Measuring Student Engagement in Lower Division Engineering Mathematics Classes

本项目由美国国家科学基金“改善本科STEM教育计划：教育与人力资源”（IUSE: EHR）资助，旨在通过改善本科工程教育服务于国家利益。它将通过设计、实施和测试一个系统来测量工程学生在一年级和二年级工程数学课程中的情感、行为和认知参与程度。工程专业在大一的流失率很高，主要是由于学生对课程的参与度不高。该项目计划开发一种传感器驱动的计算方法来测量学生参与的情感和行为成分。这些信息将用于确定提高参与度的教学策略，目标是提高学生在STEM教育途径中的成功和保留率。该项目的特点是工程、物理科学、心理科学和教育领域的教师和本科生研究人员之间的多学科合作。该项目将涉及大约300名一年级和二年级工程数学班的学生以及教授这些课程的经验丰富的教师。该项目的研究结果可能是朝着早期预警系统迈出的有价值的一步，该系统可以检测学生在STEM和非STEM课程中的脱离和焦虑。项目目标包括：(i)在中等规模的班级中建立一个强大的非侵入性和非侵入性传感器网络，以便实时提取面部和生命体征，并将其集成并显示在教师的仪表板上；（ii）利用传感器网络收集的数据，确定用于建模参与的情感和行为成分的稳健描述符；（iii）对系统的有效性进行试点测试，以收集有意义的数据，用于后续关于参与的情感、行为和认知指标的工作。要解决的基本研究问题涉及到通过自动捕捉非语言的参与线索来改善学生的学习：我们如何利用学生的参与表达，基于非语言的迹象，如面部表情、身体和眼睛运动、生理反应、姿势，来提高学习？该项目的研究结果将为多学科研究奠定基础，以结合新的机器学习和基于人工智能的模型来衡量STEM课程的参与度。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。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。

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by improving undergraduate engineering education. It will do so by designing, implementing, and testing a system to measure engineering students' emotional, behavioral, and cognitive engagement in first- and second-year engineering mathematics classes. Engineering programs suffer from a high rate of attrition in the freshman year, primarily due to poor engagement of students with their classes. The project plans to develop a sensor-driven, computational approach to measure emotional and behavioral components of student engagement. This information will be used to identify teaching strategies that increase engagement, with the goal of enhancing student success and retention in STEM education pathways. The project features a multi-disciplinary collaboration between faculty and undergraduate researchers in engineering, the physical sciences, psychological sciences, and education. The project will involve approximately 300 students in first- and second-year engineering mathematics classes and the experienced faculty who teach these courses. Findings from the project could be a valuable step toward an early warning system to detect student disengagement and anxiety in STEM and non-STEM courses. Project goals include: (i) establishment of a robust network of non-obtrusive and non-invasive sensors in mid-size classes to enable real-time extraction of facial and vital signs, which will be integrated and displayed on instructors' dashboards; (ii) identification of robust descriptors for modeling the emotional and behavioral components of engagement using data collected by the sensor networks; (iii) pilot testing of the system's effectiveness in gathering meaningful data for subsequent work on emotional, behavioral, and cognitive metrics of engagement. The fundamental research question to be addressed relates to improving student learning by the automated capture of non-verbal cues of engagement: How can we use students' expressions of engagement, based on non-verbal signs such as facial expressions, body and eye movements, physiological reactions, posture, to enhance learning? Findings from the project will constitute a foundation for multi-disciplinary research to incorporate novel machine learning and artificial intelligence-based models for measuring engagement in STEM classes. 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.