Self-efficacy it is worthwhile, valuable and simple. Student’s computer

Self-efficacy theory states that if individuals believe in their ability they are more likely to be motivated to complete the task or engage in behavior. (Payne, Youngcourt, & Beaubien, 2007).Decisions on actions, endeavor, endurance and success is influenced by self-efficacy  Bandura (1977a). Individuals with high self-efficacy for achieving certain outcomes work harder, engage further, go on longer when they experience challenges than the individuals who question their capacities. Forms of persuasion, Observational experiences, performance results and other reactions contribute to evaluation of self-efficacy. Successful results boost self-efficacy and failures brings it down, however when a solid strong feel of efficacy is produced a disappointment of failure should not have much effect (Bandura, 1986). Moreover, individuals compare themselves to others to gain self-efficacy information. People who look at their peers who are doing the same task consider that if others can do it they too are capable of doing it. Nevertheless, information gained indirectly needs confirmation by actual performance to remain credible. Wu, Tennyson and Hsia (2010) Conducted a study to determine how student learning satisfactions was affected by factors: self-efficacy and performance expectations, technological conditions, and social conditions in blended e-learning system. Questionnaire has been made based on 8 hypothetical statements. Total of 518 questionnaires has been distributed at the target universities. The empirical results indicate that achievement expectations and learning climate are two strong principles of learning satisfaction with e-learning system. Learning outcomes was indirectly influenced by the computer self-efficacy, system functionality, content components, and interaction implemented. These findings give initial insights into those components that could have have critical significance for planning and implementing Blended e-learning system. Components of virtual learning system should enhance students’ performance expectations and stimulate positive learning climate. Findings of this study show that performance expectations provide the most contribution to learning satisfaction. Thus, instructors should enhance students’ beliefs that they would be able to achieve improved outcomes by taking advantage of virtual learning system effectiveness in designing and teaching courses. Students’ learning satisfaction is affected by a positive learning climate. Thus,  teachers and learners have to enhance and motivate the positive learning atmosphere within the blended learning context.Therefore, student will be more likely to accept learning system if they believe that using it is worthwhile, valuable and simple. Student’s computer self-efficacy should be taken into consideration by education institutions. The empirical results show that performance expectations are positively influenced by computer self-efficacy.  BELS should provide built-in help to fit various learners’ needs in different learning circumstances. Blended e-learning system should incorporate multimedia content features and appropriate system functionality because the results of the study show that system functionality and content features improves perceived expectations. Blended e-learning system should include design that is rich in content, includes customized functions, satisfy students needs, and allows learners to control over the system. Effective interaction tools should be provided and teachers should motivate interaction openly. The results show that both learning climate and performance expectations were influenced significantly because of participant interaction.Learning climate is mainly based on positive feedback of participant interaction in learning system environment. Learning can be made easy and natural in positive learning climate. Therefore, if blended e-learning system could use a good social environment to provide the student-to-student and student-to-instructor interactive communication and collaborative learning. ReferencesBandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, N.J.: Prenctice Hall.Choi, D., Kim, J. and Kim, S. (2007). ERP training with a web-based electronic learning system: The flow theory perspective. International Journal of Human-Computer Studies, 65(3), pp.223-243.Condry, J. (1977). Enemies of exploration: Self-initiated versus other-initiated learning. Journal of Personality and Social Psychology, 35(7), pp.459-477.Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience.. New york: Harper Perennial.Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), p.319.Deci, E. and Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior .. New York: New York: Plenum.Deci, E., Koestner, R. and Ryan, R. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), pp.627-668.Guay, F., Chanal, J., Ratelle, C., Marsh, H., Larose, S. and Boivin, M. (2010). Intrinsic, identified, and controlled types of motivation for school subjects in young elementary school children. British Journal of Educational Psychology, 80(4), pp.711-735.Keller, J. (1979). Motivation and instructional design: A theoretical perspective. Journal of Instructional Development, 2(4), pp.26-34.Keller, J. and Suzuki, K. (2004). Learner motivation and E-learning design: A multinationally validated process. Journal of Educational Media, 29(3), pp.229-239.Lee, M., Cheung, C. and Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), pp.1095-1104.Liu, S., Liao, H. and Pratt, J. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), pp.599-607.Payne, S., Youngcourt, S. and Beaubien, J. (2007). A meta-analytic examination of the goal orientation nomological net. Journal of Applied Psychology, 92(1), pp.128-150.PEARCE, J., AINLEY, M. and HOWARD, S. (2005). The ebb and flow of online learning. Computers in Human Behavior, 21(5), pp.745-771.Ryan, R. and Deci, E. (2000). Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. Contemporary Educational Psychology, 25(1), pp.54-67.Wu, J., Tennyson, R. and Hsia, T. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), pp.155-164.Owston, R., Wideman, H., Murphy, J. and Lupshenyuk, D. (2008). Blended teacher professional development: A synthesis of three program evaluations. The Internet and Higher Education, 11(3-4), pp.201-210.F. Davis, R.P. Bagozzi, P.R. Warshaw, Extrinsic and intrinsic motivation to use computers in the workplace, Journal of Applied Social Psychology (22) 1992, pp. 1111–1132Nakamura, J., & Csikszentmihalyi, M. (2002). The Concept of Flow. In C. Snyder, & S. Lopez (Eds.), Handbook of Positive Psychology (pp. 89-105). New York: University Press. Webster, J., Trevino, L. K., & Ryan, L. (1993). The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior, 9(4), 411-426. Kolb, D. A.: Experiential learning: Experience as the source of learning and development. Prentice-Hall Englewood Cliffs, NJ, 1984. Weiner, B. (Ed.). (1974). Achievement motivation and attribution theory. Morristown, NJ: General Learning Press.Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89-105). New York: Oxford University Press.organization and management a system and contingency approach kastSong, L., Singleton, E., Hill, J. and Koh, M. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), pp.59-70.Paechter, M., Maier, B. and Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), pp.222-229. Locke, E. (1996). Motivation through conscious goal setting. Applied and Preventive Psychology, 5(2), pp.117-124.Chrons, O. and Sundell, S. (Microtask) (2011) Digitalkoot: Making Old Archives Accessible Using Crowdsourcing, workshop at HCOMP 2011, August 8, San Francisco, CaliforniaMuntean, Cristina Ioana. “Raising engagement in e-learning through gamification.” Proc. 6th International Conference on Virtual Learning ICVL. No. 42. 2011.Kapp, Karl M. The gamification of learning and instruction : game-based methods and strategies for training and education. San Francisco, CA: Pfeiffer, 2012. Print.