Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model

Mailizar Mailizar; Abdulsalam Almanthari; Suci Maulina

doi:10.30935/cedtech/9709

Mailizar Mailizar ¹ ^* , Abdulsalam Almanthari ², Suci Maulina ³

More Detail

CONT ED TECHNOLOGY, Volume 13, Issue 2, Article No: ep298

https://doi.org/10.30935/cedtech/9709

Submitted: 08 July 2020, Published Online: 13 February 2021

OPEN ACCESS 8630 Views 6134 Downloads

Download Full Text (PDF)

Abstract

The aim of this study was to examine factors that influenced experienced teachers’ intention to use E-learning in their teaching of mathematics. Data were collected using a questionnaire from 161 secondary school mathematics teachers who completed a six-month in-service online training provided by the Indonesian Ministry of Education. The Technology Acceptance Model (TAM) was used as the framework while E-learning experience was included as an additional construct. An extended TAM model was proposed and tested in this study. It consisted of five constructs, namely: intention to use, perceived usefulness, perceived ease of use, attitude toward using, and experience. Data were analyzed using Structural Equation Modelling with SMARTPLS 3.0. The findings showed that attitude toward E-learning use and E-learning experience were the two most significant constructs in predicting E-learning use. Contrary to previous studies, perceived ease of use and perceived usefulness were non-significant factors for the prediction of the behavioral intention. Implications for future research and practices are discussed.

Keywords: behavioral intention, teachers’ intention to use e-learning, extended TAM model, teachers’ e-learning experience, e-learning in mathematics classroom

