Design And Implementation Of Online Learning Community Learner Academic Achievement Prediction System

In recent years,Internet technology has developed rapidly,promoting the development of online learning communities and making education more fair.More and more learners choose to acquire knowledge from the online learning community,through online classrooms,and through other short text discussions on the Web.However,because of the unsupervised learning environment of the online learning community,a large number of learners are lax in the academic curriculum'because of this learning environment and the inability to predict the learning situation of their own academic courses,which leads to the failure of the final course assessment.Therefore,in order to make learners easier to graduate in the learning community,a large number of scholars and experts are actively committed to the study of learners' academic performance prediction.But in two places that are unsatisfactory,(1)from the previous experts and scholars,it is found that the use of Decision Trees,Naive Bayes,Support Vector Machines and other old traditional machine learning methods is not as good as the deep learning method.(2)So far,there is almost no universal predictive tool that can be used directly to analyze and predict learners' academic performance.In order to realize the online learner academic achievement prediction system,this paper mainly does the following work in the context of the online learning community,focusing on the characteristics of the online learner academic achievement data set:Firstly,design and implement an online learning environment to obtain an online learner academic performance data set.Secondly,the data pre-processing function is implemented,and the data is digitized,normalized,and reduced in dimension according to the characteristics of the data set,and the data is converted into a direct calculation and the dimension difference is eliminated.Then,the academic performance prediction function is realized.Based on the data pre-processing,the academic achievement is predicted,and the learner's academic achievement is classified and predicted according to the academic achievement level.In the prediction algorithm,in order to improve the accuracy rate,the current hot learning method is selected to classify and predict learners'academic achievements.Finally,the data visualization function is implemented,which is presented to the learner in the form of a graph based on the original data set and the predicted academic performance.