Mediating Effect of It Tools Usage on the Relationship Between Academic Self-efficacy, Learning Attitude and Academic Performance

Authors

  • Nargis Abbas Assistant Professor, Department of Education, University of Sargodha, Pakistan
  • Uzma Ashiq Department of Social Work, University of Sargodha, Pakistan
  • Ayesha Abbas Leads Business School, Lahore Leads University, Pakistan

DOI:

https://doi.org/10.47067/ramss.v3i3.72

Keywords:

Information Technology Integration, Path Analysis, Academic Performance, Secondary Public School

Abstract

Information technology has a powerful impact on our daily doings in all walks of life. Particularly in educational settings, the pyramid of learning attitude has been altered by the usage of technological tools in learning process and thus the performance of the students. However, comprehensive integration of information technology tools to enhance the learning is a deemed necessity of information age where adolescents are seemed as digital natives. Therefore, this study focused on measuring the mediating effect of information technology usage on the relationship of Academic efficacy &learning attitude and academic performance of the students in secondary schools. Multi stage sampling technique was used; 10% of secondary public schools were randomly selected from four randomly selected Tehsils of Sargodha as sample; at second stage, 20% of the 10th graders were selected from each school through stratified random sampling. Data was collected through questionnaire by using quantitative survey method. Path analysis was applied to study the mediating effect of IT usage on the relationship between academic self-efficacy and academic performance. Findings revealed that academic self-efficacy exert significant positive in direct effect on the academic performance mediated through IT usage. Similarly, academic attitude also found to have significant direct and indirect effect on the academic performance. Therefore, it is suggested that teachers should integrate the technology embedded activities in their teaching.

References

Abbas, N. (2011). Towards a model of mathematics attitudes formation: Through the child's perception of social agents and self-beliefs. (PhD). Université de Strasboug, Strasbourg, France.

Andrea, C., Andrew, B., Kathryn, H., & Lyn, A. (2011). Podcasting in Education: Student Attitudes, Behaviour and Self-Efficacy. Journal of Educational Technology & Society, 14(2), 236-247. Retrieved from http://www.jstor.org/stable/jeductechsoci.14.2.236

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. doi:10.1037/0033-295X.84.2.191

Bandura, A., & Locke, E. (2003). Negative Self-Efficacy and Goal Effects Revisited. The Journal of applied psychology, 88, 87-99. doi:10.1037/0021-9010.88.1.87

Byrne, B. M. (2006). Structural Equation Modeling With EQS. New York: Routledge.

Byun, J., Sung, T. E., & Park, H. W. (2018). Technological innovation strategy: how do technology life cycles change by technological area. Technology Analysis & Strategic Management, 30(1), 98-112. doi:10.1080/09537325.2017.1297397

Gulek, C. J., & Demirtas, H. (2005). Learning With Technology: The Impact of Laptop Use on Student Achievement. The Journal of Technology, Learning and Assessment, 3(2). Retrieved from https://ejournals.bc.edu/index.php/jtla/article/view/1655

Díaz-Posada, L., Varela-Londoño, S. P., & Rodríguez-Burgos, L. P. (2017). Multiple intelligences and curriculum implementation: Progress, trends and opportunities. Revista de Psicodidáctica, 22(1). doi:10.1387/RevPsicodidact.15614

Drain, T., Grier, L., & Sun, W. (2012). Is the growing use of electronic devices beneficial to academic performance? Results from archival data and a survey. Issues in Information Systems, 13(1), 225-231. Retrieved from http://www.iacis.org/iis/iis_articles.php?volume=13&issue=1

Duncan, T. G., & McKeachie, W. J. (2005). The Making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist, 40(2), 117-128. doi:10.1207/s15326985ep4002_6

Farnsworth, B. J., Shaha, S. H., Bahr, D. L., Lewis, V. K., & Benson, L. F. (2002). Preparing Tomorrow's Teachers to use Technology: learning and attitudinal impacts on elementary students. Journal of Instructional Psychology, 29, 121-138.

Fonseca, D., Martí, N., Redondo, E., Navarro, I., & Sánchez, A. (2014). Relationship between student profile, tool use, participation, and academic performance with the use of Augmented Reality technology for visualized architecture models. Computers in Human Behavior, 31(Feb), 434-445. doi:https://doi.org/10.1016/j.chb.2013.03.006

Gilakjani, A. P., Leong, L.-M., & Ismail, H. N. (2013). Teachers’ Use of Technology and Constructivism. International Journal of Modern Education and Computer Science, 5(4), 49-63. doi:10.5815/ijmecs.2013.04.07

Graham, J., Nosek, B., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. (2011). Mapping the Moral Domain. Journal of personality and social psychology, 101(2), 366-385. doi:https://doi.org/10.1037/a0021847

Grani?, A., & Maranguni?, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. doi:10.1111/bjet.12864

Gülay Ogelman, H., Güngör, H., Körükçü, Ö., & Erten Sarkaya, H. (2018). Examination of the relationship between technology use of 5–6 year-old children and their social skills and social status. Early Child Development and Care, 188(2), 168-182. doi:10.1080/03004430.2016.1208190

Hair, J., & Van der Leeuw, S. (2009). Multivariate Data Analysis. London: Prentice Hall, London.

