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Published on 04 March 2022 |

Version V1

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

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Zhang, Yong-Wei

Description

The dataset was randomly produced by MATLAB R2018b.The dataset named 'NewDataN' contains a structure P that saved all the student and material attributes. Where *** stands for the number of materials.The dataset named 'NewN' 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.

Citations (0)

Mentions (0)

Metrics

Dataset Index

1.5

FAIR Score

69%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

10.57760/sciencedb.01552

Publisher

Science Data Bank

Assigned Domain

Subfield

Computational Theory and Mathematics

Field

Computer Science

Domain

Physical Sciences

Confidence Score

55%

Source

Scholar Data Model

Keywords

Information science and systems scienceInformation and systems science related engineering and technologyComputer science and technologyPedagogyComputer and information sciencesFOS: Computer and information sciencescombination optimizationdifferential evolutionmulti-attribute matchingpersonalized learning pathproblem representation

Normalization Factors

FT

15.38

CTw

1.00

MTw

1.00