Research Progress on Educational Data Mining

doi:10.3778/j.issn.1002-8331.1903-0153

Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (14): 15-23.DOI: 10.3778/j.issn.1002-8331.1903-0153

Previous Articles Next Articles

Research Progress on Educational Data Mining

LI Yufan1, ZHANG Huifu2, LIU Shangli2, TANG Bing1

1.School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
2.Network Information Center, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

Online:2019-07-15 Published:2019-07-11

教育数据挖掘研究进展

李宇帆1，张会福2，刘上力2，唐兵1

1.湖南科技大学计算机科学与工程学院，湖南湘潭 411201
2.湖南科技大学网络信息中心，湖南湘潭 411201

Abstract

Abstract: Educational Data Mining（EDM） is an interdisciplinary research field which is concerned with three main areas：computer science, education and statistic. Its goal is to better understand students, the settings which they learn in and improve educational effect by exploring the unique data that comes from educational settings. In order to understand research progress on EDM, systematical overview of relative literatures from Web of Science and related research at home and abroad is made firstly. Then educational knowledge discovery and data mining process is introduced. Next, it discusses the data mining technology in the application of four categories in education domain. Finally, some typical instances in rapid development stage are analyzed and discussed.

Key words: educational big data, educational data mining, data mining

摘要： 教育数据挖掘（Educational Data Mining，EDM）是一门涉及计算机科学、教育学、统计学的交叉学科。它致力于探索来自教育环境的独特数据，其目的是更好地了解学生及其学习环境，从而提高教育成效。为了深入分析EDM的研究进展，从Web of Science库相关文献、国内外研究现状对EDM进行了系统性梳理，介绍了EDM的工作流程，把数据挖掘技术在教育领域的应用归纳为4类，对处于快速发展阶段的一些EDM典型案例进行了统计分析并讨论了其不足与发展趋势。

关键词: 教育大数据, 教育数据挖掘, 数据挖掘

LI Yufan1, ZHANG Huifu2, LIU Shangli2, TANG Bing1. Research Progress on Educational Data Mining[J]. Computer Engineering and Applications, 2019, 55(14): 15-23.

李宇帆1，张会福2，刘上力2，唐兵1. 教育数据挖掘研究进展[J]. 计算机工程与应用, 2019, 55(14): 15-23.

[1]	ZONG Xiaoping, TAO Zeze. Knowledge Tracing Model Based on Mastery Speed [J]. Computer Engineering and Applications, 2021, 57(6): 117-123.
[2]	GAO Tianyu, WANG Qingrong, YANG Lei. Data Mining Model Based on Attribute Dependability Enhancement of Rough Set [J]. Computer Engineering and Applications, 2021, 57(3): 87-93.
[3]	WU Hao, XU Xingjian, MENG Fanjun. Knowledge Graph-Assisted Multi-task Feature-Based Course Recommendation Algorithm [J]. Computer Engineering and Applications, 2021, 57(21): 132-139.
[4]	MA Yang, ZHAO Xujun. Multi-source Outlier Detection Algorithm Based on Relevant Subspace [J]. Computer Engineering and Applications, 2021, 57(17): 88-95.
[5]	ZHANG Nianpeng, WU Xu, ZHU Qiang. Entropy-Based Oversampling Framework [J]. Computer Engineering and Applications, 2021, 57(13): 96-101.
[6]	ZHANG Bowen, LIU Zhi, SANG Guoming. Anomaly Detection Algorithm Based on Kernel Density Fluctuation [J]. Computer Engineering and Applications, 2021, 57(12): 132-136.
[7]	RAO Jiawang, MA Ronghua. Improved Kernel Density Estimator Based Spatial Point Density Algorithm [J]. Computer Engineering and Applications, 2021, 57(11): 260-265.
[8]	WANG Jie, CHEN Zhigang, LIU Jialing, CHENG Hongbing. Privacy Behavior Mining Technology for Cloud Computing Based on Clustering [J]. Computer Engineering and Applications, 2020, 56(5): 80-84.
[9]	WANG Zilong, LI Jin, SONG Yafei. Improved K-means Algorithm Based on Distance and Weight [J]. Computer Engineering and Applications, 2020, 56(23): 87-94.
[10]	JI Wenlu, WANG Hailong, SU Guibin, LIU Lin. Review of Recommendation Methods Based on Association Rules Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 33-41.
[11]	YI Junyan, WU Boya, YONG Qiaoling. Research on Clustering Algorithm of Elastic Net with Weighted Characteristics [J]. Computer Engineering and Applications, 2020, 56(22): 55-65.
[12]	LIU Wenfen, MU Xiaodong, HUANG Yuehua. Anomaly Detection Method Based on Multi-resolution Grid [J]. Computer Engineering and Applications, 2020, 56(17): 78-85.
[13]	MENG Haidong1，2, SUN Xinjun2, SONG Yuchen1. Improved LOF Algorithm Based on Data Field [J]. Computer Engineering and Applications, 2019, 55(3): 154-158.
[14]	LEI Le, WANG Lizhen, XIAO Qing. Study on Fuzzy Mining Technology in Spatial Co-Location Pattern Mining [J]. Computer Engineering and Applications, 2019, 55(21): 158-166.
[15]	GUO Peng, CAI Cheng. Data Mining and Analysis of Students’ Score Based on Clustering and Association Algorithm [J]. Computer Engineering and Applications, 2019, 55(17): 169-179.

Research Progress on Educational Data Mining

教育数据挖掘研究进展

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics