Predicting Student Rankings Based on the Dual-Student Performance Comparison Model

Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performance regression gives an absolute score value,and it cannot directly show the position of a student in the class.To overcome the above shortcomings,we will focus on a rare learning early warning method-ranking prediction.We propose a dual-student performance comparison model(DSPCM)to judge the ranking relationship between a pair of students.Then,we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713,respectively.We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students.The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter.This reflects the advantage of the DSPCM in ranking prediction.