如何理解MOOCs学习完成率——对MOOCs学习者留存问题研究的评析
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摘要
围绕如何界定MOOCs学习完成率、哪些学习过程变量有效影响/预测学习者学完MOOCs两个问题,文章对近五年发表的相关实证研究进行述评。分析发现,当前研究已逐渐正视MOOCs学习的特殊性,从不同角度对MOOCs完成率进行多元界定,反映出研究者对MOOCs学习过程及实效的重视。尽管诸多研究发现,影响学习者完成MOOCs的因素涉及学习者、学习环境、学习过程交互等多个方面,但这些研究大都侧重对学习者外显行为变量进行描述、观察和归纳,疏于从教与学的角度将学习行为数据与学习效果及思维品质的变化建立联系。研究认为,深入理解MOOCs学习留存问题,应充分考虑在线学习者学习需求和学习过程的复杂性。构建行为数据分析与学习理论延展的联结点,开展基于设计的研究,探索有效增进在线学习者的积极性、知识构建品质和深度理解水平的学习设计方案,或是未来的方向。
As for how to define the completion rate of MOOCs learning and which learning variables affect/predict learners' completion of MOOCs effectively, this paper reviews the relevant empirical studies published in the past five years. The results show that the current research has gradually paid attention to the particularity of MOOCs learning, and defined the completion rate of MOOCs from different perspectives, reflecting that researchers attach importance to the learning process and effectiveness of MOOCs. Although many studies have found that factors affecting learners' completion of MOOCs involve learners, learning environment, learning process interaction and other aspects, but most of those studies focus on the description, observation and generalization of learners' overt behavior variables, and neglect to establish a connection between learning behavior data and changes in learning effect and thinking quality from the perspective of teaching and learning. This study suggests that in order to understand the remaining problems of MOOCs learning, it is necessary to think over the learning needs and the complexity of the learning process of online learners. In the future, it is hoped to build a link between behavioral data analysis and the extension of learning theories, to carry out the design-based research, and explore learning design schemes that can effectively enhance the initiative of online learners, the quality of knowledge construction and the level of deep understanding.
As for how to define the completion rate of MOOCs learning and which learning variables affect/predict learners' completion of MOOCs effectively, this paper reviews the relevant empirical studies published in the past five years. The results show that the current research has gradually paid attention to the particularity of MOOCs learning, and defined the completion rate of MOOCs from different perspectives, reflecting that researchers attach importance to the learning process and effectiveness of MOOCs. Although many studies have found that factors affecting learners' completion of MOOCs involve learners, learning environment, learning process interaction and other aspects, but most of those studies focus on the description, observation and generalization of learners' overt behavior variables, and neglect to establish a connection between learning behavior data and changes in learning effect and thinking quality from the perspective of teaching and learning. This study suggests that in order to understand the remaining problems of MOOCs learning, it is necessary to think over the learning needs and the complexity of the learning process of online learners. In the future, it is hoped to build a link between behavioral data analysis and the extension of learning theories, to carry out the design-based research, and explore learning design schemes that can effectively enhance the initiative of online learners, the quality of knowledge construction and the level of deep understanding.
引文
[1] PAPPANO L. The year of the MOOC[J]. The New York times, 2012-11-04(26).
[2] FREITAS S I, MORGAN J, GIBSON D. Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision[J]. British journal of educational technology, 2015, 46(3):455-471.
[3]范逸洲,刘敏,欧阳嘉煜,汪琼.MOOCs中学习者流失问题的预测分析———基于24篇中英文文献的综述[J].中国远程教育,2018(4):5-14.
[4] ILLERIS K. How we learn:learning and non-learning in school and beyond[M]. London&New York:Routledge,2007.
[5]陈云帆,张铭.MOOCs课程学生流失现象分析与预警[J].工业和信息化教育,2014(9):30-36.
[6] SINHA T, JERMANN P, LI N, et al. Your click decides your fate:inferring information processing and attrition behavior from MOOC video clickstream interactions[DB/OL].(2014-09-16)[2018-11-20]. http://arxiv.org/abs/1407.7131.
