Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining

doi:10.23940/ijpe.20.04.p6.549559

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (4): 549-559.doi: 10.23940/ijpe.20.04.p6.549559

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Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining

Zhaoyu Shou^a,b,*, Yanguo Wang^a, Yiru Wen^a, and Huibing Zhang^c

^aSchool of Information and Communication, Guilin University of Electronic Technology, Guilin, 541000, China
^bKey Laboratory of Cognitive Radio and Information Processing Ministry of Education, Guilin, 541000, China
^cGuangxi Key Laboratory of Trusted Software, Guilin, 541000, China

Submitted on ; Revised on ; Accepted on
Contact: Shou Zhaoyu
About author:Zhaoyu Shou received his Ph.D. in computer science and technology from Guilin University of Electronic Technology. His research interest covers outlier detection, computer information management, and education big data.
Yanguo Wang is a master's student in the School of Information and Communication at Guilin University of Electronic Technology. His research interests include theories and algorithms for personalized recommendations, educational big data, and analytics.
Yiru Wen is a master's student in the School of Information and Communication at Guilin University of Electronic Technology. Her research interests include knowledge map construction and analysis.
Huibing Zhang received his Ph.D. in computer science and technology from the Beijing University of Technology. He is an associate professor in the Guangxi Key Laboratory of Trusted Software at Guilin University of Electronic Technology. His research interests include educational big data, Internet of things, and social computing.
Supported by:
This work was supported by the National Natural Science Foundation of China (61967005, 61662013, and U1501252), Innovation Project of GUET Graduate Education (2020YCXS022), the Key Laboratory of Cognitive Radio and Information Processing Ministry of Education (CRKL190107).

Abstract

Abstract:

Online courses will produce different learning effects due to the differences in the structure of knowledge points and the design of the teaching process. Therefore, a knowledge point recommendation algorithm based on enhanced correction factor and weighted sequence pattern mining (ECF-WSPM) is proposed, which divides learners into different groups according to their cognitive levels. To further improve the accuracy of the similarity calculation between learners, we innovatively propose the enhanced correction factor and reconstruct the similarity calculation model between learners based on the enhanced correction factor. In addition, the conceptual interaction of knowledge points and the sequential learning patterns of learners are combined for the first time to effectively mine the differences in the learning behavior characteristics of learners, so as to generate the final recommendation list of knowledge points based on the differences and improve the learning effects of learners. Comparison experiments on the real dataset demonstrate that our proposed algorithm improves the overall performance of the recommended algorithm.

Key words: enhanced correction factor, conceptual interaction of knowledge points, weighted sequential pattern mining, knowledge point recommendation

Zhaoyu Shou, Yanguo Wang, Yiru Wen, and Huibing Zhang. Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining [J]. Int J Performability Eng, 2020, 16(4): 549-559.

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Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining

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