Multimodal Learning Analytics and Neurofeedback for Optimizing Online Learners' Self-Regulation

被引：3

作者：

Han, Insook ^{[1
]}

Obeid, Iyad ^{[2
]}

Greco, Devon ^{[3
]}

机构：

[1] Korea Univ, Dept Educ, Seoul, South Korea

[2] Temple Univ, Dept Elect & Comp Engn, Philadelphia, PA USA

[3] Narbis Inc, Ambler, PA USA

来源：

TECHNOLOGY KNOWLEDGE AND LEARNING | 2023年 / 28卷 / 04期

关键词：

Multimodal learning analytics; Electroencephalogram; Neurofeedback; Online learners; Self-regulated learning; EYE-TRACKING; ATTENTION; BEHAVIOR;

D O I：

10.1007/s10758-023-09675-5

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

This report describes the use of electroencephalography (EEG) to collect online learners' physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new technology for collecting accurate information on learners' concentration levels. When combined with other learner data, neural data can be used to analyze and predict self-regulated behaviors during online learning. We further suggest the use of machine learning algorithms to provide optimal live neurofeedback to train online learners' brains to improve their self-regulated learning behaviors. The challenges of EEG and neurofeedback in online educational settings are also discussed.

引用

页码：1937 / 1943

页数：7

共 37 条

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