基于DEA的在线学习者学习效率评价
|
摘要
针对现有在线学习者学习效率评价方法的局限性,本研究选取英国开放大学数据集中的一门STEAM课程,构建了在线学习者学习活动效率输入/输出评价体系,之后采用数据包络分析的方法,使用CCR模型、BCC模型、超效率模型与Malmquist指数四种模型对学习效率进行了分析。研究发现,此门课程学生综合效率偏低,其原因偏向于学生没有使用合适的学习方法与技巧,而并非学习活动投入时间不够;学生普遍存在投入不足与产出冗余的情况;超效率分析所区分出的综合效率有效的学生的差异较小;连续选修了两个学期课程的55位同学其全要素生产率提升,且与第二次课程评价结果一致。数据包络分析原理及方法对分析在线学习者的学习过程具有一定的适用性,但在模型应用与结果解释方面仍需要优化改进。
In view of the limitation of the existing online learners' learning efficiency evaluation methods, this study selects a STEAM course in the Open University UK learning analytics dataset to build online learners learning activity input/output efficiency evaluation system using the method of Data Envelopment Analysis(DEA). The learning efficiency is analyzed with CCR model, BCC model, super efficiency model and Malmquist Index model. The results suggest that the overall efficiency of students in this course is low, due to the fact that students do not use appropriate learning strategies and skills, rather than the lack of time devoted to learning activities. Students generally have the situation of insufficient input and output redundancy. The results of the super efficiency model show that the difference between the students whose efficiency values are equal to 1 is small. The total factor productivity of 55 students who have been enrolled in two consecutive semesters of courses is consistent with the results of the second course evaluation. The principles and methods of Data Envelopment Analysis are applicable to the analysis of online learners' learning process, but the application of the model and the interpretation of results still need to be improved.
In view of the limitation of the existing online learners' learning efficiency evaluation methods, this study selects a STEAM course in the Open University UK learning analytics dataset to build online learners learning activity input/output efficiency evaluation system using the method of Data Envelopment Analysis(DEA). The learning efficiency is analyzed with CCR model, BCC model, super efficiency model and Malmquist Index model. The results suggest that the overall efficiency of students in this course is low, due to the fact that students do not use appropriate learning strategies and skills, rather than the lack of time devoted to learning activities. Students generally have the situation of insufficient input and output redundancy. The results of the super efficiency model show that the difference between the students whose efficiency values are equal to 1 is small. The total factor productivity of 55 students who have been enrolled in two consecutive semesters of courses is consistent with the results of the second course evaluation. The principles and methods of Data Envelopment Analysis are applicable to the analysis of online learners' learning process, but the application of the model and the interpretation of results still need to be improved.
引文
[1]鲍平平(2007a).数据包络分析法在网络自主学习者学习的相对有效性评价中的应用研究[C]//第6届教育技术国际论坛论文集.北京:电子工业出版社.
[2]鲍平平(2007b).DEA在网络学习相对有效性评价中的应用[J].现代远程教育研究,(6):65-68.
[3]陈美华(2012).基于PCA-DEA的英语有效学习过程评价模型研究[J].东南大学学报(哲学社会科学版),14(6):108-111.
[4]成刚(2014).数据包络分析方法与MaxDEA软件[M].北京:知识产权出版社:210-232.
[5]邓可(2015).基于DEA-BCC模型的我国农产品加工业上市公司绩效评价研究[D].成都:四川农业大学.
[6]董晔(2014).高中生数学学习效率的个体差异研究[D].北京:首都师范大学.
[7]段炼(2009).浅论如何在网络远程教育学习中提高学习效率[J].湖南邮电职业技术学院学报,8(2):75-78.
[8]段明芳(2016).基于数据包络分析模型的学生数学学习效率评价[J].中学数学教学参考旬刊,(36):59-61.
[9]华建新(2008).远程开放教育提高网上自主学习效率的研究[J].江西广播电视大学学报,37(1):54-57.
[10]姜秀丽,江孝感(2008).DEA方法在学习成绩相对有效性评价中的应用[J].价值工程,27(11):23-25.
[11]科埃利(2008).效率与生产率分析引论[M].王忠玉译.北京:中国人民大学出版社:267-290.
[12]李金霞(2006).基于DEA方法的学生学习相对有效性评价[D].北京:中国地质大学.