[7] LIYANAGUNAWARDENA T R, PARSLOW P, WILLIAMS S. Dropout:Mooc participants'perspective[C]//Proceedings of the Second MOOC European Stakeholders Summit. Lausanne:EMOOCs, 2014.
[8] KIM J, GUO P J, SEATON D T, et al. Understanding in-video dropouts and interaction peaks in online lecture videos[C]//Proceedings of the First ACM Conference on Learning@Scale Conference. New York:ACM,2014.
[9] JIANG S, WILLIAMS A, SCHENKE K, et al. Predicting mooc performance with week 1 behavior[C]//Proceedings of the 7th International Conference on Educational Data Mining. London:EDM, 2014.
[10]蒋卓轩,张岩,李晓明.基于MOOC数据的学习行为分析与预测[J].计算机研究与发展,2015(3):614-628.
[11] KOEDINGER K R, KIM J, JIA J Z, et al. Learning is not a spectator sport:doing is better than watching for learning from a MOOC[C]//Proceedings of the second ACM conference on learning@scale. New York:ACM,2015.
[12] JORDAN K. Massive open online course completion rates revisited:assessment, length and attrition[J]. The international review of research in open and distributed learning, 2015, 16(3):341-358.
[13] HANSEN J D, REICH J. Democratizing education? Examining access and usage patterns in massive open online courses[J].Science, 2015, 350(6265):1245-1248.
[14] GREENE J A, OSWALD C A, POMERANTZ J. Predictors of retention and achievement in a massive open online course[J].American educational research journal,2015,52(5):925-955.
[15] ENGLE D, MANKOFF C, CARBREY J. Coursera’s introductory human physiology course:factors that characterize successful completion of a MOOC[J]. The international review of research in open and distributed learning, 2015, 16(2):46-68.
[16] ALRAIMI K M, ZO H, CIGANEK A P. Understanding the MOOCs continuance:the role of openness and reputation[J].Computers&education, 2015(80):28-38.
[17] XING W, CHEN X, STEIN J, et al. Temporal predication of dropouts in MOOCs:reaching the low hanging fruit through stacking generalization[J]. Computers in human behavior, 2016(58):119-129.
[18] SUNAR A, WHITE S, ABDULLAH N,et al. How learners'interactions sustain engagement:a mooc case study[J].IEEE transactions on learning technologies, 2016,10(1):475-487.
[19] PURSEL B K, ZHANG L, JABLOKOW K W, et al. Understanding MOOC students:motivations and behaviours indicative of MOOC completion[J]. Journal of computer assisted learning, 2016, 32(3):202-217.
[20] OAKLEY B, POOLE D, NESTOR M. Creating a sticky MOOC[J]. Online learning, 2016, 20(1):13-24.
[21] HONE K S, EL SAID G R. Exploring the factors affecting MOOC retention:a survey study[J]. Computers&education, 2016,98(c):157-168.
[22] FIDALGO-BLANCO譧, SEIN-ECHALUCE M L, GARC魱A-PE譙ALVO F J. From massive access to cooperation:lessons learned and proven results of a hybrid xmooc/cmooc pedagogical approach to moocs[J]. International journal of educational technology in higher education, 2016, 13:24. https://doi.org/10.1186/s41239-016-0024-z.
[23] EVANS B J, BAKER R B, DEE T S. Persistence patterns in massive open online courses(MOOCs)[J]. The journal of higher education,2016, 87(2):206-242.
[24] CROSSLEY S, PAQUETTE L, DASCALU M, et al. Combining click-stream data with NLP tools to better understand MOOC completion[C]//Proceedings of the Sixth International Conference on Learning Analytics&Knowledge, New York:ACM, 2016:6-14.
[25] BORRAS-GENE O, MARTINEZ-NUNEZ M, FIDALGO-BLANCO譧. New challenges for the motivation and learning in engineering education using gamification in MOOC[J]. International journal of engineering education, 2016, 32(1):501-512.
