TY - GEN
T1 - Linked Open Data-Driven Contrastive Cognitive Subgraph Searching for Understanding Concepts in e-Learning
AU - Liu, Mengge
AU - Tian, Feng
AU - Yao, Yundong
AU - Ni, Yifu
AU - Chen, Yan
AU - Zhu, Haiping
AU - Zheng, Qinghua
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - Along with rise of e-learning, searching services as an important part of e-learning system has attracted more and more e-learners and researchers. According to theory of cognitive development, when e-learners are having problems to understand a concept during online learning, they prefer to search related information to form new cognitive structures or strengthen existing cognitive structures in order to improve learning efficiency. Although the existing search engines are extremely mature, they play a less role in cognitive structures for e-learners. Depending on the theory of constructivism, an effective mean to improve cognitive efficiency is to enhance the improvement and development of individual cognitive structure. Therefore, relying on thinking map, we develop a Linked Open Data-driven contrastive cognitive subgraph searching system for understanding concepts. Besides, during constructing contrastive cognitive subgraphs, we propose a method of calculating similarity between two keywords, whose accuracy and stability have been effectively improved compared with the other algorithm on LOD.
AB - Along with rise of e-learning, searching services as an important part of e-learning system has attracted more and more e-learners and researchers. According to theory of cognitive development, when e-learners are having problems to understand a concept during online learning, they prefer to search related information to form new cognitive structures or strengthen existing cognitive structures in order to improve learning efficiency. Although the existing search engines are extremely mature, they play a less role in cognitive structures for e-learners. Depending on the theory of constructivism, an effective mean to improve cognitive efficiency is to enhance the improvement and development of individual cognitive structure. Therefore, relying on thinking map, we develop a Linked Open Data-driven contrastive cognitive subgraph searching system for understanding concepts. Besides, during constructing contrastive cognitive subgraphs, we propose a method of calculating similarity between two keywords, whose accuracy and stability have been effectively improved compared with the other algorithm on LOD.
KW - Cognitive subgraph
KW - Linked open data
KW - Similarity calculation
UR - https://www.scopus.com/pages/publications/85061481849
U2 - 10.1109/ICEBE.2018.00050
DO - 10.1109/ICEBE.2018.00050
M3 - 会议稿件
AN - SCOPUS:85061481849
T3 - Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018
SP - 263
EP - 268
BT - Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on e-Business Engineering, ICEBE 2018
Y2 - 12 October 2018 through 14 October 2018
ER -