The role of socio-cognitive variables in predicting learning satisfaction in smart schools
The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through mult...
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doaj-52a96af400b3402b83f0214efe97ef5a2020-11-25T03:45:01ZengKura PublishingInternational Electronic Journal of Elementary Education1307-92981307-92982017-03-0193613626The role of socio-cognitive variables in predicting learning satisfaction in smart schoolsMohammad Reza Firoozi0Ali Kazemi1Maryam Jokar2Yasouj UniversityYasouj UniversityYasouj UniversityThe present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003), Performance Expectation Questionnaire developed by Compeau and Higgins (1995), System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006), Interaction Questionnaire developed by Johnston, Killion and Oomen (2005), Learning Climate Questionnaire developed by Chou` and Liu (2005) and Learning Satisfaction Questionnaire developed by Chou and Liu (2005). In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.https://iejee.com/index.php/IEJEE/article/view/179/175Learning SatisfactionComputer Self-EfficiencyPerformance Expectation and Learning ClimateSystem Functionality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohammad Reza Firoozi Ali Kazemi Maryam Jokar |
spellingShingle |
Mohammad Reza Firoozi Ali Kazemi Maryam Jokar The role of socio-cognitive variables in predicting learning satisfaction in smart schools International Electronic Journal of Elementary Education Learning Satisfaction Computer Self-Efficiency Performance Expectation and Learning Climate System Functionality |
author_facet |
Mohammad Reza Firoozi Ali Kazemi Maryam Jokar |
author_sort |
Mohammad Reza Firoozi |
title |
The role of socio-cognitive variables in predicting learning satisfaction in smart schools |
title_short |
The role of socio-cognitive variables in predicting learning satisfaction in smart schools |
title_full |
The role of socio-cognitive variables in predicting learning satisfaction in smart schools |
title_fullStr |
The role of socio-cognitive variables in predicting learning satisfaction in smart schools |
title_full_unstemmed |
The role of socio-cognitive variables in predicting learning satisfaction in smart schools |
title_sort |
role of socio-cognitive variables in predicting learning satisfaction in smart schools |
publisher |
Kura Publishing |
series |
International Electronic Journal of Elementary Education |
issn |
1307-9298 1307-9298 |
publishDate |
2017-03-01 |
description |
The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003), Performance Expectation Questionnaire developed by Compeau and Higgins (1995), System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006), Interaction Questionnaire developed by Johnston, Killion and Oomen (2005), Learning Climate Questionnaire developed by Chou` and Liu (2005) and Learning Satisfaction Questionnaire developed by Chou and Liu (2005). In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction. |
topic |
Learning Satisfaction Computer Self-Efficiency Performance Expectation and Learning Climate System Functionality |
url |
https://iejee.com/index.php/IEJEE/article/view/179/175 |
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