Design and Implementation of A Fast Data Visualization Framework For E-learning Behavior Analysis

HONG Dan-dan; LI Fei; YAO Lei; XU Mo; SUO Zhi-hai

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Design and Implementation of A Fast Data Visualization Framework For E-learning Behavior Analysis

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- Design and Implementation of A Fast Data Visualization Framework For E-learning Behavior Analysis
- Vol. 35, Issue 7, Pages: 6-12(2018)
- 作者机构：
  
  西安交通大学网络信息中心,陕西,西安,710049
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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洪丹丹, 李飞, 姚磊, 徐墨, 锁志海. 在线学习行为分析数据可视化快速开发框架设计与实践[J]. 微电子学与计算机, 2018, 35(7): 6-12.

HONG Dan-dan, LI Fei, YAO Lei, et al. Design and Implementation of A Fast Data Visualization Framework For E-learning Behavior Analysis[J]. 2018, 35(7): 6-12.
洪丹丹, 李飞, 姚磊, 徐墨, 锁志海. 在线学习行为分析数据可视化快速开发框架设计与实践[J]. 微电子学与计算机, 2018, 35(7): 6-12. DOI：

HONG Dan-dan, LI Fei, YAO Lei, et al. Design and Implementation of A Fast Data Visualization Framework For E-learning Behavior Analysis[J]. 2018, 35(7): 6-12. DOI：

摘要

为了充分挖掘高校在线学习平台中的用户学习行为

发挥各类用户行为数据的价值并增强各类在线统计数据的可读性

以统计学理论为基础

结合JFinal轻量级框架及ECharts前端数据可视化技术研究设计了一款可扩展、通用性强的轻量级数据分析可视化快速开发框架.并以典型的在线学习平台中用户最喜爱课程分析实例为背景

对其部分数据库模型、算法指标模型、前端数据可视化及后端数据处理等关键技术进行了详细研究及实现.以框架在西安交通大学在线学习平台中的实践结果表明

其可以满足数据分析快速可视化需求且支持数据关联分析和数据逐级下钻可视化.最后

对未来大数据环境下的高校学习平台用户学习行为分析提出建设意见.

Abstract

In order to analyze the userse-leaning behaviors in university campus

a fast and extensible development framework is designed and implemented that combined with JFinal lightweight framework and ECharts visualization technology. Based on the university e-leaning platform

a typical analysis theme was chosen as the development background

the key technologies such as algorithms model

front-end data visualization

database model and back-end data processing were studied and implemented in detail. The result of the implementation shows that the framework developed in this paper can support various visualization requirements of the data analysis in the e-learning platform. Meanwhile

the future constructions were put forward for the e-learning behavior analysis in big data environment.

关键词

Keywords

references

Kinnebrew J, Segedy J, Biswas G. Integrating model-driven and data-driven techniques for analyzing learning behaviors in open-ended learning environments[J]. IEEE Transactions on Learning Technologies, 2017, 10(2):140-153. DOI: 140

Wang J, Lv H Y, Cao B, et al. Application of educational data mining on analysis of students' online learning behavior[C]// International Conference on Image, Vision and Computing. China, Chengdu, IEEE, 2017.

Safaripour R, Khendek F, Glitho R, et al. A RESTfull architecture for enabling rapid development and deployment of companion robot applications[C]// International Conference on Computing, Networking and Communications. Hawaii, USA, 2014: 971-976.

耿秀丽, 马万元.考虑未知属性权重的区间直觉模糊VIKOR方法[J].计算机工程与应用, 2016, 52(12):1-5. DOI: 1

彭文辉, 杨宗凯, 黄克斌.网络学习行为分析及其模型研究[J].中国电化教育, 2006, 26(10):31-35. DOI: 31

韩玉焱. Java Web的JFinal开发框架研究[J].电子世界, 2017, 38(12):153-153. DOI: 153

郑幸源, 洪亲, 蔡坚勇, 等.基于AJAX异步传输技术与Echarts3技术的动态数据绘图实现[J].软件导刊, 2017, 16(3):143-145. DOI: 143

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