通过整合生物传感器和多模态学习分析增强在线学习：用于检测和预测学生行为的综述
Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
AI总结：
本综述探讨整合生物传感器与多模态学习分析以检测和预测学生行为，基于54项研究，强调其变革潜力，旨在推动个性化自适应在线学习。
摘要：
在现代在线学习中，理解和预测学生行为对于提升参与度和优化教育成果至关重要。本系统综述探讨了生物传感器与多模态学习分析（MmLA）的整合，以分析和预测基于计算机的学习会话中的学生行为。我们考察了关键挑战，包括情绪和注意力检测、行为分析、实验设计以及数据收集中的人口统计学考量。我们的研究强调了生理信号（如心率、大脑活动和眼动追踪）与传统交互数据及自我报告相结合，在深入洞察认知状态和参与水平方面日益增长的作用。我们综合了54项关键研究的发现，分析了常用方法，如先进的机器学习算法和多模态数据预处理技术。本综述识别了该领域当前的研究趋势、局限性和新兴方向，强调了生物传感器驱动的自适应学习系统的变革潜力。我们的研究结果表明，整合多模态数据可以促进个性化学习体验、实时反馈和智能教育干预，最终推动实现更加定制化和自适应的在线学习体验。
英文摘要：
In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking, combined with traditional interaction data and self-reports to gain deeper insights into cognitive states and engagement levels. We synthesize findings from 54 key studies, analyzing commonly used methodologies such as advanced machine learning algorithms and multimodal data pre-processing techniques. The review identifies current research trends, limitations, and emerging directions in the field, emphasizing the transformative potential of biosensor-driven adaptive learning systems. Our findings suggest that integrating multimodal data can facilitate personalized learning experiences, real-time feedback, and intelligent educational interventions, ultimately advancing toward a more customized and adaptive online learning experience.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
