TY - GEN
T1 - Research on Personalized Learning Path Discovery Based on Differential Evolution Algorithm and Knowledge Graph
AU - Wang, Feng
AU - Zhang, Lingling
AU - Chen, Xingchen
AU - Wang, Ziming
AU - Xu, Xin
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning system, to achieve learning goals. The main challenge of building this system is to provide appropriate educational resources for different learners with different interests and background knowledge. The system should be efficient and adaptable. In addition, the best learning path to adapt learners can help reduce cognitive overload and disorientation. This paper proposes a framework for learning path discovery based on differential evolutionary algorithm and Knowledge graph. In the first stage, learners are investigated to form learners’ records according to their cognitive models, knowledge backgrounds, learning interests and abilities. In the second step, learners’ model database is generated, based on the classification of learners’ examination results. In the third stage, the knowledge graph based on disciplinary domain knowledge, is established. The differential evolution algorithm is then introduced as a method in the fourth stage. The framework is applied to learning path discovery based on differential evolution algorithm and disciplinary knowledge graph. The output of the system is a learning path adapted to learner’s needs and learning resource recommendation referring to the learning path.
AB - Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning system, to achieve learning goals. The main challenge of building this system is to provide appropriate educational resources for different learners with different interests and background knowledge. The system should be efficient and adaptable. In addition, the best learning path to adapt learners can help reduce cognitive overload and disorientation. This paper proposes a framework for learning path discovery based on differential evolutionary algorithm and Knowledge graph. In the first stage, learners are investigated to form learners’ records according to their cognitive models, knowledge backgrounds, learning interests and abilities. In the second step, learners’ model database is generated, based on the classification of learners’ examination results. In the third stage, the knowledge graph based on disciplinary domain knowledge, is established. The differential evolution algorithm is then introduced as a method in the fourth stage. The framework is applied to learning path discovery based on differential evolution algorithm and disciplinary knowledge graph. The output of the system is a learning path adapted to learner’s needs and learning resource recommendation referring to the learning path.
KW - Different evolution algorithm
KW - Knowledge graph
KW - Learning path
UR - http://www.scopus.com/inward/record.url?scp=85080855254&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-2810-1_28
DO - 10.1007/978-981-15-2810-1_28
M3 - Conference article published in proceeding or book
AN - SCOPUS:85080855254
SN - 9789811528095
T3 - Communications in Computer and Information Science
SP - 285
EP - 295
BT - Data Science - 6th International Conference, ICDS 2019, Revised Selected Papers
A2 - He, Jing
A2 - Yu, Philip S.
A2 - Shi, Yong
A2 - Li, Xingsen
A2 - Xie, Zhijun
A2 - Huang, Guangyan
A2 - Cao, Jie
A2 - Xiao, Fu
PB - Springer
T2 - 6th International Conference on Data Science, ICDS 2019
Y2 - 15 May 2019 through 20 May 2019
ER -