The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

Mohammad Reza FIROOZI , Ali KAZEMI , Maryam JOKAR


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.


Learning Satisfaction, Computer Self-Efficiency, Performance Expectation and Learning Climate, System Functionality.

Paper Details

Paper Details
Topic Elementary Education
Pages 613 - 626
Issue IEJEE, Volume 9, Issue 3
Date of acceptance 13 March 2017
Read (times) 33
Downloaded (times) 27

Author(s) Details

Mohammad Reza FIROOZI

Yasouj University, Iran


Yasouj University, Iran

Maryam JOKAR

Yasouj University, Iran


Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bolt, M. A., Killough, L. N., & Koh, H. C. (2001). Testing the interaction effects of task complexity in computer training using the social cognitive model. Decision Sciences32(1), 1-20.

Chan, F. M. (2002). ICT in Malaysian schools: Policy and strategies. In a Workshop on the Promotion of ICT in Education to Narrow the Digital Divide, Tokyo, Japan, 15–22 October.

Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399-416.

Chou, S. W., & Liu, C. H. (2005). Learning effectiveness in a Web‐based virtual learning environment: a learner control perspective. Journal of Computer Assisted Learning21(1), 65-76.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.

Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Davis, N. E., & Tearle, P. (1999). A core curriculum for telematics in teacher training. Tele-teaching 98 Conference; Vienna. Retrieved November 28, 2003, from

Francescato, D., Porcelli, R., Mebane, M., Cuddetta, M., Klobas, J., & Renzi, P. (2006). Evaluation of the efficacy of collaborative learning in face-to-face and computer-supported university contexts. Computers in Human Behavior, 22(2), 163-176.

Graham, C.R. (2006). Blended learning systems: Definitions, current trends and future directions. In C. J. Bonk, & C. R. Graham (Eds.), The Handbook of blended learning: Global perspectives, local designs (pp. 3-21). San Francisco: Pfeiffer.

Hashim, Y., & Man, R. (1999). Multimedia-based teaching and learning: issue and application in smart education. Instructional Technology and Smart Education: Preparation and Challenges in the New Millennium, Malaysian Association of Educational Technology, Penang, 45-64.

Heba, E. D., & Nouby, A. (2008). Effectiveness of a blended e-learning cooperative approach in an Egyptian teacher education programme. Computers & Education51(3), 988-1006.

Hong, W., Thong, J. Y., & Wai-Man Wong, K. Y. T. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. Journal of Management Information Systems18(3), 97-124.

Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems11(4), 87-114.

Jawahar, I. M., & Elango, B. (2001). The effect of attitudes, goal setting and self-efficacy on end user performance. Journal of Organizational and End User Computing13(2), 40-45.

Jin, D., & Lin, S. (Eds.). (2012). Advances in Future Computer and Control Systems (Vol. 1). Springer Science & Business Media.

Johnston, J., Killion, J., & Oomen, J. (2005). Student satisfaction in the virtual classroom. Internet Journal of Allied Health Sciences and Practice3(2), 1-7.

Kazu, I. Y., & Demirkol, M. (2014). Effect of Blended Learning Environment Model on High School Students' Academic Achievement. TOJET: The Turkish Online Journal of Educational Technology13(1) 78-87.

Kirschner, P., & Wopereis, I. G. (2003). Mindtools for teacher communities: A European perspective. Technology, Pedagogy and Education12(1), 105-124.

Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50.

Malaysian Ministry of Education, (MOE), (1997). The Malaysian Smart Schools Implementation Plan.

Marakas, G. M., Yi, M. Y., & Johnson, R. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information systems research9(2), 126-163.

Martins, L. L., & Kellermanns, F. W. (2004). A model of business school students' acceptance of a web-based course management system. Academy of Management Learning & Education3(1), 7-26.

Martirosyan, N. M., Saxon, D. P., & Wanjohi, R. (2014). Student satisfaction and academic performance in Armenian higher education. American International Journal of Contemporary Research4(2), 1-5.