[13]马占新(2013).偏序集与数据包络分析[M].北京:科学出版社:75-161.
[14]斯特凡·斯莱特,斯万克·约克西莫维奇,维特梅·科万诺维奇,莱恩·贝克,德拉甘·加斯维奇(2018).教育数据挖掘工具综述[J].崔鑫,王靖译.开放学习研究,23(1):37-47.
[15]单文涛(2018).基于DEA模型的大学生学习效率评价体系研究[J].哈尔滨职业技术学院学报,(1):68-71.
[16]汪世蓉(2010).基于动态DEA学习效果评价模型研究[J].统计与决策,(8):57-59.
[17]王香柯,王金柱(2002).基于数据包络分析模型的学习效率评价研究[J].陕西教育学院学报,18(1):66-68.
[18]魏权龄(2004).数据包络分析[M].北京:科学出版社:20-83.
[19]夏曼(2012).学习平台与远程学习者有效学习的研究[D].济南:山东师范大学.
[20]于嘉天,张华(2013).基于DEA的中学生学习效率评价[J].中国校外教育旬刊,(z1):169-170.
[21]袁莺楹,刘红梅,葛文璇(2013).基于DEA方法的高校课程学习评价研究[J].高教论坛,(6):91-94.
[22]詹棠森,程航,杨利华(2018).基于灰色关联的DEA模型在大学生学习效率评价中应用[J].内江科技,(3):35-36.
[23]张巧红(2017).基于DEA模型的高等代数分层次教学探析[J].长治学院学报,34(2):77-79.
[24]郑勤华,曹莉,陈丽,吴云峰(2013).远程学习者学习绩效影响因素研究[J].开放教育研究,19(6):88-94.
[25]Abramo,G.,D’Angelo,C.A.,&Pugini,F.(2008).The measurement of Italian universities’research productivity by a non parametric-bibliometric methodology[J].Scientometrics,76(2):225-244.
[26]Fuentes,R.,Fuster,B.,&Lillo-Ba?uls,A.(2015).A th ree-stage DEA model to evaluate lear ning-teaching technical effi ciency:Key performance indicators and contextual variables[J].Expert Systems with Applications,(48):89-99.
[27]Jalalvand,Z.&Navabakhsh,M.(2017).Evaluation of the Performance of the system’s distance learning education in Iran education system using data envelopment analysis[J].Palma Journal,(16):80-90.
[28]Kuzilek,J.,Hlosta,M.,&Zdrahal,Z.(2017).Open university learning analytics dataset[DB/OL].(2017-01-07)[2018-06-03].https://analyse.kmi.open.ac.uk/open_dataset.
[29]Lidia Angulo Meza,Jo?o Carlos Correia Baptista Soares de Mello,&Silvio Figueiredo Gomes Junior(2012).Benchmarking distance learning centers with a multiobjective data envelopment analysis model[EB/OL].[2019-3-11].https://www.intechopen.com/books/advanced-topics-in-appliedoperations-management/benchmarking-distance-learningcentres-with-a-multiobjective-data-envelopment-analysismodel.
[30]Nguyen,Q.,Huptych,M.,&Rienties,B.(2018).Linking students’timing of engagement to learning design and academic performance[C]//Proceedings of the 8th International Conference on Learning Analytics and Knowledge.New York:ACM:141-150.
[31]Yang,X.M.,Shieh,C.J.,&Wu,W.C.(2014).Measuring distance learning performance with data envelopment analysis[J].Eurasia Journal of Mathematics Science&Technology Education,10(6):559-564.
[2]鲍平平(2007b).DEA在网络学习相对有效性评价中的应用[J].现代远程教育研究,(6):65-68.
[3]陈美华(2012).基于PCA-DEA的英语有效学习过程评价模型研究[J].东南大学学报(哲学社会科学版),14(6):108-111.
[4]成刚(2014).数据包络分析方法与MaxDEA软件[M].北京:知识产权出版社:210-232.
[5]邓可(2015).基于DEA-BCC模型的我国农产品加工业上市公司绩效评价研究[D].成都:四川农业大学.
[6]董晔(2014).高中生数学学习效率的个体差异研究[D].北京:首都师范大学.
