NSF-BSF: Utilizing Neurophysiological Measures to Better Understand and Improve Engagement and Learning with Intelligent Tutoring Systems

NSF-BSF：利用神经生理学措施通过智能辅导系统更好地理解和改善参与和学习

• 批准号: 2141139
• 负责人: Ido Davidesco
• 金额: $ 85万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141139&HistoricalAwards=false
• 关键词: NSF; BSF; Utilizing; Neurophysiological; Measures

Computer-based intelligent tutoring systems (ITSs) provide students with a personalized learning experience that is tailored to their prior knowledge and learning progression. ITSs have been shown to support student learning and are implemented widely in classrooms, but not all students engage effectively with ITSs, leading to varying learning outcomes. Prior research primarily relied on data that is automatically collected by tutors (e.g., How many errors a student makes, how fast students answer a question posed by the tutor), but this data cannot provide sufficiently detailed information about learner engagement. For example, students might be slow in responding to a question either because they are distracted or because they are thinking deeply about the problem. In this proposed project, log-data will be complemented with an array of physiological measures, consisting of eye gaze, Electroencephalography (EEG), and heart rate, to provide a more comprehensive understanding of when and why students get disengaged with ITSs. Neurophysiological data is typically acquired in controlled laboratory environments, but this project will leverage recent technological developments in portable and wearable technologies to study student engagement with ITS in school environments. Additionally, the investigators will experimentally manipulate the level of tutor assistance (e.g., whether hints are provided automatically or on-demand) and measure its impact on student engagement. The proposed studies will be conducted concurrently in two countries - the U.S. and Israel – which will contribute to the ability to generalize results to a wider range of students. The results of this project will support the design of more engaging and effective tutors, which could improve the learning experience of tens of thousands of students each year.The optimal level of assistance provided to students by ITSs is a much-debated topic in learning and instruction since both too much and too little assistance can be detrimental to student engagement and learning (the “assistance dilemma”). Prior research primarily relied on log-data, which cannot capture the multi-dimensional nature of learner engagement. This project will investigate the mediating role of behavioral, cognitive, and affective components of learner engagement in the relation between tutor assistance and learning outcomes. This goal will be achieved using a multimodal approach to study student engagement with log-data, eye gaze, EEG, heart rate, galvanic skin response, and self-reported measures. Additionally, the investigators will experimentally manipulate two key features of tutor assistance - the level of information provided by hints (principle-based vs. problem-specific hints) and their mode of presentation (proactive vs. on-demand) - and measure their impact on learner engagement. This research will be conducted in high school-based laboratories in both the U.S. and Israel using a well-tested intelligent tutor for learning chemistry concepts, the StoichTutor. The project findings will contribute to the Interactive-Constructive- Active-Passive (ICAP) theoretical framework and to the design of more engaging and effective tutors.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.

基于计算机的智能辅导系统（ITS）为学生提供个性化的学习体验，根据他们的先验知识和学习进度进行定制。ITS已被证明可以支持学生的学习，并在课堂上广泛实施，但并非所有学生都能有效地参与ITS，导致不同的学习成果。以前的研究主要依赖于导师自动收集的数据（例如，学生犯了多少错误，学生回答导师提出的问题有多快），但这些数据无法提供有关学习者参与度的足够详细的信息。例如，学生回答问题可能很慢，因为他们分心了，或者因为他们正在深入思考这个问题。在这个拟议的项目中，日志数据将补充一系列生理措施，包括眼睛凝视，脑电图（EEG）和心率，以提供更全面的了解学生何时以及为什么会脱离ITS。神经生理学数据通常是在受控的实验室环境中获得的，但该项目将利用便携式和可穿戴技术的最新技术发展来研究学生在学校环境中与ITS的互动。此外，研究人员将实验性地操纵导师帮助的水平（例如，提示是自动提供的还是按需提供的），并衡量其对学生参与度的影响。拟议的研究将在两个国家同时进行-美国和以色列-这将有助于将结果推广到更广泛的学生。这个项目的结果将支持设计更有吸引力和更有效的导师，这可以改善成千上万的学生的学习经验，每年。ITSs提供给学生的最佳援助水平是一个在学习和教学中备受争议的话题，因为太多或太少的援助都可能不利于学生的参与和学习（“援助困境”）。以前的研究主要依赖于日志数据，无法捕捉学习者参与的多维性质。本研究将探讨行为、认知与情感因素在教师协助与学习成效关系中的中介作用。这一目标将使用多模式方法来研究学生参与日志数据，眼睛凝视，EEG，心率，皮肤电反应和自我报告的措施。此外，研究人员将实验性地操纵导师协助的两个关键特征-提示提供的信息水平（基于原则的提示与特定问题的提示）及其呈现模式（主动与按需）-并测量其对学习者参与的影响。这项研究将在美国和以色列的高中实验室进行，使用经过良好测试的智能导师学习化学概念，StoichTutor。该项目的研究结果将有助于互动-建设-主动-被动（ICAP）的理论框架，并设计更有吸引力和有效的导师。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。