优秀的慕课学习者如何学习——慕课学习行为模式挖掘
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摘要
基于中国大学MOOC平台上《翻转课堂教学法》MOOC中17204名学习者的行为日志数据,在为学习者的页面访问记录赋予有意义的行为编码标签,建立其学习路径模型后,使用统计和共现分析的方法,研究其中优秀学习者的学习行为模式特点。研究发现,优秀学习者总行为序列长度显著高于其他学习者,上线学习的次数显著地多于其他学习者;但是每次上线学习发生的有意义的交互行为的数量与其他学习者相仿,学习时长也相仿;其参与和回答教师的提问、复习已学过的内容、参与论坛互动的行为在其总学习行为中占比更多,而学习全新内容、参与测验、把握全局等行为的占比较少。研究用共现分析的方法分析了学习者每次上线产生的行为之间的共现关系,发现优秀的慕课学习者在学习新内容时更少发生走神和中断的情况,且其每次上线的目的更鲜明,学习主题更突出。研究的发现揭示了优秀慕课学习者学习行为模式的特征,有助于改进慕课教学。此外,共现分析的方法也为行为数据的挖掘提供了新的思路。
On the basis of log data of 17,204 learners' behavior generated in MOOC of Pedagogics in Flipped Classroom on ICOURSE163, this study tags the learner's records of accessing web pages with semantic codes for modelling. Employing statistic methods and the co-occurrence analysis, the study explores the characteristics of those good MOOC learners' behavior patterns.Their time duration and the length of the behavior sequence as well are significantly larger than others. Nevertheless, the average time duration as well as the behavior sequence length in each time of their logging on the platform show no significant difference compared to others. Proportions of their behaviors involved in the instructors' questions, reviewing the lesson and participating in forum interaction are higher while learning new contents, and taking quizzes and viewing the syllabus is lower than the other learners. The study adopts method of co-occurrence analysis and finds that the good MOOC learners are less likely to be interrupted when learning new contents, and they possess more recognizable incentives when they log on to learn. The findings reveal the characteristics of good MOOC learners' learning behavior pattern and will contribute to the improvement of teaching and learning on MOOC platforms. Additionally,the method of co-occurrence analysis also provides a new perspective to interpret behavior data.
On the basis of log data of 17,204 learners' behavior generated in MOOC of Pedagogics in Flipped Classroom on ICOURSE163, this study tags the learner's records of accessing web pages with semantic codes for modelling. Employing statistic methods and the co-occurrence analysis, the study explores the characteristics of those good MOOC learners' behavior patterns.Their time duration and the length of the behavior sequence as well are significantly larger than others. Nevertheless, the average time duration as well as the behavior sequence length in each time of their logging on the platform show no significant difference compared to others. Proportions of their behaviors involved in the instructors' questions, reviewing the lesson and participating in forum interaction are higher while learning new contents, and taking quizzes and viewing the syllabus is lower than the other learners. The study adopts method of co-occurrence analysis and finds that the good MOOC learners are less likely to be interrupted when learning new contents, and they possess more recognizable incentives when they log on to learn. The findings reveal the characteristics of good MOOC learners' learning behavior pattern and will contribute to the improvement of teaching and learning on MOOC platforms. Additionally,the method of co-occurrence analysis also provides a new perspective to interpret behavior data.
引文
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[16]Ho,D.E.,Imai,K.,et al.MatchIt:nonparametric preprocessing for parametric causal inference[J].Journal of Statistical Software,2011,42(8):1-28.
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[18]牛温佳.用户网络行为画像[M].北京:电子工业出版社,2016.
[19]Fredricks J A,Blumenfeld P C,et al.School engagement:Potential of the concept,state of the evidence[J].Review of educational research,2004,74(1):59-109.
[2]教育部办公厅.教育部办公厅关于印发《2018年教育信息化和网络安全工作要点》的通知[DB/OL].http://www.moe.edu.cn/srcsite/A16/s3342/201803/t20180313_329823.html,2018-09-04.
[3]王应解,冯策等.我国高校慕课教育中的问题分析与对策[J].中国电化教育,2015,(6):80-85.
[4]任博洋.英国“慕课”的支持策略和学习方式研究[J].北京广播电视大学学报,2015(1):36-39.
[5]樊文强.基于关联主义的大规模网络开放课程(MOOC)及其学习支持[J].远程教育杂志,2012,30(3):31-36.
[6]贾积有,于悦洋.学习活动指数LAI及在线学习活动指数OLAI的具体分析[J].中国远程教育,2017,(04):15-22.
[7]杨现民,王怀波等.滞后序列分析法在学习行为分析中的应用[J].中国电化教育,2016,(02):17-23.
[8]Maldonado J,Pérez-Sanagustín M,et al.Mining Theory-Based Patterns from Big Data:Identifying Self-Regulated Learning Strategies in Massive Open Online Courses[J].Computers in Human Behavior,2018,(80):179-196.
[9]Davis D,Chen G,et al.Gauging MOOC Learners'Adherence to the Designed Learning Path[J].EDM,2016,(9):54-61.
[10]贾积有,缪静敏等.MOOC学习行为及效果的大数据分析--以北大6门MOOC为例[J].工业和信息化教育,2014,(9):23-29.
[11]李爽,钟瑶等.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,2017,(3):88-95.
[12]王改花,傅钢善.数据挖掘视角下网络学习者行为特征聚类分析[J].现代远程教育研究,2018,(4):106-112.
[13]Vaessen B E,Prins F J,et al.University students'achievement goals and help-seeking strategies in an intelligent tutoring system[J].Computers&Education,2014,72(C):196-208.
[14]张志园.中学生学习疲劳的问卷编制与干预研究[D].大同:山西师范大学,2013.
[15]P.R.Rosenbaum,D.B.Rubin.Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome[J].Journal of the Royal Statistical Society.Series B(Methodological),1983,45(2):212-218.
[16]Ho,D.E.,Imai,K.,et al.MatchIt:nonparametric preprocessing for parametric causal inference[J].Journal of Statistical Software,2011,42(8):1-28.
[17]Randolph J J,Falbe K,et al.A Step-by-Step Guide to Propensity Score Matching in R[J].Practical Assessment Research&Evaluation,2014,(19):1-6.
[18]牛温佳.用户网络行为画像[M].北京:电子工业出版社,2016.
[19]Fredricks J A,Blumenfeld P C,et al.School engagement:Potential of the concept,state of the evidence[J].Review of educational research,2004,74(1):59-109.