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

基于计算机的智能辅导系统（ITSs）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量身定制。已证明信息技术支持学生学习，并在课堂上广泛实施，但并非所有学生都有效地参与信息技术，导致不同的学习成果。之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关于学习者参与度的足够详细的信息。例如，学生对问题的反应可能很慢，要么是因为他们分心了，要么是因为他们正在深入思考这个问题。在这个拟议的项目中，日志数据将与一系列生理测量相辅相成，包括眼睛注视、脑电图（EEG）和心率，以更全面地了解学生何时以及为什么会脱离ITSs。神经生理学数据通常是在受控的实验室环境中获取的，但该项目将利用便携式和可穿戴技术的最新技术发展，研究学生在学校环境中使用ITS的情况。此外，研究人员将通过实验操纵导师的帮助水平（例如，提示是自动提供还是按需提供），并测量其对学生参与度的影响。拟议的研究将在美国和以色列这两个国家同时进行，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，这可以改善每年成千上万的学生的学习体验。在学习和教学中，信息技术服务为学生提供的最佳援助水平是一个备受争议的话题，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”）。先前的研究主要依赖于日志数据，这无法捕捉到学习者参与的多维性。本研究将探讨学习者参与的行为、认知和情感因素在导师协助与学习成果关系中的中介作用。这一目标将通过多模式方法来研究学生的参与，包括日志数据、眼睛注视、脑电图、心率、皮肤电反应和自我报告的测量。此外，研究人员将通过实验操纵导师帮助的两个关键特征——提示提供的信息水平（基于原则的提示与特定问题的提示）和它们的呈现模式（主动的与按需的）——并测量它们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化学导师StoichTutor来学习化学概念。该项目的研究结果将有助于建立互动-建设性-主动-被动（ICAP）理论框架，并有助于设计更有吸引力和更有效的导师。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。

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.