Research On Data Mining Of E-learning Behavior

Due to the rapid development of modern information technology and the Internet,online learning methods have become a focus of attention.Today’s society is in a fast-paced life.People can use the online education platform to use their scattered time to learn,but it is difficult for learners to judge their learning status and learning effect in the learning process,and it is difficult for teachers to understand each learner in depth The learning behavior of the learner cannot predict the learning behavior and learning effect of the learner in time,and the learning of the learner is easy to accomplish with less effort.When the learner learns on the online learning platform,he will leave the data containing the learning behavior information on the learning platform.We analyze and visualize these data to find the hidden problems that the learner has in learning,which is the online platform.Of educators provide early warning capabilities and can also provide learners with learning assistance.The main contents of this study are:1)Based on the collected learning data of MOOC learners,the statistical analysis method is used to analyze the three aspects of the learner’s learning behavior characteristics,learner type,and the relationship between learning behavior and learning performance,and then obtain the data The valuable information behind lays the foundation for the establishment of prediction models later.For example,the use of multiple linear regression to explore the impact of learning behavior on academic performance,the analysis found that in the learning process,the number of forum posts,active days,number of learning events,module completion ratio,and the number of learning chapters exist between the academic performance Positive correlation.And explore the five kinds of learning behaviors to explain the strength of academic performance.2)Researched the prediction method of single decision classifier of decision tree,gradient boosting decision tree and random forest classifier to the final score of MOOC users.Through the evaluation of the prediction model,the performance of these three models is analyzed and compared.Based on the comparison of the classification results of the three performance prediction models,studies have shown that the integrated classification prediction model is better than the single classification prediction model,and among the three classification models,the random forest algorithm performance prediction model has the best prediction classification effect.This article makes an in-depth analysis of the experimental results and obtains the relationship between MOOC learners’ learning behavior and performance.For MOOC platform teachers,they can learn about the learning characteristics and autonomous learning of MOOC learners,promote teaching reform,and provide help in optimizing teaching plan decisions;for MOOC platform users,they can understand the pros and cons of their learning behavior performance,and The impact of behavioral performance on learning performance during the learning process gradually changes learning habits to improve learning efficiency and optimize learning methods.