Research on Prediction of MOOC Learning Performance Based on K-nearest Neighbor Optimization Algorithm
WANG Fengqin
LI Ying
HAN Qinglong
Abstract：How to use big data of students online learning has become a hot topic in the field of learning analysis. In order to predict students'academic performance on the Massive Open Online Courses(MOOCs)platform,the data modeling of online data and class performance data is first presented,in which data items are defined,then the prediction model based on k- nearest neigh?bor optimization algorithm is constructed. In order to eliminate the difference between the data items,the data preprocessing method is given. Finally,using the genetic algorithm to optimize k- nearest neighbor algorithm,the principles and implementation methods are described with Python. The experimental results show that the k- nearest neighbor algorithm optimized by genetic algorithm makes the prediction more accurately.
Keywords：academic performance predictionk-nearest neighbor algorithmMOOC data analysisclassification algorithmlearning analysis
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 785-788 )