Effects of Technology Access and Technical Self-Efficacy Changes Attitudes in Lecturers’ Readiness
One of the evolutions in classroom learning is the use of information and communication technology (ICT) as a source of integrated teaching and learning, whether in the classroom or outside of it. This study intended to examine the readiness of UTMSPACE lecturers towards the implementation of blended learning. Apart from that, this study also examines how personal factors affected the success of e-learning systems and provided better results. Structural equation models on the data of 101 targeted respondents showed that online communication self-efficacy, attitude, and online media are the multiple mediators between the technology access and technical usage self-efficacy and lead to increased blended learning readiness among the lecturers at UTMSPACE. It appears that despite technological factors, the lecturers with a high belief in their ability and attitude are more prepared to adopt the alternative ways of teaching and learning as they gain more experience.
Cain, Meghan & Zhang, Zhiyong & Yuan, Ke-Hai. (2016). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 2016. 17. 10.3758/s13428-016-0814-1.
Creswell, J.W. (2015). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. 4th edition. London: Pearson New International Edition.
Chin, Wynne & Marcolin, Barb & Newsted, Peter. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study, Information Systems Research. 14. 189-217. 10.1287/isre.126.96.36.19918.
Franke, George and Marko Sarstedt. (2018). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, forthcoming.
Jeffrey, L. M., Milne, J., Suddaby. G., & Higgins, A. (2014). Blended learning: How teachers balance the blend of online and classroom components. Journal of Information Technology Education: Research, 13, 121-140.
Junus, K.; Santoso, H.B.; Putra, P.O.H.; Gandhi, A.; Siswantining, T. (2021) Lecturer Readiness for Online Classes during the Pandemic: A Survey Research. Educ. Sci. 11, 139. https://doi.org/ 10.3390/educsci11030139
Hahn, E.D., & Ang, S.H. (2017). From the editors: New directions in the reporting of statistical results in the Journal of World Business. Journal of World Business.52, 125-126.
Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM, European Business Review.Vol. 31 No. 1, pp. 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Hult, G. T. M., Ringle, C. M., &Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. (PLS-SEM). 2nd edition. Thousand Oaks: Sage Publications.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy Marketing Science. 40(3), 414–433.
Henseler, J., & Chin, W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling. 17 (1), 82–109.
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equation models perform better than regression. Journal of Consumer Psychology. Vol. 7(2), 140-154.
Konak, A., Kulturel-Konak, S., & Cheung, G. W. (2018). Teamwork Attitudes, Interest and Self-Efficacy Between Online and Face-To-Face Information Technology Students, Team Performance Management. http://doi.org/10.1108/TPM-05-2018-0035
Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems. 13(7), 546-580.
Kock, N. (2012) Common method bias in PLS-SEM: A full collinearity assessment approach, International Journal of e-Collaboration. 11(4), 1-10.
Ong, M.H.A., &Puteh, F.(2017). Quantitative Data Analysis: Choosing Between SPSS, PLS, and AMOS in Social Science Research. International Interdisciplinary Journal of Scientific Research. Vol. 3 (1), pp.14-25
Mohammed, Y. (2018). E-Learning Readiness Assessment of Medical Students in University of Fallujah. 1st Annual International Conference on Information and Sciences (AiCIS), 201-207.
Monteiro, E., & Morrison, K. (2014). Challenges for Collaborative Blended Learning in Undergraduate Students. Educational Research and Evaluation. 20(7-8), 564-591.
Ringle, C. M., Wende, S., & Becker, J.-M.(2015). "SmartPLS 3." Boenningstedt: SmartPLS GmbH. http://www.smartpls.com.
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis. 2nd edition. Kuala Lumpur, Malaysia: Pearson.
Rapanta, C., Botturi, L., Goodyear, P. (2020). Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity. Postdigit Sci Educ 2, 923–945. https://doi.org/10.1007/s42438-020-00155-y
Rasheed Abubakar Rasheed, Amirrudin Kamsin, Nor Aniza Abdullah. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education. Vol 144, 103701, ISSN 0360-1315, https://doi.org/10.1016/j.compedu.2019.103701.
Zhao, X., Lynch, J.G.J., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truth about mediation analysis. Journal of Consumer Research. Vol. 17, 197-206.