Ming, T. S., Murugaiah, P., Wah, L. K., Azman, H., Yean, T. L., & Sim, L. Y. (2010). Grappling with technology: A case of supporting Malaysian Smart School teachers’ professional development. Australasian Journal of Educational Technology26(3), 400-416.

Ong, E. T., & Ruthven, K. (2009). The effectiveness of smart schooling on students’ attitudes towards science. Eurasia Journal of Mathematics, Science & Technology Education, 5(1), 35-45.

Osguthorpe, R. T., & Graham, C. R. (2003). Blended Learning Environments: Definitions and Directions. Quarterly Review of Distance Education4(3), 227-33.

Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: results from a worldwide educational assessment. Computers & education, 37(2), 163-178.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426.

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education47(2), 222-244.

Rahimi, M., & Yadollahi, S. (2011). The anxiety of high school students and its relationship with the use of computers and personal computer ownership. In Proceedings of the Fourth Conference on E-Learning, University of Technology, Tehran (Vol. 18).

Santhanam, R., Sasidharan, S., & Webster, J. (2008). Using self-regulatory learning to enhance e-learning-based information technology training. Information Systems Research19(1), 26-47.

Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education19, 10-17.

Shih, H. P. (2006). Assessing the effects of self-efficacy and competence on individual satisfaction with computer use: An IT student perspective. Computers in Human Behavior22(6), 1012-1026.

So, H. J., & Brush, T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education51(1), 318-336.

Soltani, M. (2012). The structure of smart schools in the educational system. Journal of Basic and Applied Scientific Research2(6), 6250-6254.

SSPT, Smart School Project Team, (1997). The Malaysian Smart School: Conceptual Blueprint. Ministry of Education. Malaysia, Kuala Lumpur.

Sung, Y. H., Kwon, I. G., & Ryu, E. (2008). Blended learning on medication administration for new nurses: Integration of e-learning and face-to-face instruction in the classroom. Nurse Education Today28(8), 943-952.

Tajuddin, R. A., Baharudin, M., & Hoon, T. S. (2013). System quality and its influence on students’ learning satisfaction in UiTM Shah Alam. Procedia-Social and Behavioral Sciences90, 677-685.

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing12(2), 137-155.

Torkzadeh, G., Koufteros, X., & Pflughoeft, K. (2003). Confirmatory analysis of computer self-efficacy. Structural Equation Modeling10(2), 263-275.

Umat, J. (2000, September). Web-based dissemination and utilization of learning resources: Tigerweb project. In Asia and Pacific Seminar/Workshop on Educational Technology (pp. 6-12).

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wood, R., & Bandura, A. (1989). Social cognitive theory of organizational management.  Academy of Management Review14(3), 361-384.

Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education55(1), 155-164.

Wu, J. H., Tennyson, R. D., Hsia, T. L., & Liao, Y. W. (2008). Analysis of E-learning innovation and core capability using a hypercube model. Computers in Human Behavior24(5), 1851-1866.

Wu, M. L., Wu, P. L., & Tasi, P. V. (2014). The relationship between classroom climate and learning satisfaction senior high school students. Asian Journal of Management Sciences & Education, 3(1), 58-68.

Zain, M. Z., Atan, H., & Idrus, R. M. (2004). The impact of information and communication technology (ICT) on the management practices of Malaysian Smart Schools. International Journal of Educational Development24(2), 201-211.

Zamanpour, E., Khani, M, H., & Moradiani Deizehrud, S, Kh. (2013). The effect of Computer Anxiety on Attitude towards E-Learning: The Mediating Role of Attitude and Self-Efficacy of Computer and Internet. Journal of Educational Psychology, 28(9),78-98.

Zhang, X., Keeling, K. B., & Pavur, R. J. (2000). Information quality of commercial web site home pages: an explorative analysis. In Proceedings of the twenty first international conference on Information systems (pp. 164-175). Association for Information Systems.