Abstract: With the wide use of online courses and the population of online learning,massive data of online learning behaviors have been collected.How to take advantages of those accumulated data through novel data mining technology for improving teaching decision and learning efficiency is becoming the research focus.In this paper,online learning behavior features are extracted,relationship between online learner’s personality and learning efficiency is modeled and analyzed,and personal learning recommendation is designed as well.First,online learner behavior features were extracted,and BP neural network based academic performance prediction algorithm was put forward,in which offline score was predicted based on accordingly online learning behavior features.Second,in order to further analyze the relationship of online learning behavior and offline practical score,a novel actual entropy based online learning behavior orderness evaluation model was proposed.Each learner’s offline academic performance can be predicted on basis of online learning orderness.Third,learners’ personalities were estimated through Felder-Silverman method.K-means algorithm was carried out on those personality vectors to achieve clusters of learners with the similar personality.Among those learners clustered into the same class,the top scored learner’s learning behavior will be recommended to the rest learners.Finally,tackinga practical online courses platform’s data as our experimental subject,plenty of experiments were carried out including online learning behavior feature extraction,offline academic performance evaluation and orderness analysis,perso-nal learning behavior recommendation,and the efficiency and application value of proposed method was proved.

Key words: Actual entropy, BP neural network, Felder-Silverman personality analysis, Online learning behavior, Personal learning recommendation