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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Main Authors: Mohammad Reza Firoozi, Ali Kazemi, Maryam Jokar
Format: Article
Language:English
Published: Kura Publishing 2017-03-01
Series:International Electronic Journal of Elementary Education
Subjects:
Online Access:https://iejee.com/index.php/IEJEE/article/view/179/175
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spelling 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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