基于大数据的大学生学习行为分析与研究
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摘要
在网络技术不断更新的大背景下,云学习环境逐步全球化。资源日益丰富的图书馆、Web 2. 0和MOOC(慕课)为网络学习提供了新的途径与平台,刺激了大学生的求知欲望,自主学习已经成为大学生高效学习的主旋律。基于网络数据集,运用大数据分析方法建立数学关系,找出相关因子,研究了同时期大学生学习行为的共性与个性。研究发现,大学生主要根据"个人兴趣"和"课程实用性"进行自主学习,且学习的自主性得到了有效的提高;同时,在线学习对学生具有极大的吸引力,但学生的辍学行为等也是值得注意的问题。大数据分析对于大学生学习行为的分析与研究有着积极的辅助性及推动性作用,且大数据分析结论可为教学管理部门进一步提高大学生的学习成绩和学术水平出谋划策。
The cloud learning environment has been gradually globalized against the context of constantly updated network technology. The increasingly growing library resources,the advent of Web 2. 0 and MOOC( massive open online courses),which provides the new means and platform for network learning,have stimulated the students' thirst for knowledge. Nowadays,autonomous learning has become the theme of efficient learning for college students. Based on network data set,the mathematical relationship is built by using big data analysis method and the correlation factor is found,which can be used to study the commonness and personality of the students' learning behaviors in the same period. The study has found that the university students learn autonomously according to their " personal interest" and " course practicality",and their learning autonomy has been improved a lot. At the same time,online learning is a great attraction to the students,and students' dropout is worth paying attention to. Big data analysis has played the supporting and motivating role in the analysis and study of students' learning behaviors,which can provide reference for the university teaching administration department to improve the university students academic achievement.
The cloud learning environment has been gradually globalized against the context of constantly updated network technology. The increasingly growing library resources,the advent of Web 2. 0 and MOOC( massive open online courses),which provides the new means and platform for network learning,have stimulated the students' thirst for knowledge. Nowadays,autonomous learning has become the theme of efficient learning for college students. Based on network data set,the mathematical relationship is built by using big data analysis method and the correlation factor is found,which can be used to study the commonness and personality of the students' learning behaviors in the same period. The study has found that the university students learn autonomously according to their " personal interest" and " course practicality",and their learning autonomy has been improved a lot. At the same time,online learning is a great attraction to the students,and students' dropout is worth paying attention to. Big data analysis has played the supporting and motivating role in the analysis and study of students' learning behaviors,which can provide reference for the university teaching administration department to improve the university students academic achievement.
引文
[1]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,2015,52(3):614-628.
[2]严婷,文欣秀,赵嘉豪,等.基于Python的可视化数据分析平台设计与实现[J].计算机时代,2017(12):54-56.
[3]秦科,刘贵松.面向Python应用的大学教学改革初探[J].计算机教育,2017(9):21-25.
[4]贾程有,缪静敏,汪琼. 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.
[5]孙笑音,周围.基于MOOC的大数据分析技术[J].计算机与现代化,2017(4):89-93.
[6]陈雷.大数据学习分析的研究与应用——以浙江省教师教育MOOC培训平台的课程为例[J].现代教育技术,2016,26(8):109-115.
[7]张强. MOOC学习者学习行为聚类分析[J].通化师范学院学报,2015(4):37-39.
[2]严婷,文欣秀,赵嘉豪,等.基于Python的可视化数据分析平台设计与实现[J].计算机时代,2017(12):54-56.
[3]秦科,刘贵松.面向Python应用的大学教学改革初探[J].计算机教育,2017(9):21-25.
[4]贾程有,缪静敏,汪琼. 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.
[5]孙笑音,周围.基于MOOC的大数据分析技术[J].计算机与现代化,2017(4):89-93.
[6]陈雷.大数据学习分析的研究与应用——以浙江省教师教育MOOC培训平台的课程为例[J].现代教育技术,2016,26(8):109-115.
[7]张强. MOOC学习者学习行为聚类分析[J].通化师范学院学报,2015(4):37-39.