一种采用偏最小二乘回归的情绪调节策略预测模型

秦继伟 1; 3; 郑庆华 1; 2; 田锋 1; 杨扬 1; 2

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一种采用偏最小二乘回归的情绪调节策略预测模型

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- 一种采用偏最小二乘回归的情绪调节策略预测模型
- A Predictive Model for Emotion Regulation Strategies Using Partial Least Squares Regression
- 西安交通大学学报 2011年45卷第6期页码：46-49+110
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 西安交通大学计算机科学与技术系,西安,710049
  3. 新疆大学现代教育技术中心,乌鲁木齐,830046
- 作者简介：
- 基金信息：
  
  : 2010-12-20. 作者简介: 秦继伟(1978-),女,博士生,工程师;郑庆华(联系人),男,教授,博士生导师. 基金项目: 国家杰出青年基金资助项目(60825202);高等学校博士学科点专项科研基金资助项目(20090201110060);国家自然科学基金创新群体资助项目(60921003);国家科技支撑计划资助项目(2009BAH51B02)
- DOI：
  中图分类号： TP391
- 网络首发：2011-06-10，
  
  纸质出版：2011
- 稿件说明：
移动端阅览
秦继伟 1, 3, 郑庆华 1, 等. 一种采用偏最小二乘回归的情绪调节策略预测模型[J]. 西安交通大学学报, 2011,45(6):46-49+110.

A Predictive Model for Emotion Regulation Strategies Using Partial Least Squares Regression[J]. 2011, 45(6): 46-49+110.
秦继伟 1, 3, 郑庆华 1, 等. 一种采用偏最小二乘回归的情绪调节策略预测模型[J]. 西安交通大学学报, 2011,45(6):46-49+110. DOI：

A Predictive Model for Emotion Regulation Strategies Using Partial Least Squares Regression[J]. 2011, 45(6): 46-49+110. DOI：

摘要

针对目前个性化网络学习中重知识轻情感的现状

从学习者负面情绪调节入手

提出采用偏最小二乘回归方法

以采集的学习者的个性特征为自变量

负面情绪调节的策略特征作为应变量

建立学习者的情绪调节策略预测模型.通过实例验证

此模型具有较好的拟合预测能力.实验结果表明

个性对情绪调节策略的解释能力达70%

揭示出学习者负面情绪补偿的方法与个性相关联.

Abstract

A model of emotion regulation strategy using partial least squares regression is proposed based on personality traits to improve the situation that most researchers concern only learners' cognition

and construct a great amount of substantive digital learning resources

but neglect learners' affect in current personalized e-learning systems. The approach applies the principal component analysis and multiple regressions to extract some principle components in personality attributes

which are major influencing factors on emotion regulation strategy. Then the predictive model of emotion regulation strategy is demonstrated by an emotion chatting platform. Experimental results show that the explain ability of the emotion regulation strategy in personality characteristics reaches about 70% and that the model can reveal the relation between personality characteristic and the method of emotion compensation.

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Keywords

references

PICARD R W. Affective computing [M].Boston, MA, USA: MIT Press, 1997:1-10.

TIAN Feng, ZHENG Qinghua, ZHENG Deli. Mining patterns of e-learner emotion communication in turn level of Chinese interactive text experiments and findings[C]∥Proceedings of the 14th International Conference on Computer Supported Cooperative Work in Design. Piscataway, NJ, USA: IEEE, 2010: 664-670.

孟绍兰. 情绪心理学[M]. 北京:北京大学出版社, 2005: 3-4.

JOHN O P, GROSS J J. Healthy and unhealthy emotion regulation: personality processes [J]. Individual Difference Journal of Personality, 2004, 72(6):1301-1333.

GROSS J J. Emotion regulation: affective, cognitive and social consequences [J]. Psychophysiology, 2002, 39(3): 281-291.

DENNIS T A. Interactions between emotion regulation strategies and affective style: implications for trait anxiety versus depressed mood [J]. Motiv Emot, 2007, 31: 200-207.

叶奕乾, 何存道, 梁建宁. 普通心理学[M].上海:华东师范大学出版社, 2008:1-3.

EYSENCK S B G, EYSENCK H J, BARRETT P. A revised version of the psychoticism scale [J]. Personality and Individual Differences, 1985, 6(1): 21-29.

DIGMAN J M. Personality structure: emergence of the five-factor model[J]. Annual Review of Psychology, 1990:41(2):417-440.

CATTEL R B, CATTELL H E P. Personality structure and the new fifth edition of the 16PF [J]. Educational and Psychological Measurement, 1995,55(6):926-937.

王力, 柳恒超, 李中权, 等. 情绪调节问卷中文版的信效度研究[J]. 中国健康心理学杂志, 2007, 15(6): 503-505.

WANG Li, LIU Hengchao, LI Zhongquan, et al. Reliability and validity of emotion regulation questionnaire Chinese revised version[J].China Journal of Health Psychology, 2007, 15(6): 503-505.

GELADI P. KOWALSKI B R. Partial least-squares regression: a tutorial [J]. Analytica Chimica Acta, 1986,185:1-17.

王惠文. 偏最小二乘回归方法及其应用[M]. 北京: 国防工业出版社, 1999:151-168.

ZHENG Qinghua, WU Xiyuan, LI Haifei. A rough set based approach to find learners' key personality attributes in an e-learning environment [J]. International Journal of Web-Based Learning and Teaching Technologies, 2008, 3(4): 29-56.

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