Developing a Model for Learning Satisfaction at Smart Schools with Reference to Bandura's Cognitive-Social Theory

The present study sought to develop a model for learning satisfaction at smart schools, using Bandura’s Cognitive-Social Theory. To accomplish this, 383 participants (210 male and 173 female) who were junior high school second graders at smart schools in Shiraz, were selected using random cluster sampling method. The participants were asked to complete the Computer Self-Efficiency Questionnaire developed by Torkzadeh, Performance Expectation Questionnaire developed by Compeau and Higgins, System Functionality and Content Feature Questionnaire developed by Pituch and Lee, Interaction Questionnaire developed by Johnston, Killion and Oomen, Learning Climate Questionnaire developed by Chou and Liu and Learning Satisfaction Questionnaire developed by Chiu, Hesu and Sun. The findings revealed that computer self-efficiency, performance expectation, system functionality, content feature, interaction and learning climate are the main contributing factors to learning satisfaction at smart schools. Through performance expectation, the independent variable of self-efficiency both directly and indirectly had an impact on learning satisfaction. No significant correlation was found between interaction and learning satisfaction. Through learning climate and performance expectation, interaction could significantly predict the learning satisfaction. Computer self-efficiency, system functionality, and content feature have a direct impact on learning satisfaction. Generally speaking, it could be said that these factors are essential to planning at smart schools and if they are taken into consideration, learning satisfaction will increase.