[26] BARBA P D, KENNEDY G E, AINLEY M. The role of students'motivation and participation in predicting performance in a MOOC[J]. Journal of computer assisted learning, 2016, 32(3):218-231.
[27]牟智佳,武法提.MOOCs学习结果预测指标探索与学习群体特征分析[J].现代远程教育研究,2017(3):58-66.
[28]王雪宇,邹刚,李骁.基于MOOC数据的学习者辍课预测研究[J].现代教育技术,2017,27(6):94-100.
[29] CHEN Y,ZHANG M. MOOC student dropout:pattern and prevention[C]//Proceedings of the ACM Turing 50th Celebration Conference.Shanghai:ACM,2017.
[30] SHAPIRO H B, LEE C H, ROTH N E W, et al. Understanding the massive open online course(MOOCs)student experience:An examination of attitudes, motivations, and barriers[J]. Computers&education,2017(110):35-50.
[31]张刚要,李紫衣.基于质性分析的MOOCs高退学率归因研究[J].电化教育研究,2018,39(1):29-35.
[32] GREGORI E B, ZHANG J, GALV譧N-FERN譧NDEZ C, et al. Learner support in MOOCs:identifying variables linked to completion[J]. Computers&education, 2018,122(7):153-168.
[33] JOKSIMOVIC'S, POQUET O, KOVANOVIC'V, et al. How do we model learning at scale? a systematic review of research on MOOCs[J]. Review of educational research, 2018, 88(1):43-86.
[34] GONZ譧LEZ G, SKULTETY L. Teacher learning in a combined professional development intervention[J].Teaching and teacher education,2018(71):341-354.
[35] CSIKSZENTMIHALYI M. Finding flow:the psychology of engagement with everyday life[M]. New York:Basic Books,1997.
[36] DEBOER J, HO A D, STUMP G S, et al. Changing “course”:reconceptualizing educational variables for massive open online courses[J]. Educatioanl researchera, 2014, 43(2):74-84.
[37]李曼丽,黄振中.MOOCs平台大数据的教育实证[J].科学通报,2015(1):570-580.
[38]陈伯栋,黄天慧.解析学习分析学:一次撬动冰山的尝试[J].开放教育研究,2017,22(4):1-9.
[39]万海鹏,余胜泉.基于学习元平台的学习认知地图构建[J].电化教育研究,2017(9):83-88.
(1)MOOCs是一种非正式的网络学习环境。在MOOCs学习期间,注册学习者可以随时放弃(Dropout)课程学习。这种开放性使得MOOCs学习中的学习者留存现象受到特别关注。研究者在讨论这一现象时常用完成率或弃课率、学习者留存问题或学习者流失问题等不同术语展开探讨。实际上在MOOCs学习环境中,完成-弃课、留存-流失是成对互补的概念。“MOOC完成率”在字面上的含义为一门MOOC中完成学习的人数占其学习者总数的比率,为便于讨论,本文主要使用“完成率”概括指称MOOCs的完成情况,用学习者留存描述他们参与MOOCs学习期间选择坚持学习或弃课的现象。
(1)国际主流期刊包括:Computers&Education、Computers in Human Behavior、British Journal of Educational Technology、Journal of Computer Assisted Learning、Internet and Higher Education等。国内期刊包括:《现代教育技术》《开放教育研究》《电化教育研究》《中国远程教育》等。国际会议包括:International conference on learning analytics and knowledge、ACM Conference on Learning@Scale Conference等。
(1)所谓“跟帖者(Follow)”,是那些至少发过一条评论的活跃学习者,或者至少跟过一次别的学习者所发的帖的人。
[2] FREITAS S I, MORGAN J, GIBSON D. Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision[J]. British journal of educational technology, 2015, 46(3):455-471.
[3]范逸洲,刘敏,欧阳嘉煜,汪琼.MOOCs中学习者流失问题的预测分析———基于24篇中英文文献的综述[J].中国远程教育,2018(4):5-14.
