Preliminary Steps Towards Detection of Proactive and Reactive Control States During Learning with fNIRS Brain Signals
Preliminary Steps Towards Detection of Proactive and Reactive Control States During Learning with fNIRS Brain Signals
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发表时间：
2021
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通讯作者：
Alicia Howell-Munson;Deniz Sonmez Unal;Erin Walker;Catherine M. Arrington;E. Solovey
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作者：
Alicia Howell-Munson;Deniz Sonmez Unal;Erin Walker;Catherine M. Arrington;E. Solovey
本文描述了一种双管齐下的方法来创建多模式智能辅导系统（ITS），该系统利用神经数据向系统通报学生的认知状态。最终目标是使用 fNIRS 大脑成像来区分使用真实世界学习环境期间的主动控制状态和反应控制状态。这些状态与学习直接相关，并且很难通过智能交通系统中的典型数据流来识别。作为识别大脑中的这些状态并了解它们对学习的影响的第一步，我们描述了两项初步研究：（1）我们在受控的连续表现任务中使用 fNIRS 大脑成像区分主动和反应控制；（2）我们提示学生进行主动或反应控制，同时使用 ITS 来了解这两种模式如何影响学习进度。我们建议将 fNIRS 数据流与 ITS 集成，创建一个多模式系统，用于检测用户的认知状态并适应环境以促进更好的学习策略。
This paper describes a two-pronged approach to creating a multimodal intelligent tutoring system (ITS) that leverages neural data to inform the system about the student’s cognitive state. The ultimate goal is to use fNIRS brain imaging to distinguish between proactive and reactive control states during the use of a real-world learning environment. These states have direct relevance to learning and have been difficult to identify through typical data streams in ITSs. As a first step towards identifying these states in the brain and understanding their effects on learning, we describe two preliminary studies: (1) we distinguished proactive and reactive control using fNIRS brain imaging in a controlled continuous performance task and (2) we prompted students to engage in either proactive or reactive control while using an ITS to understand how the two modes affect learning progress. We propose integrating the fNIRS datastream with the ITS to create a multimodal system for detecting the user’s cognitive state and adapting the environment to promote better learning strategies.