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The use of genetic algorithm to optimize quantitative learner's motivation model

Data publikacji: 27.04.2018

Czasopismo Techniczne, 2018, Volume 4 Year 2018 (115), s. 189 - 194

https://doi.org/10.4467/2353737XCT.18.066.8378

Autorzy

,
Paweł Lempa
Institute of Applied Informatics, Faculty of Mechanical Engineering, Cracow University of Technology
Wszystkie publikacje autora →
,
Michal Ptaszynski
Department of Computer Science Kitami Institute of Technology, Japan
Wszystkie publikacje autora →
Fumito Masui
Department of Computer Science Kitami Institute of Technology, Japan
Wszystkie publikacje autora →

Tytuły

The use of genetic algorithm to optimize quantitative learner's motivation model

Abstrakt

The paper presents a method of optimizing Quantitative Learner’s Motivation Model with the use of genetic algorithm. It is focused on optimizing the formula for prediction of learning motivation by means of different weights for three values: interest, usefulness in the future and satisfaction. For the purpose of this optimization, we developed a C++ library that implements a genetic algorithm and an application in C# which uses that library with data acquired from questionnaires enquiring about those three elements. The results of the experiment showed improvement in the estimation of student’s learning motivation.

Bibliografia

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Informacje

Informacje: Czasopismo Techniczne, 2018, Volume 4 Year 2018 (115), s. 189 - 194

Typ artykułu: Oryginalny artykuł naukowy

Tytuły:

Polski:

The use of genetic algorithm to optimize quantitative learner's motivation model

Angielski:

The use of genetic algorithm to optimize quantitative learner's motivation model

Autorzy

Institute of Applied Informatics, Faculty of Mechanical Engineering, Cracow University of Technology

Department of Computer Science Kitami Institute of Technology, Japan

Department of Computer Science Kitami Institute of Technology, Japan

Publikacja: 27.04.2018

Status artykułu: Otwarte __T_UNLOCK

Licencja: Żadna

Udział procentowy autorów:

Paweł Lempa (Autor) - 33%
Michal Ptaszynski (Autor) - 33%
Fumito Masui (Autor) - 34%

Korekty artykułu:

-

Języki publikacji:

Angielski