[4] ILLERIS K. How we learn:learning and non-learning in school and beyond[M]. London&New York:Routledge,2007.
[5]陈云帆,张铭.MOOCs课程学生流失现象分析与预警[J].工业和信息化教育,2014(9):30-36.
[6] SINHA T, JERMANN P, LI N, et al. Your click decides your fate:inferring information processing and attrition behavior from MOOC video clickstream interactions[DB/OL].(2014-09-16)[2018-11-20]. http://arxiv.org/abs/1407.7131.
[7] LIYANAGUNAWARDENA T R, PARSLOW P, WILLIAMS S. Dropout:Mooc participants'perspective[C]//Proceedings of the Second MOOC European Stakeholders Summit. Lausanne:EMOOCs, 2014.
[8] KIM J, GUO P J, SEATON D T, et al. Understanding in-video dropouts and interaction peaks in online lecture videos[C]//Proceedings of the First ACM Conference on Learning@Scale Conference. New York:ACM,2014.
[9] JIANG S, WILLIAMS A, SCHENKE K, et al. Predicting mooc performance with week 1 behavior[C]//Proceedings of the 7th International Conference on Educational Data Mining. London:EDM, 2014.
[10]蒋卓轩,张岩,李晓明.基于MOOC数据的学习行为分析与预测[J].计算机研究与发展,2015(3):614-628.
[11] KOEDINGER K R, KIM J, JIA J Z, et al. Learning is not a spectator sport:doing is better than watching for learning from a MOOC[C]//Proceedings of the second ACM conference on learning@scale. New York:ACM,2015.
[12] JORDAN K. Massive open online course completion rates revisited:assessment, length and attrition[J]. The international review of research in open and distributed learning, 2015, 16(3):341-358.
[13] HANSEN J D, REICH J. Democratizing education? Examining access and usage patterns in massive open online courses[J].Science, 2015, 350(6265):1245-1248.
[14] GREENE J A, OSWALD C A, POMERANTZ J. Predictors of retention and achievement in a massive open online course[J].American educational research journal,2015,52(5):925-955.
[15] ENGLE D, MANKOFF C, CARBREY J. Coursera’s introductory human physiology course:factors that characterize successful completion of a MOOC[J]. The international review of research in open and distributed learning, 2015, 16(2):46-68.
[16] ALRAIMI K M, ZO H, CIGANEK A P. Understanding the MOOCs continuance:the role of openness and reputation[J].Computers&education, 2015(80):28-38.
[17] XING W, CHEN X, STEIN J, et al. Temporal predication of dropouts in MOOCs:reaching the low hanging fruit through stacking generalization[J]. Computers in human behavior, 2016(58):119-129.
[18] SUNAR A, WHITE S, ABDULLAH N,et al. How learners'interactions sustain engagement:a mooc case study[J].IEEE transactions on learning technologies, 2016,10(1):475-487.
[19] PURSEL B K, ZHANG L, JABLOKOW K W, et al. Understanding MOOC students:motivations and behaviours indicative of MOOC completion[J]. Journal of computer assisted learning, 2016, 32(3):202-217.
[20] OAKLEY B, POOLE D, NESTOR M. Creating a sticky MOOC[J]. Online learning, 2016, 20(1):13-24.
[21] HONE K S, EL SAID G R. Exploring the factors affecting MOOC retention:a survey study[J]. Computers&education, 2016,98(c):157-168.
[22] FIDALGO-BLANCO譧, SEIN-ECHALUCE M L, GARC魱A-PE譙ALVO F J. From massive access to cooperation:lessons learned and proven results of a hybrid xmooc/cmooc pedagogical approach to moocs[J]. International journal of educational technology in higher education, 2016, 13:24. https://doi.org/10.1186/s41239-016-0024-z.
[23] EVANS B J, BAKER R B, DEE T S. Persistence patterns in massive open online courses(MOOCs)[J]. The journal of higher education,2016, 87(2):206-242.
