TY - GEN
T1 - Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style
AU - Yan, Lingyao
AU - Yin, Chuantao
AU - Chen, Hui
AU - Rong, Wenge
AU - Xiong, Zhang
AU - David, Bertrand
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - With the development of the Internet, e-learning has become a new trend for education. However, unlike traditional learning that is face-to-face, e-learning systems construct an environment where learners control their learning process. Many issues have occurred in online learning systems, such as low efficiency, high dropout rates, poor grades and so on. One of the leading causes is students’ low interest in e-learning content, and they cannot find attractive learning materials. Learning resource recommendations can solve this problem by recommending materials that learners may like. However, traditional recommendation methods omit that user’s identity as a student and face underperformance. In this paper, a new learning resource recommendation method based on Online Learning Style is proposed. By integrating learning style characteristics into collaborative filtering algorithm with association rules mining, experimental results showed that the proposed method achieved 25% improvement compared to the method without learners’ features.
AB - With the development of the Internet, e-learning has become a new trend for education. However, unlike traditional learning that is face-to-face, e-learning systems construct an environment where learners control their learning process. Many issues have occurred in online learning systems, such as low efficiency, high dropout rates, poor grades and so on. One of the leading causes is students’ low interest in e-learning content, and they cannot find attractive learning materials. Learning resource recommendations can solve this problem by recommending materials that learners may like. However, traditional recommendation methods omit that user’s identity as a student and face underperformance. In this paper, a new learning resource recommendation method based on Online Learning Style is proposed. By integrating learning style characteristics into collaborative filtering algorithm with association rules mining, experimental results showed that the proposed method achieved 25% improvement compared to the method without learners’ features.
KW - Clustering
KW - Online learning style
KW - Online learning system
KW - Recommendation
UR - https://www.scopus.com/pages/publications/85113754903
U2 - 10.1007/978-3-030-82153-1_31
DO - 10.1007/978-3-030-82153-1_31
M3 - 会议稿件
AN - SCOPUS:85113754903
SN - 9783030821524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 373
EP - 385
BT - Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
A2 - Qiu, Han
A2 - Zhang, Cheng
A2 - Fei, Zongming
A2 - Qiu, Meikang
A2 - Kung, Sun-Yuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
Y2 - 14 August 2021 through 16 August 2021
ER -