多元分析框架下的MOOC学习行为分析
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摘要
MOOC学习行为分析研究已从描述性研究转向以大数据为证据、学习行为模型和分析框架为理论基础的深入研究阶段。MOOC课程数量激增,平台的功能愈发强大,学习者学习行为的印记被多种数据形式记录,分析学习绩效、学习行为产生的原因及相关性已有了研究环境和研究基础。本文结合中国大学MOOC平台上的X课程,梳理学习者学习行为类型,运用系统的观点和方法,从学习行为、行为分析维度、数据来源及类型、分析目的等要素及相互关系分析的视角,构建了多元学习分析框架,根据框架中的学习行为及影响要素,分析了X课程数据,对学习行为的整体趋势进行了分析,得出了目前MOOC课程学习中学习者持续力不强、学习投入不足、参与度不高的结论。在此基础上讨论了改善学习支持服务、优化课程设计的思路。
The study of MOOC learning behavioral analysis has gone from descriptive phase to intensive research,which treats large data as evidence on the basis of the learning behavior model and analytical framework.With the proliferation of MOOC courses and optimization of platform,learners' learning behaviors are recorded in a variety of forms.It has already had research environment and existing base to analyze learning performance,causes and correlation of learning behaviors.Based on the X course from China's Universities MOOC platform,this paper tends to define the types of learners' behaviors and formulated a multiple analytical framework in the perspectives of behavioral analysis,learning behavioral dimension,data sources,data types as well as analytical purposes and correlation analysis.According to the influencing factors of framework,this paper analyzes the data from X course and the overall trend of learning behaviors.It is concluded that current MOOC learners' persistence is not strong,learners' engagement in learning is insufficient and that learners' learning participation is not high.Based on such problems,this paper tends to propose some ideas for improving learning support service and optimizing curriculum design.
The study of MOOC learning behavioral analysis has gone from descriptive phase to intensive research,which treats large data as evidence on the basis of the learning behavior model and analytical framework.With the proliferation of MOOC courses and optimization of platform,learners' learning behaviors are recorded in a variety of forms.It has already had research environment and existing base to analyze learning performance,causes and correlation of learning behaviors.Based on the X course from China's Universities MOOC platform,this paper tends to define the types of learners' behaviors and formulated a multiple analytical framework in the perspectives of behavioral analysis,learning behavioral dimension,data sources,data types as well as analytical purposes and correlation analysis.According to the influencing factors of framework,this paper analyzes the data from X course and the overall trend of learning behaviors.It is concluded that current MOOC learners' persistence is not strong,learners' engagement in learning is insufficient and that learners' learning participation is not high.Based on such problems,this paper tends to propose some ideas for improving learning support service and optimizing curriculum design.
引文
[1]冯雪松,于青青,李晓明(2016).慕课问道:将教学视频转型为慕课的尝试[J].开放学习研究,(1):37-41.
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[13]郁晓华,顾小清(2013).学习活动流:一个学习分析的行为模型[J].远程教育杂志,(4):20-28.
[14]周玲(2017).多维视角下MOOC学习分析发展机制与分析框架建构[J].电化教育研究,(4):41-46.
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[16]Kerr,M.M.,Zigmond,N.,Schaeffer,A.L.,&Brown,G.M.(1986).An observational follow-up study of successful and unsuccessful high school students[J].The High School Journal,70(1):20-24.
[17]Miserandino,M.(1996).Children who do well in school:Individual differences in perceived competence and autonomy in above-average children[J].Journal of Educational Psychology,88(2):203-214.
[18]Reich,J.(2015).Rebooting MOOC research[J].Science,347(6217):34-35.
[19]Verbert,K.,Manouselis,N.,Drachsler,H.,&Duval,E.(2012).DatasetDr iven Research to Suppor t Learning and Knowledge Analytics[J].Educational Technology&Society,15(3):133-148+214.
[2]郭炯,郑晓俊(2017).基于大数据的学习分析研究综述[J].中国电化教育,(1):121-130.
[3]胡勇,王陆(2006).在线学习者的自我评价与反思研究[J].开放教育研究,(2):69-73.
[4]胡勇,赵凤梅(2015).在线学习成效的理论分析模型及测量[J].电化教育研究,(10):37-45.
[5]蒋卓轩,张岩,李晓明(2015).基于MOOC数据的学习行为分析与预测[J].计算机研究与发展,(3):614-628.
[6]李爽,王增贤,喻忱,宗阳(2016).在线学习行为投入分析框架与测量指标研究—基于LMS数据的学习分析[J].开放教育研究,22(2):77-88.
[7]李爽,喻忱(2015).远程学生学习投入评价量表编制与应用[J].开放教育研究,21(6):62-70+103
[8]彭文辉,杨宗凯,刘清堂(2013).网络学习行为系统概念模型构建研究[J].中国电化教育,(9):39-46.
[9]瑞贝卡·弗格森,迈克·夏普斯,拉塞尔·贝尔,范奕博,陈文宜译(2015).MOOCs2030:大规模开放学习的未来展望[J].北京广播电视大学学报,(6):19-24.
[10]肖为胜,方志军(2009).在线学习模式浅探[J].教育学术月刊,(6):108-109.
[11]杨明权,韩景卫(2003).行为科学[M].北京:陕西人民出版社.
[12]杨素娟(2011).在线学习社区的组织气氛探析[J].中国远程教育,(6):64-68.
[13]郁晓华,顾小清(2013).学习活动流:一个学习分析的行为模型[J].远程教育杂志,(4):20-28.
[14]周玲(2017).多维视角下MOOC学习分析发展机制与分析框架建构[J].电化教育研究,(4):41-46.
[15]Greller,W.,Drachsler,H.(2012).Translating learning into numbers:Ageneric framework for learning analytics[J].Educational technology&society,(15):42-57.
[16]Kerr,M.M.,Zigmond,N.,Schaeffer,A.L.,&Brown,G.M.(1986).An observational follow-up study of successful and unsuccessful high school students[J].The High School Journal,70(1):20-24.
[17]Miserandino,M.(1996).Children who do well in school:Individual differences in perceived competence and autonomy in above-average children[J].Journal of Educational Psychology,88(2):203-214.
[18]Reich,J.(2015).Rebooting MOOC research[J].Science,347(6217):34-35.
[19]Verbert,K.,Manouselis,N.,Drachsler,H.,&Duval,E.(2012).DatasetDr iven Research to Suppor t Learning and Knowledge Analytics[J].Educational Technology&Society,15(3):133-148+214.