看到问题的相关特征，软件将在学习过程中提供实时反馈，从而为学生提供支持。项目团队的目标是，一旦化学教育中的益处得到证明，就扩展到其他STEM学科。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和高科技的方法，同时也对学生的学习产生重大影响。这项跨机构合作的目的是参与基础工作，以创建一个智能辅导系统，该系统使用网络摄像头眼动追踪数据和人工智能，在解决问题的活动中为化学学习者提供实时反馈。该研究将分三个阶段进行：1)传统的基于屏幕的眼动追踪方法将与基于网络摄像头的眼动追踪方法进行比较；2)第一阶段收集的数据将用于训练和评估机器学习模型，以预测学生的学习成果；3)团队将调查早期线索改变积极参与问题解决的学生的观看模式的能力。通过系统地开发机器学习模型，该模型使用眼动追踪来预测化学问题解决的成就结果，从而可以开发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有效的方式解决问题，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，这项研究有可能在解决化学问题的背景下支持大量的学生，包括那些可能没有表现出传统求助行为的学生。此外，将网络摄像头眼动追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参与研究的有价值的新工具。该项目的研究结果将通过会议和出版物分享给STEM本科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。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。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。

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.