[1]龚礼林,刘红霞,赵 蔚,等.情感导学系统(ATS)的关键技术及其导学模型研究——论智能导学系统走向情感导学系统之意蕴[J].远程教育杂志,2019,(05):045-55.
 Gong Lilin,Liu Hongxia,Zhao Wei,et al.Research on Key Techniques of Affective Tutor System and Its Tutoring Model:The Implications from Intelligent Tutoring System to Affective Tutoring System[J].Distance Education Journal,2019,(05):045-55.
点击复制

情感导学系统(ATS)的关键技术及其导学模型研究——论智能导学系统走向情感导学系统之意蕴()
分享到:

《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年05期
页码:
045-55
栏目:
前沿探索
出版日期:
2019-10-01

文章信息/Info

Title:
Research on Key Techniques of Affective Tutor System and Its Tutoring Model:The Implications from Intelligent Tutoring System to Affective Tutoring System
作者:
龚礼林; 刘红霞; 赵 蔚; 刘阳
东北师范大学 信息科学与技术学院,吉林长春 130000
Author(s):
Gong Lilin; Liu Hongxia; Zhao Wei; Liu Yang
College of Information Science and Technology, Northeast Normal University, Changchun Jilin 130000
关键词:
智能导学系统情感导学系统情感识别导学模型学习理论ITSATS
Keywords:
Intelligent Tutoring System Affective Tutoring System Emotion Recognition Learning Theories ITS ATS
分类号:
G420
文献标志码:
A
摘要:
神经科学、心理学和教育学等的研究表明,学习者情感显著影响学习行为和学习效果,融入学习者情感的智能导学系统即情感导学系统有望进一步提高自适应能力,促进学习效果的提升。深入了解情感导学系统相关技术及其模型有助于促进更个性化、人文化的导学系统研究。采用文献研究法对情感导学系统及其相关理论的缘起与发展历程进行了梳理,对情感导学系统的情感识别技术包括情感采集、预处理、特征提取、情感识别进行了概述,着重对情感导学系统的学习者情感、学习兴趣、学习风格等相关模型进行了剖析,并在社会认知理论和学习风格理论的指导下,提出基于学习者情感、学习兴趣和学习风格的导学模型与导学策略,最后从多模态技术融合、智能化情感反馈、深度人机交互、具身认知理念、伦理道德等方面分析了情感导学系统的未来发展趋势与挑战,以期为情感导学系统的研究与实践提供参考与借鉴。
Abstract:
Studies in neuroscience, psychology, and education have shown that learner emotions significantly affect learning behaviors and learning effects. The intelligent tutoring system that integrates learners’ emotions, that is, the affective tutoring system, is expected to further improve adaptive ability and promote learning outcomes. An in-depth understanding of the technologies and models of the affective tutoring system will help to promote a more personalized, humanistic tutoring system. The literature research method is used to sort out the origin and development process of the affective tutoring system and its related theories. The emotion recognition technologies of the affective tutoring system, including emotion collection, preprocessing, feature extraction and emotion recognition, are summarized. The related models of learner emotions, learning interests and learning styles of the affective tutoring system are analyzed. Under the guidance of social cognitive theory and learning style theory, a guiding model and tutoring strategies based on learners’ emotions, learning interests and learning styles is proposed. Finally this paper analyzed the future development trends and challenges of the affective tutoring system from the aspects of multi-modal technology fusion, intelligent emotional feedback, deep human-computer interaction, personal cognitive concepts, ethics and morality, in order to guide the research and practice of affective tutoring systems.

备注/Memo

备注/Memo:
基金项目:本文系教育部人文社会科学研究项目青年课题“大数据时代在线学习者情感挖掘与干预研究”(项目编号:16YJC880046)的研究成果
更新日期/Last Update: 1900-01-01