Towards on the MOOCs knowledge discovery based on concept lattice

被引：0

作者：

Gao, Junfeng ^{[1
]}

Qu, Jingye ^{[1
]}

Xin, Zhong ^{[2
]}

机构：

[1] Beihua University, Jilin City, China

[2] Jilin Technology College of Electronic Information, Jilin City, China

来源：

International Journal of Multimedia and Ubiquitous Engineering | 2015年 / 10卷 / 05期

关键词：

Concept Lattices - Guiding principles - Knowledge discover - Knowledge organization - Knowledge structures - MOOCs clustering - Navigation methods - Structure characteristic;

D O I：

10.14257/ijmue.2015.10.5.26

中图分类号：

学科分类号：

摘要：

This paper puts forward the methods on MOOCs knowledge organization and discovery, adopts the method of Formal Concept Analysis, uses concept lattice as the support tool, and selects the data from “Coursera” to cluster the courses and mine the inner knowledge association among courses, so as to discover the connotative knowledge correlation among MOOCs on the same topic and the structure characteristics among courses. Finally, based on the supergraph of the MOOCs concept lattice, the paper puts forward the visualization navigation method of the courses and provides flexible guiding principles for the learners whose knowledge structure is unusual in various fields when choosing the courses, so as to promote the development and improvement of the MOOCs websites by the knowledge organization and knowledge discovery technology. © 2015 SERSC.

引用

页码：287 / 296

共 50 条

[31] Comprehensive knowledge discovery: Theory, concept and application
Sha, Zongyao
Bian, Fuling
Chen, Jiangping
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2002, 27 (04):
[32] Research on Knowledge Graphs with Concept Lattice Constraints
Lan, Ning
Yang, Shuqun
Yin, Ling
Gao, Yongbin
SYMMETRY-BASEL, 2021, 13 (12):
[33] Enhancing ontology concept design by knowledge discovery
Castano, Silvana
Ferrara, Alfio
DEXA 2007: 18TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2007, : 480 - +
[34] A new case-based classification using incremental concept lattice knowledge
Muangprathub, Jirapond
Boonjing, Veera
Pattaraintakorn, Puntip
DATA & KNOWLEDGE ENGINEERING, 2013, 83 : 39 - 53
[35] Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
Gong, Jibing
Wang, Shen
Wang, Jinlong
Feng, Wenzheng
Peng, Hao
Tang, Jie
Yu, Philip S.
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), 2020, : 79 - 88
[36] AGNE: Attentional Graph Convolutional Network Embedding for Knowledge Concept Recommendation in MOOCs
Chen, Jiahui
Meng, Dan
Gao, Xiangyun
Zhang, Liping
Kong, Chao
WEB INFORMATION SYSTEMS AND APPLICATIONS, WISA 2024, 2024, 14883 : 463 - 475
[37] Mining concept associations for knowledge discovery through concept chain queries
Jin, Wei
Srihari, Rohini K.
Wu, Xin
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2007, 4426 : 555 - +
[38] Towards DSS: Enhancing domain knowledge through knowledge discovery
Campbell, P. R. J.
Adamson, K.
Ford-Hutchinson, R.
2006 INNOVATIONS IN INFORMATION TECHNOLOGY, 2006, : 76 - 80
[39] Towards MOOCs scenaries based on Collaborative Learning Approaches
Claros, Ivan
Echeverria, Leovy
Cobos, Ruth
PROCEEDINGS OF 2015 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON), 2015, : 989 - 992
[40] Towards Geological Knowledge Discovery Using Vector-Based Semantic Similarity
Enkhsaikhan, Majigsuren
Liu, Wei
Holden, Eun-Jung
Duuring, Paul
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2018, 2018, 11323 : 224 - 237

← 1 2 3 4 5 →