【作 者】：
马 婧， 姜 萌， 董 艳
【关 键 词 】：
虚拟现实； 学习投入； 传感系统； 模型建构； 学习分析
【栏 目】：
学习环境与资源
【中文摘要】：
基于虚拟现实技术的学习者学习投入模型研究，是开发虚拟现实硬件及资源、进行相应教学设计的重要依据。目前，对虚拟现实环境的学习投入模型研究存在数据采集偏主观、实施采样困难、不易构建动态模型等问题，且模型分析维度单一，对特征值间的关联性分析不足。为了构建更为真实客观动态的学习投入模型，研究利用现代检测技术与虚拟现实相结合的分布式传感器系统，实现对学习者多维生理信息的实时监测，并对采集的多参数、多维度特征间的关联性进行分析。针对学习投入的情感、认知、行为三个维度，以电生理信号采样、语音特征提取、语义识别等现代计算机与模式识别技术为主要分析手段，实现对情绪类型的识别，并对唤醒度及情绪正负面程度进行量化；采用覆盖模型和约束模型，引入语音、语义及反应行为等信息以实现对认知投入和行为投入的分析。通过研究虚拟现实环境中学习者的学习投入综合模型，为深入分析虚拟现实教学策略与模式，优化虚拟现实学习环境提供了理论与实践支持。
【英文摘要】：
The study of student engagement model based on virtual reality technology is an important basis for developing virtual reality hardware and resources and carrying out corresponding teaching design. At present, the study of student engagement models for virtual reality environments has problems such as subjective data collection, difficulty in implementing sampling, and difficulty in constructing dynamic models, the model analysis with a single dimension and insufficient analysis of the correlation between feature values. In order to construct a more realistic and objective dynamic model of student engagement, this study uses distributed sensor system combining modern detection technology and virtual reality to achieve real-time monitoring of learners' multidimensional physiological information, and analyzes the correlation between the collected multi-parameter and multidimensional features. Aiming at three dimensions of student engagement of emotion, cognition and behavior, modern computer and pattern recognition techniques, such as electrophysiological signal sampling, speech feature extraction and semantic recognition, are used as the main analysis tools to achieve the identification of emotional types and to quantify the arousal degree and the degree of positive and negative emotions. The overlay model and constraint model are used to analyze the cognitive and behavioral engagement by introducing speech, semantic and reactive information. By studying the comprehensive model of student engagement in virtual reality environment, it provides theoretical and practical support for in-depth analysis of virtual reality teaching strategies and for models and optimization of virtual reality learning environment.