Data: Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

The dataset was randomly produced by MATLAB R2018b.The dataset named 'NewData***N' contains a structure P that saved all the student and material attributes. Where *** stands for the number of materials.The dataset named 'New***N' contains three variables: S, Gbest, and Gtime.S is a cell-matrix. Each cell contains a structure that saved the algorithm running information.Gbest is a double matrix of 30 rows. Each row saves the best fitness value of one algorithm run on 100 different learners.Gtime is a double matrix of 30 rows as well. Each row saves the running time of the corresponding algorithm run on 100 different learners.