[24] CROSSLEY S, PAQUETTE L, DASCALU M, et al. Combining click-stream data with NLP tools to better understand MOOC completion[C]//Proceedings of the Sixth International Conference on Learning Analytics&Knowledge, New York:ACM, 2016:6-14.
[25] BORRAS-GENE O, MARTINEZ-NUNEZ M, FIDALGO-BLANCO譧. New challenges for the motivation and learning in engineering education using gamification in MOOC[J]. International journal of engineering education, 2016, 32(1):501-512.
[26] BARBA P D, KENNEDY G E, AINLEY M. The role of students'motivation and participation in predicting performance in a MOOC[J]. Journal of computer assisted learning, 2016, 32(3):218-231.
[27]牟智佳,武法提.MOOCs学习结果预测指标探索与学习群体特征分析[J].现代远程教育研究,2017(3):58-66.
[28]王雪宇,邹刚,李骁.基于MOOC数据的学习者辍课预测研究[J].现代教育技术,2017,27(6):94-100.
[29] CHEN Y,ZHANG M. MOOC student dropout:pattern and prevention[C]//Proceedings of the ACM Turing 50th Celebration Conference.Shanghai:ACM,2017.
[30] SHAPIRO H B, LEE C H, ROTH N E W, et al. Understanding the massive open online course(MOOCs)student experience:An examination of attitudes, motivations, and barriers[J]. Computers&education,2017(110):35-50.
[31]张刚要,李紫衣.基于质性分析的MOOCs高退学率归因研究[J].电化教育研究,2018,39(1):29-35.
[32] GREGORI E B, ZHANG J, GALV譧N-FERN譧NDEZ C, et al. Learner support in MOOCs:identifying variables linked to completion[J]. Computers&education, 2018,122(7):153-168.
[33] JOKSIMOVIC'S, POQUET O, KOVANOVIC'V, et al. How do we model learning at scale? a systematic review of research on MOOCs[J]. Review of educational research, 2018, 88(1):43-86.
[34] GONZ譧LEZ G, SKULTETY L. Teacher learning in a combined professional development intervention[J].Teaching and teacher education,2018(71):341-354.
[35] CSIKSZENTMIHALYI M. Finding flow:the psychology of engagement with everyday life[M]. New York:Basic Books,1997.
[36] DEBOER J, HO A D, STUMP G S, et al. Changing “course”:reconceptualizing educational variables for massive open online courses[J]. Educatioanl researchera, 2014, 43(2):74-84.
[37]李曼丽,黄振中.MOOCs平台大数据的教育实证[J].科学通报,2015(1):570-580.
[38]陈伯栋,黄天慧.解析学习分析学:一次撬动冰山的尝试[J].开放教育研究,2017,22(4):1-9.
[39]万海鹏,余胜泉.基于学习元平台的学习认知地图构建[J].电化教育研究,2017(9):83-88.
(1)MOOCs是一种非正式的网络学习环境。在MOOCs学习期间,注册学习者可以随时放弃(Dropout)课程学习。这种开放性使得MOOCs学习中的学习者留存现象受到特别关注。研究者在讨论这一现象时常用完成率或弃课率、学习者留存问题或学习者流失问题等不同术语展开探讨。实际上在MOOCs学习环境中,完成-弃课、留存-流失是成对互补的概念。“MOOC完成率”在字面上的含义为一门MOOC中完成学习的人数占其学习者总数的比率,为便于讨论,本文主要使用“完成率”概括指称MOOCs的完成情况,用学习者留存描述他们参与MOOCs学习期间选择坚持学习或弃课的现象。
(1)国际主流期刊包括:Computers&Education、Computers in Human Behavior、British Journal of Educational Technology、Journal of Computer Assisted Learning、Internet and Higher Education等。国内期刊包括:《现代教育技术》《开放教育研究》《电化教育研究》《中国远程教育》等。国际会议包括:International conference on learning analytics and knowledge、ACM Conference on Learning@Scale Conference等。
(1)所谓“跟帖者(Follow)”,是那些至少发过一条评论的活跃学习者,或者至少跟过一次别的学习者所发的帖的人。
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