[7]段炼(2009).浅论如何在网络远程教育学习中提高学习效率[J].湖南邮电职业技术学院学报,8(2):75-78.
[8]段明芳(2016).基于数据包络分析模型的学生数学学习效率评价[J].中学数学教学参考旬刊,(36):59-61.
[9]华建新(2008).远程开放教育提高网上自主学习效率的研究[J].江西广播电视大学学报,37(1):54-57.
[10]姜秀丽,江孝感(2008).DEA方法在学习成绩相对有效性评价中的应用[J].价值工程,27(11):23-25.
[11]科埃利(2008).效率与生产率分析引论[M].王忠玉译.北京:中国人民大学出版社:267-290.
[12]李金霞(2006).基于DEA方法的学生学习相对有效性评价[D].北京:中国地质大学.
[13]马占新(2013).偏序集与数据包络分析[M].北京:科学出版社:75-161.
[14]斯特凡·斯莱特,斯万克·约克西莫维奇,维特梅·科万诺维奇,莱恩·贝克,德拉甘·加斯维奇(2018).教育数据挖掘工具综述[J].崔鑫,王靖译.开放学习研究,23(1):37-47.
[15]单文涛(2018).基于DEA模型的大学生学习效率评价体系研究[J].哈尔滨职业技术学院学报,(1):68-71.
[16]汪世蓉(2010).基于动态DEA学习效果评价模型研究[J].统计与决策,(8):57-59.
[17]王香柯,王金柱(2002).基于数据包络分析模型的学习效率评价研究[J].陕西教育学院学报,18(1):66-68.
[18]魏权龄(2004).数据包络分析[M].北京:科学出版社:20-83.
[19]夏曼(2012).学习平台与远程学习者有效学习的研究[D].济南:山东师范大学.
[20]于嘉天,张华(2013).基于DEA的中学生学习效率评价[J].中国校外教育旬刊,(z1):169-170.
[21]袁莺楹,刘红梅,葛文璇(2013).基于DEA方法的高校课程学习评价研究[J].高教论坛,(6):91-94.
[22]詹棠森,程航,杨利华(2018).基于灰色关联的DEA模型在大学生学习效率评价中应用[J].内江科技,(3):35-36.
[23]张巧红(2017).基于DEA模型的高等代数分层次教学探析[J].长治学院学报,34(2):77-79.
[24]郑勤华,曹莉,陈丽,吴云峰(2013).远程学习者学习绩效影响因素研究[J].开放教育研究,19(6):88-94.
[25]Abramo,G.,D’Angelo,C.A.,&Pugini,F.(2008).The measurement of Italian universities’research productivity by a non parametric-bibliometric methodology[J].Scientometrics,76(2):225-244.
[26]Fuentes,R.,Fuster,B.,&Lillo-Ba?uls,A.(2015).A th ree-stage DEA model to evaluate lear ning-teaching technical effi ciency:Key performance indicators and contextual variables[J].Expert Systems with Applications,(48):89-99.
[27]Jalalvand,Z.&Navabakhsh,M.(2017).Evaluation of the Performance of the system’s distance learning education in Iran education system using data envelopment analysis[J].Palma Journal,(16):80-90.
[28]Kuzilek,J.,Hlosta,M.,&Zdrahal,Z.(2017).Open university learning analytics dataset[DB/OL].(2017-01-07)[2018-06-03].https://analyse.kmi.open.ac.uk/open_dataset.
[29]Lidia Angulo Meza,Jo?o Carlos Correia Baptista Soares de Mello,&Silvio Figueiredo Gomes Junior(2012).Benchmarking distance learning centers with a multiobjective data envelopment analysis model[EB/OL].[2019-3-11].https://www.intechopen.com/books/advanced-topics-in-appliedoperations-management/benchmarking-distance-learningcentres-with-a-multiobjective-data-envelopment-analysismodel.
[30]Nguyen,Q.,Huptych,M.,&Rienties,B.(2018).Linking students’timing of engagement to learning design and academic performance[C]//Proceedings of the 8th International Conference on Learning Analytics and Knowledge.New York:ACM:141-150.
[31]Yang,X.M.,Shieh,C.J.,&Wu,W.C.(2014).Measuring distance learning performance with data envelopment analysis[J].Eurasia Journal of Mathematics Science&Technology Education,10(6):559-564.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |