基于在线学习行为数据的远程学习者学业情绪分析研究——以Moodle平台为例
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摘要
学习者的学业情绪对学习有重要作用,异步学习平台的非及时性以及教师与学习者分离的特征使得难以对远程学习者进行学业情绪分析。本研究通过构建异步学习平台中远程学习者学业情绪分析模型,并将该模型进行Moodle平台案例课程的应用,探究远程学习者在线学习行为与学业情绪之间的关系。本研究在案例应用中通过对远程学习者在线学习行为数据进行统计分析,对学习者论坛文本数据进行情感计算,发现:学习者的学业情绪与作业成绩显著正相关,说明学习者的学业情绪会显著影响其学习效果;教师情感值与学习者学业情绪显著正相关,说明教师在论坛中的情绪倾向会正向影响学习者的学业情绪;学习者实时学业情绪与创作性行为之间显著正相关,并且发现案例课程中主要存在两类学业情绪表现不同的学习者,第一类学习者在案例课程中所发帖子的情感值为正向情感的占80%以上,第二类学习者学业情绪属于同一倾向的帖子数不超过80%,单独分析第二类学习者实时学业情绪与对应在线学习行为之间的相关性,发现两者之间显著性有较大提高。
Learners' academic emotions play an important role in learning,the timeliness of asynchronous learning platform and the separation of teacher and learner make it difficult to judge distance learners' academic emotions.This study builds a distance learners academic emotions analysis model and applys this model to one Moodle platform case course,to explore the relationship between the distance learners online learning behavior and academic emotions.In the case application of research,it analyzes learners' online learning behavior and compute learners' emotion through learners' text data.It is found that students' academic emotions and learning outcome have significantly positive correlation,this shows that students' academic emotions can significantly influence their learning outcome; distance learners' academic emotions and teachers' emotion have significantly positive correlation,this shows that teachers' emotional tendencies in BBS will be positive influence on learners' academic emotions; distance learners' academic emotions and their creative behavior have significantly positive correlation and there are two types of different learners in our case course: the first kind of learners post 80% text information which has positive emotion; the second category of learners' academic emotions are not the same in large scale.This study analyzes the second category of learners separately,with the finding thatthe correlation between academic emotions and their online learning behavior has improved significantly.
Learners' academic emotions play an important role in learning,the timeliness of asynchronous learning platform and the separation of teacher and learner make it difficult to judge distance learners' academic emotions.This study builds a distance learners academic emotions analysis model and applys this model to one Moodle platform case course,to explore the relationship between the distance learners online learning behavior and academic emotions.In the case application of research,it analyzes learners' online learning behavior and compute learners' emotion through learners' text data.It is found that students' academic emotions and learning outcome have significantly positive correlation,this shows that students' academic emotions can significantly influence their learning outcome; distance learners' academic emotions and teachers' emotion have significantly positive correlation,this shows that teachers' emotional tendencies in BBS will be positive influence on learners' academic emotions; distance learners' academic emotions and their creative behavior have significantly positive correlation and there are two types of different learners in our case course: the first kind of learners post 80% text information which has positive emotion; the second category of learners' academic emotions are not the same in large scale.This study analyzes the second category of learners separately,with the finding thatthe correlation between academic emotions and their online learning behavior has improved significantly.
引文
[1]曹良亮,陈丽(2006a).异步交互中远程学习者教学交互水平的研究[J].中国远程教育,(2):12-16+25.
[2]曹良亮,陈丽(2006b).远程学习者异步交互的行为方式和特点[J].中国远程教育,(1):15-19+78.
[3]韩斌(2014).远程学习者情绪表达和情感诉求的初步研究:以潜在远程学习者为例[J].北京广播电视大学学报,(6):48-53+60.
[4]李爽,钟瑶,喻忱等(2017).基于行为序列分析对在线学习参与模式的探索[J].中国电化教育,(3):88-95.
[5]李笑樱(2011).远程学习者情感自评量表及预警模型设计研究[D].华东师范大学.
[6]李勇帆,李里程(2013).情感计算在网络远程教育系统中的应用:功能、研究现状及关键问题[J].现代远程教育研究,(2):100-106.
[7]廖瑞华,李勇帆,刘琳,文海英(2010).基于SIFT的远程在线教育情感识别研究[J].计算机应用与软件,(6):67-69.
[8]梅英,谭冠政,刘振焘(2017).面向智慧学习环境的学习者情感预测方法[J].计算机辅助设计与图形学学报,(2):354-364.
[9]孙传远,刘玉梅,孙少华(2011).远程教育中的情感交互:问题与策略——基于远程学习者的实证研究[J].广东广播电视大学学报,(5):17-22.
[10]唐伟志(2014).远程教育中的情感缺失应对策略探析——基于联通主义学习理论为视角[J].现代远距离教育,(2):34-37.
[11]魏顺平(2013).学习分析技术:挖掘大数据时代下教育数据的价值[J].现代教育技术,(2):5-11.
[12]卫晓娜(2010).e-Learning系统中学生注意力识别的研究和应用[D].太原理工大学.
[13]徐菊红(2013).混合现实虚拟智能教室的方案设计[J].武汉工程大学学报,(3):83-86.