Johnson, R. D., & Brown, K. G. (2017). E?Learning. In G. Hertel, D. L. Stone, R. D. Johnson, & J. Passmore (Eds.), Handbook of the Psychology of the Internet at Work (pp. 369-400). Hoboken, NJ: John Wiley & Sons Ltd.

Kalmus, V., Siibak, A., & Blinka, L. (2013). Internet and child well-being.Theories, methods and policies in global perspective. In A. Ben-Arieh, F. Casas, I. Frønes, & J. E. Korbin (Eds.), Handbook of Child Well-Being (1st ed., Vol. 41, pp. 2093-2133). Dordrech: Springer.

Kandalaft, M. R., Didehbani, N., Krawczyk, D. C., Allen, T. T., & Chapman, S. B. (2013). Virtual reality social cognition training for young adults with high-functioning autism. J Autism Dev Disord, 43(1), 34-44. doi:10.1007/s10803-012-1544-6

Klein, H. K., & Kleinman, D. L. (2002). The Social Construction of Technology: Structural Considerations. Science, Technology, & Human Values, 27(1), 28-52. Retrieved from http://www.jstor.org/stable/690274

Lei, J. (2010). Quantity versus quality: A new approach to examine the relationship between technology use and student outcomes. British Journal of Educational Technology, 41(3), 455-472. doi:10.1111/j.1467-8535.2009.00961.x

Moran, M., Hawkes, M., & Gayar, O. E. (2010). Tablet Personal Computer Integration in Higher Education: Applying the Unified Theory of Acceptance and use Technology Model to Understand Supporting Factors. Journal of Educational Computing Research, 42(1), 79-101. doi:10.2190/EC.42.1.d

Ng, S. F., Zakaria, R., Lai, S. M., & Confessore, G. J. (2016). A study of time use and academic achievement among secondary-school students in the state of Kelantan, Malaysia. International Journal of Adolescence and Youth, 21(4), 433-448. doi:10.1080/02673843.2013.862733

Pacurar, E., & Abbas, N. (2015). Analysis of French secondary school teachers’ intention to integrate digital work environments into their teaching practices. Education and Information Technologies, 20(3), 537-557. doi:10.1007/s10639-013-9301-9

Partin, M. L., & Haney, J. J. (2012). The CLEM model: Path analysis of the mediating effects of attitudes and motivational beliefs on the relationship between perceived learning environment and course performance in an undergraduate non-major biology course. Learning Environments Research, 15(1), 103-123. doi:10.1007/s10984-012-9102-x

Partin, M. L., Haney, J. J., Worch, E. A., Underwood, E. M., Nurnberger-Haag, J. A., Scheuermann, A., & Midden, W. R. (2011). Yes I Can: The Contributions of Motivation and Attitudes on Course Performance Among Biology Nonmajors. Journal of College Science Teaching, 40(6), 86-95. Retrieved from http://www.jstor.org/stable/42992902

Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63(October), 604-612. doi:http://dx.doi.org/10.1016/j.chb.2016.05.084

Schumacker, R. E., & Lomax, R. G. (2010). A beginner's guide to structural equation modeling, 3rd ed. New York, NY, US: Routledge/Taylor & Francis Group.

Schunk, D. H. (1991). Self-Efficacy and Academic Motivation. Educational Psychologist, 26(3-4), 207-231. doi:10.1080/00461520.1991.9653133

Strumsky, D., Lobo, J., & van der Leeuw, S. (2012). Using patent technology codes to study technological change. Economics of Innovation and New Technology, 21(3), 267-286. doi:10.1080/10438599.2011.578709

Tenhet, T. O. J. (2013). An examination of the relationship between tablet computing and student engagement, self-efficacy, and student attitude toward learning. (Ed.D). California State University, Fresno, Fresno. Retrieved from http://digitized.library.fresnostate.edu/cdm/ref/collection/thes/id/104687

Topal?, I. (2014). Attitudes towards Academic Learning and Learning Satisfaction in Adult Students. Procedia - Social and Behavioral Sciences, 142, 227-234. doi:https://doi.org/10.1016/j.sbspro.2014.07.583

Trimmel, M., & Bachmann, J. (2004). Cognitive, social, motivational and health aspects of students in laptop classrooms. Journal of Computer Assisted Learning, 20(2), 151-158. doi:https://doi.org/10.1111/j.1365-2729.2004.00076.x

Walker, C., & Greene, B. (2009). The Relations Between Student Motivational Beliefs and Cognitive Engagement in High School. Journal of Educational Research, 102(6), 463-472. doi:10.3200/JOER.102.6.463-472

Yau, H. K., & Leung, Y. F. (2018, March 14-16). The Relationship between Self-Efficacy and Attitudes towards the Use of Technology in Learning in Hong Kong Higher Education. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong.

Zhu, J., & Mok, M. M. C. (2020). Predictors of students' participation in internet or computer tutoring for additional instruction and its effect on academic achievement. Journal of Computer Assisted Learning, 36(5), 729-740. doi:10.1111/jcal.12440

Downloads

Published

2020-12-31

How to Cite

Abbas, N. ., Ashiq, U. ., & Abbas, A. . (2020). Mediating Effect of It Tools Usage on the Relationship Between Academic Self-efficacy, Learning Attitude and Academic Performance. Review of Applied Management and Social Sciences, 3(3), 377-389. https://doi.org/10.47067/ramss.v3i3.72