摘要
在"人—技"协同进化的教育发展态势下,学习者的学习方式和交互环境正面临深刻变革,物理学习空间内的学习支持服务亟待重塑。近年来,研究者们致力于采用传感器获取学习者的生理行为数据,结合学习分析技术推测其情绪状态,并以适当的干预机制来提高积极情绪唤醒度,进而助力于个体学业成功。当前,在物理学习空间中,针对学习者情绪感知的主要手段有人工观察法、自我报告法、基于生理信号、语音信号、面部表情信号以及眼动信号的感知方法;应用研究案例包含智能导师系统、虚拟学习同伴、情绪互动支持、自我调节能力评估、学情分析监控等主题。对物理学习空间中学习者情绪感知的研究,可为未来学习空间的重塑带来新的研究视角和参照。
Under the educational development of'human-technology'co-evolution,learners’learning styles and interactive environments are facing profound transformations;Thus,the learning support services in physical spaces need to be reshaped urgently.In recent years,researchers have focused on using sensor technology to collect learners’physiological and behavioral data,combining learning analytics to infer their emotional states,and adopting appropriate intervention mechanisms to improve the positive emotional arousal and academic success.Currently,the main methods of learners’emotion perception(artificial observational and self-report methods,perception methods based on physiological signals,speech signals,facial expression signals and eye movement signals)and five typical cases(intelligent tutoring system,virtual learning companion,emotional interaction support,self-regulated capability assessment and learning situation analysis monitoring)are pointed out to provide a novel research insight for the future reconstruction of the physical learning spaces.
作者
刘智
方常丽
刘三(女牙)
孙建文
Liu Zhi;Fang Changli;Liu Sanya;Sun Jianwen(National Engineering Laboratory for Educational Big Data,Central China Normal University;Collaborative Innovation Center for Educational Technology,Central China Normal University;National Engineering Research Center for E-Learning,Central China Normal University,Wuhan Hubei 430079)
出处
《远程教育杂志》
CSSCI
北大核心
2019年第2期33-44,共12页
Journal of Distance Education
基金
国家自然科学基金项目"多场景网络学习中基于‘行为-情感-主题’联合建模的学习者兴趣挖掘关键技术研究"(项目编号:61702207)
国家重点研发计划课题"数据驱动的数字教育个性化服务支撑技术研究"(项目编号:2017YFB1401303)
教育部人文社会科学研究项目"高校慕课环境下的互动话语行为及其对学习效果的影响机理研究"(项目编号:16YJC880052)资助
关键词
情绪感知
传感器
可穿戴技术
生理信号
学习分析
智能教育
Emotion Perception
Sensor
Wearable Technology
Physiological Signals
Learning Analytics
Intelligent Education