[14]詹泽慧(2013).基于智能Agent的远程学习者情感与认知识别模型——眼动追踪与表情识别技术支持下的耦合[J].现代远程教育研究,(5):100-105.
[15]朱祖林,黄彩虹,李锐等(2011).成人在线学业情绪倾向的测度方法研究[J].中国电化教育,(6):55-60.
[16]宗阳,孙洪涛,张亨国,郑勤华,陈丽(2 0 16).MOOCs学习行为与学习效果的逻辑回归分析[J].中国远程教育,(5):14-22+79.
[17]Bales,R.F.(1950).Interaction process analysis:a method for the study of small groups[J].Public Opinion Quarterly,13(3):388-389.
[18]Jaques,P.A.,Vicari,R.M.,Pesty,S.(2004).Applying affective tactics for a better learning[C].European Conference on Artificial Intelligence.IOS Press:114-118.
[19]Liu,M.(2015).Sentiment Analysis in Chinese Web Discussion Forums[J].International Journal of e-Education,e-Business,e-Management and e-Learning,(5):47-54.
[20]Ortony,A.,Clore,G.L.,Collins,A.(1990).The cognitive structure of emotions.[M].Cambridge University Press,1990.
[21]R.,Goetz,T.,Titz,W.,et al.(2002).Academic Emotions in Students’Self-Regulated Learning and Achievement:A Program of Qualitative and Quantitative Research[J].Educational Psychologist,37(2):91-105.
[22]Picard,R.W.(1997).Affective computing[J].Igi Global,1(1):71-73.
[23]Wen,M.,Yang,D.,RoséC,P.(2014).Sentiment analysis in MOOC discussion forums:What does it tell us?[C].Educational Data Mining.London,United Kingdom,July 4-7.
[2]曹良亮,陈丽(2006b).远程学习者异步交互的行为方式和特点[J].中国远程教育,(1):15-19+78.
[3]韩斌(2014).远程学习者情绪表达和情感诉求的初步研究:以潜在远程学习者为例[J].北京广播电视大学学报,(6):48-53+60.
[4]李爽,钟瑶,喻忱等(2017).基于行为序列分析对在线学习参与模式的探索[J].中国电化教育,(3):88-95.
[5]李笑樱(2011).远程学习者情感自评量表及预警模型设计研究[D].华东师范大学.
[6]李勇帆,李里程(2013).情感计算在网络远程教育系统中的应用:功能、研究现状及关键问题[J].现代远程教育研究,(2):100-106.
[7]廖瑞华,李勇帆,刘琳,文海英(2010).基于SIFT的远程在线教育情感识别研究[J].计算机应用与软件,(6):67-69.
[8]梅英,谭冠政,刘振焘(2017).面向智慧学习环境的学习者情感预测方法[J].计算机辅助设计与图形学学报,(2):354-364.
[9]孙传远,刘玉梅,孙少华(2011).远程教育中的情感交互:问题与策略——基于远程学习者的实证研究[J].广东广播电视大学学报,(5):17-22.
[10]唐伟志(2014).远程教育中的情感缺失应对策略探析——基于联通主义学习理论为视角[J].现代远距离教育,(2):34-37.
[11]魏顺平(2013).学习分析技术:挖掘大数据时代下教育数据的价值[J].现代教育技术,(2):5-11.
[12]卫晓娜(2010).e-Learning系统中学生注意力识别的研究和应用[D].太原理工大学.
[13]徐菊红(2013).混合现实虚拟智能教室的方案设计[J].武汉工程大学学报,(3):83-86.
[14]詹泽慧(2013).基于智能Agent的远程学习者情感与认知识别模型——眼动追踪与表情识别技术支持下的耦合[J].现代远程教育研究,(5):100-105.
[15]朱祖林,黄彩虹,李锐等(2011).成人在线学业情绪倾向的测度方法研究[J].中国电化教育,(6):55-60.
[16]宗阳,孙洪涛,张亨国,郑勤华,陈丽(2 0 16).MOOCs学习行为与学习效果的逻辑回归分析[J].中国远程教育,(5):14-22+79.
[17]Bales,R.F.(1950).Interaction process analysis:a method for the study of small groups[J].Public Opinion Quarterly,13(3):388-389.
[18]Jaques,P.A.,Vicari,R.M.,Pesty,S.(2004).Applying affective tactics for a better learning[C].European Conference on Artificial Intelligence.IOS Press:114-118.
[19]Liu,M.(2015).Sentiment Analysis in Chinese Web Discussion Forums[J].International Journal of e-Education,e-Business,e-Management and e-Learning,(5):47-54.
[20]Ortony,A.,Clore,G.L.,Collins,A.(1990).The cognitive structure of emotions.[M].Cambridge University Press,1990.
[21]R.,Goetz,T.,Titz,W.,et al.(2002).Academic Emotions in Students’Self-Regulated Learning and Achievement:A Program of Qualitative and Quantitative Research[J].Educational Psychologist,37(2):91-105.
[22]Picard,R.W.(1997).Affective computing[J].Igi Global,1(1):71-73.
[23]Wen,M.,Yang,D.,RoséC,P.(2014).Sentiment analysis in MOOC discussion forums:What does it tell us?[C].Educational Data Mining.London,United Kingdom,July 4-7.