Статья

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system

Z. Pourtousi, S. Khalijian, A. Ghanizadeh, M. Babanezhad, A. Nakhjiri, A. Marjani, S. Shirazian,
2021

We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.

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  • 1. Version of Record от 2021-12-01

Метаданные

Об авторах
  • Z. Pourtousi
    Islamic Azad University, Science and Research Branch
  • S. Khalijian
    Shahid Beheshti University
  • A. Ghanizadeh
    Imam Reza International University
  • M. Babanezhad
    Duy Tan University, Duy Tan University, Shunderman Industrial Strategy Co.
  • A. Nakhjiri
    Islamic Azad University, Science and Research Branch
  • A. Marjani
    Islamic Azad University, Arak Branch
  • S. Shirazian
    South Ural State University
Название журнала
  • Scientific Reports
Том
  • 11
Выпуск
  • 1
Финансирующая организация
  • Government Council on Grants, Russian Federation
Номер гранта
  • FENU-2020-0019
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • scopus