Students'Performance Prediction in Remote Education Based on CII with Classifiers
FENG Li
Abstract：Traditional integrated algorithms usually run as batch mode in remote education ,for the issue that it is una‐vailable under the condition with data generated continuously ,a student's performance predicting algorithm based on combi‐nation incremental integration(CII) with classifiers is proposed .Firstly ,the three popular ensemble classifiers incremental version of simple Bayesian ,1‐NN and WINNOW algorithm are introduced .Then ,the three algorithms are used to generate each hypothesis .Finally ,three hypothesizes are integrated and voting method is used to predict student's performance .Ex‐perimental results on training set HOU supplied by information course at Greek University of Distance Education show that proposed algorithm has higher classification accuracy and less training time than several advanced classifiers ,which indicates that it provides a powerful prediction tool of student's performance .
Keywords：combination incremental integrationremote educationstudent's performance predictionclassifiersvot-ing method
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2017-2021,2145 )