Association analysis of online learning behaviour in interactive education based on an intelligent concept machine Online publication date: Thu, 02-Apr-2020
by Yuenan Chen
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 30, No. 2, 2020
Abstract: Aiming at the problem that the existing association analysis of online learning behaviour in interactive education has poor practical application effect, this paper proposes an association analysis method of online learning behaviour in interactive education based on intelligent concept machine. Association rules are used to obtain association information between data. Based on the characteristics of online learning, a data classification index system was constructed by RFM analysis method. K-means method is adopted to cluster user behaviours. PageRank algorithm was used to obtain the most representative learning users, recommend the best courses for users, and analyse the learning behaviour and effect through association rules. Finally, through simulation experiment, it is found that the average learning score of learners increases by 15 points after using this method, and the application effect is good, which verifies the effectiveness of this method.
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