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

摘要

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.