学习云空间中基于大数据分析的学情预测研究
|
摘要
随着学习云空间应用的不断深入,海量过程性数据与富媒体资源使其呈现为典型大数据特征环境。文章对学习云空间的大数据特征、分析方法及其带来的机遇进行了深入的分析,并依据学习云空间传统数据及其过程性数据提出一种学情预测模型及其实现方法。在模型构建过程中,通过计算各学情因素的Gini增益,实现学习效果影响程度的判别,并提出改进的并行随机森林算法,以世界大学城系统平台为支撑进行学习预测与效果检验。结果表明,该方法较为有效地实现了大数据环境下学习云空间的学情预测,并为学习云空间中基于大数据技术的智能服务提供了一种可行的参考方案。
With the deepening application of cloud learning space, the cloud learning space is becoming a typical environment of big data because of massive process data and rich media resources. This paper firstly analyzes the characteristics, analysis methods and opportunities of big data in cloud learning space, and then proposes a learning condition prediction model and its implementation method based on the traditional data and process data in cloud learning space. During the model building, the Gini Gain of every learning condition factor is calculated to distinguish their impacts on learning effect. After that, an improved parallel algorithm of Random Forests is purposed to predict learning situation and verify its effects in WordUC platform. The results show that this method can effectively predict the learning situation of cloud learning space in big data environment and provide a feasible reference scheme for big data-based intelligent service in cloud learning space.
With the deepening application of cloud learning space, the cloud learning space is becoming a typical environment of big data because of massive process data and rich media resources. This paper firstly analyzes the characteristics, analysis methods and opportunities of big data in cloud learning space, and then proposes a learning condition prediction model and its implementation method based on the traditional data and process data in cloud learning space. During the model building, the Gini Gain of every learning condition factor is calculated to distinguish their impacts on learning effect. After that, an improved parallel algorithm of Random Forests is purposed to predict learning situation and verify its effects in WordUC platform. The results show that this method can effectively predict the learning situation of cloud learning space in big data environment and provide a feasible reference scheme for big data-based intelligent service in cloud learning space.
引文
[1]杨现民,赵鑫硕,刘雅馨,潘青青,陈世超.网络学习空间的发展:内涵、阶段与建议[J].中国电化教育,2016(4):30-36.
[2]谢晨,胡惠闵.学情分析中“学情”的理解[J].全球教育展望,2015,44(2):20-27.
[3]陈瑶.学情分析研究综述[J].当代教育理论与实践,2014,6(6):21-23.
[4]黄昌勤,王希哲,张冬冬,梅晓勇,周宇文.学习云空间支持下的研究性学习研究与实践[J].中国电化教育,2015(8):21-28.
[5]傅钢善,佟海静.网络环境下有效学习评价指标体系构建研究[J].电化教育研究,2016(8):23-30.
[6]李兴保,徐进,刘敏,赵可云.虚拟学习社区学习评价指标体系的设计[J].中国电化教育,2016(11):61-67,93.
[7]方海光,罗金萍,陈俊达,杜婧敏.基于教育大数据的量化自我MOOC自适应学习系统研究[J].电化教育研究,2016,37(11):38-42,92.
[8] BISHOP C M. Pattern recognition and machine learning(Information Science and Statistics)[M]. New York:Springer. 2006.
[9]丁继红,刘华中.大数据环境下基于多维关联分析的学习资源精准推荐[J].电化教育研究,2018,39(2):53-59,66.
[10]余胜泉,李晓庆.基于大数据的区域教育质量分析与改进研究[J].电化教育研究,2017,38(7):5-12.
[11]陈明选,王诗佳.测评大数据支持下的学习反馈设计研究[J].电化教育研究,2018,39(3):35-42,61.
[12] BREIMAN L. Random forests[J]. Machine learning, 2001, 45(1):5-32.
[13]何克抗.大数据面面观[J].电化教育研究,2014,35(10):8-16,22.
[14]知网.情感分析用词语集(beta版)[EB/OL].[2007-10-22]. http://www.keenage.com/html/c_bulletin_2007.htm.
[15]谢幼如,李克东.教育技术学研究方法[M].北京:高等教育出版社,2017.
[16] COVER T M, THOMAS J A. Elements of information theory[M]. Hoboken:John Wiley&Sons, 2012.
[17] EFRON B. Bootstrap methods:another look at the Jackknife[J]. The annals of statistics, 1979, 7(1):1-26.
[2]谢晨,胡惠闵.学情分析中“学情”的理解[J].全球教育展望,2015,44(2):20-27.
[3]陈瑶.学情分析研究综述[J].当代教育理论与实践,2014,6(6):21-23.
[4]黄昌勤,王希哲,张冬冬,梅晓勇,周宇文.学习云空间支持下的研究性学习研究与实践[J].中国电化教育,2015(8):21-28.
[5]傅钢善,佟海静.网络环境下有效学习评价指标体系构建研究[J].电化教育研究,2016(8):23-30.
[6]李兴保,徐进,刘敏,赵可云.虚拟学习社区学习评价指标体系的设计[J].中国电化教育,2016(11):61-67,93.
[7]方海光,罗金萍,陈俊达,杜婧敏.基于教育大数据的量化自我MOOC自适应学习系统研究[J].电化教育研究,2016,37(11):38-42,92.
[8] BISHOP C M. Pattern recognition and machine learning(Information Science and Statistics)[M]. New York:Springer. 2006.
[9]丁继红,刘华中.大数据环境下基于多维关联分析的学习资源精准推荐[J].电化教育研究,2018,39(2):53-59,66.
[10]余胜泉,李晓庆.基于大数据的区域教育质量分析与改进研究[J].电化教育研究,2017,38(7):5-12.
[11]陈明选,王诗佳.测评大数据支持下的学习反馈设计研究[J].电化教育研究,2018,39(3):35-42,61.
[12] BREIMAN L. Random forests[J]. Machine learning, 2001, 45(1):5-32.
[13]何克抗.大数据面面观[J].电化教育研究,2014,35(10):8-16,22.
[14]知网.情感分析用词语集(beta版)[EB/OL].[2007-10-22]. http://www.keenage.com/html/c_bulletin_2007.htm.
[15]谢幼如,李克东.教育技术学研究方法[M].北京:高等教育出版社,2017.
[16] COVER T M, THOMAS J A. Elements of information theory[M]. Hoboken:John Wiley&Sons, 2012.
[17] EFRON B. Bootstrap methods:another look at the Jackknife[J]. The annals of statistics, 1979, 7(1):1-26.