| 基于传感数据的学习分析应用研究 |
| |
| 引用本文： | 李卿,任缘,黄田田,刘三女牙,屈杰.基于传感数据的学习分析应用研究[J].电化教育研究,2019(5):64-71. |
| |
| 作者姓名： | 李卿 任缘 黄田田 刘三女牙 屈杰 |
| |
| 作者单位： | 华中师范大学教育大数据应用技术国家工程实验室,湖北武汉,430079;华中师范大学国家数字化学习工程技术研究中心,湖北武汉,430079;临江市第一中学,吉林临江,134600 |
| |
| 基金项目： | 国家自然科学基金;湖北省自然科学基金 |
| |
| 摘 要： | 数据是学习分析研究的重要前提。传感技术的发展极大地提升了物理学习空间中的数据采集能力,拓展了学习分析的边界。为把握传感技术的应用现状与趋势,文章在分析传感技术的特征和功能基础上,使用文献研究法探讨了传感数据与学习分析结合的应用价值,构建了基于传感数据的学习分析框架,包括感知学习状态、预测学习表现、干预与反馈学习过程等。基于现有研究成果,将基于传感数据的学习分析应用归纳为学习认知、学习情感和动作技能等三个领域,分析了传感数据采集、模型优化、反馈机制等方面的挑战并提出未来可关注的研究方向。
|
| 关 键 词： | 教育大数据 传感数据 学习分析 可穿戴技术 |
Research on Application of Learning Analytics Based on Sensing Data |
| |
| LI Qing,REN Yuan,HUANG Tiantian,LIU Sanya,QU Jie.Research on Application of Learning Analytics Based on Sensing Data[J].E-education Research,2019(5):64-71. |
| |
| Authors: | LI Qing REN Yuan HUANG Tiantian LIU Sanya QU Jie |
| |
| Institution: | (National Engineering Laboratory for Educational Big Data,Central China Normal University,WuhanHubei 430079;National Engineering Research Center for E-Learning,Central China Normal University,Wuhan Hubei 430079;Linjiang NO.1 Middle School,Linjiang Jilin 134600) |
| |
| Abstract: | Data is an important prerequisite for research on learning analytics. The development of sensing technology has greatly improved the ability of data acquisition in physics learning space and expanded the boundary of learning analytics. In order to grasp the application status and trend of sensing technology, based on the analysis of the characteristics and functions of sensing technology, this paper adopts literature research method to explore the application value of combining sensing data with learning analytics. A framework of learning analytics based on sensing data is constructed, including perceiving learning state, predicting learning performance, intervention and feedback learning process, etc. Based on the existing research results, the application of learning analytics based on sensing data is summarized into three fields: learning cognition, learning emotion and motor skill. Finally, this paper analyzes the challenges of sensor data acquisition, model optimization and feedback mechanism, and proposes the future research directions as well. |
| |
| Keywords: | Big Data in Education Sensing Data Learning Analysis Wearable Technology |
| 本文献已被 维普 万方数据 等数据库收录！ |
|