References

Abbad, M. M., Morris, D., & De Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. The International Review of Research in Open and Distributed Learning, 10(2). https://doi.org/10.19173/irrodl.v10i2.596
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
Ajibade, P. (2018). Technology Acceptance Model Limitations and Criticisms: Exploring the Practical Applications and Use in Technology-related Studies, Mixed-method, and Qualitative Researches. Library Philosophy and Practice.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155. https://doi.org/10.14569/ijacsa.2014.050120
Alzahrani, A. M. (2019). Factors that influence secondary school teachers’ acceptance of e-learning technologies in teaching in the kingdom of Saudi Arabia. Journal of Research in Curriculum Instruction and Educational Technology, 5(2), 175-196.
Briz-Ponce, L., & García-Peñalvo, F. J. (2015). An empirical assessment of a technology acceptance model for apps in medical education. Journal of medical systems, 39(11), 176. https://doi.org/10.1007/s10916-015-0352-x
Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531. https://doi.org/10.1002/hfm.20548
Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & management, 44(4), 384-396. https://doi.org/10.1016/j.im.2007.02.003
Chang, C.-C., Yan, C.-F., & Tseng, J.-S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5). https://doi.org/10.14742/ajet.818
Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003
Chuttur, M. Y. (2009). Overview of the technology acceptance model: Origins, developments and future directions. Working Papers on Information Systems, 9(37), 9-37.
Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in human behavior, 52, 22-28. https://doi.org/10.1016/j.chb.2015.05.049
Cohen, J. (2013). Statistical power analysis for the behavioral sciences: Academic press. https://doi.org/10.4324/9780203771587
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
Davis. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688-696. https://doi.org/10.1016/j.compedu.2011.09.013
Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192. https://doi.org/10.7763/ijiet.2016.v6.683
Fishbein, M. A., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fraenkel, Wallen, N. E., & Hyun, H. H. (2011). How to design and evaluate research in education: New York: McGraw-Hill Humanities/Social Sciences/Languages.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM): Sage publications. https://doi.org/10.15358/9783800653614
Halek, M., Holle, D., & Bartholomeyczik, S. (2017). Development and evaluation of the content validity, practicability and feasibility of the Innovative dementia-oriented Assessment system for challenging behaviour in residents with dementia. BMC health services research, 17(1), 554. https://doi.org/10.1186/s12913-017-2469-8
Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Economics and Finance, 35(2016), 644-649. https://doi.org/10.1016/s2212-5671(16)00079-4
Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational statistics, 25(1), 107-120. https://doi.org/10.1007/s00180-009-0164-x
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hess, T. J., McNab, A. L., & Basoglu, K. A. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS quarterly, 38(1), 1-28. https://doi.org/10.25300/misq/2014/38.1.01
Hsia, J.-W., Chang, C.-C., & Tseng, A.-H. (2014). Effects of individuals’ locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 51-64. https://doi.org/10.1080/0144929x.2012.702284
Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159-164. https://doi.org/10.1016/j.procs.2017.01.196
Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological bulletin, 77(5), 361. https://doi.org/10.1037/h0032590
Keong, Y. C., Albadry, O., & Raad, W. (2014). Behavioral intention of EFL teachers to apply e-learning. Journal of Applied Sciences, 14(20), 2561-2569. https://doi.org/10.3923/jas.2014.2561.2569
Kiraz, E., & Ozdemir, D. (2006). The relationship between educational ideologies and technology acceptance in pre-service teachers. Journal of Educational Technology & Society, 9(2), 152-165.
Lau, S.-H., & Woods, P. C. (2008). An empirical study of learning object acceptance in multimedia learning environment. Communications of the IBIMA, 5(1), 1-6.
Lee, Y.-H., Hsiao, C., & Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5). https://doi.org/10.14742/ajet.381
Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An investigation of employees’ use of e-learning systems: applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173-189. https://doi.org/10.1080/0144929x.2011.577190
Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees’e-learning systems acceptance. Knowledge-based systems, 24(3), 355-366. https://doi.org/10.1016/j.knosys.2010.09.005
Letchumanan, M., & Tarmizi, R. (2011). Assessing the intention to use e‐book among engineering undergraduates in Universiti Putra Malaysia, Malaysia. Library Hi Tech, 29(3), 512-528. https://doi.org/10.1108/07378831111174459
Lew, S.-L., Lau, S.-H., & Leow, M.-C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788
Lin, K.-M. (2011). e-Learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56(2), 515-526. https://doi.org/10.1016/j.compedu.2010.09.017
Lin, K.-M., Chen, N.-S., & Fang, K. (2010). Understanding e-learning continuance intention: a negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77-89. https://doi.org/10.1080/01449291003752948
Mailizar, Almanthari, A., Maulina, S., & Bruce, S. (2020). Secondary School Mathematics Teachers’ Views on E-learning Implementation Barriers during the COVID-19 Pandemic: The Case of Indonesia. Eurasia Journal of Mathematics, Science and Technology Education, 16(7), 1860. https://doi.org/10.29333/ejmste/8240
Mailizar, Samingan, Rusman, Huda, I., & Yulisman, H. (2020). Mathematics, Science and Social Science teachers’ acceptance of online teacher professional development: Does internet accessibility matter? Journal of Physics: Conf. Series, 1460(2020). https://doi.org/10.1088/1742-6596/1460/1/012103
Martin, R. G. (2012). Factors affecting the usefulness of social networking inE-learning at German University of Technology in Oman. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(6), 498. https://doi.org/10.7763/ijeeee.2012.v2.171
Mou, J., Shin, D.-H., & Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behaviour & Information Technology, 36(2), 125-139. https://doi.org/10.1080/0144929x.2016.1203024
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
Pituch, K. A., & Lee, Y.-k. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007
Premchaiswadi, W., Porouhan, P., & Premchaiswadi, N. (2012). An empirical study of the key success factors to adopt e-learning in Thailand. Paper presented at the International conference on information society (i-Society 2012).
Purnomo, S. H., & Lee, Y.-H. (2013). E-learning adoption in the banking workplace in Indonesia: an empirical study. Information Development, 29(2), 138-153. https://doi.org/10.1177/0266666912448258
Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies, 3(9), 451-457.
Rezaei, M., Mohammadi, H. M., Asadi, A., & Kalantary, K. (2008). Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education, 9(1), 85-95.
Shank, G., & Brown, L. (2013). Exploring educational research literacy: Routledge. https://doi.org/10.4324/9780203943786
Sharma, S., & Chandel, J. (2013). Technology Acceptance Model For The Use Of Learning Through Websites Among Students In Oman. International Arab Journal of E-Technology, 3(1), 44-49.
Stockless, A. (2018). Acceptance of learning management system: The case of secondary school teachers. Education and Information Technologies, 23(3), 1101-1121. https://doi.org/10.1007/s10639-017-9654-6
Taat, M. S., & Francis, A. (2019). Factors Influencing the Students’ Acceptance of E-Learning at Teacher Education Institute: An Exploratory Study in Malaysia. The International Journal of Higher Education, 9, 133. https://doi.org/10.5430/ijhe.v9n1p133
Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, demographic characteristics and e-learning acceptance: a conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72-89. https://doi.org/10.5539/hes.v6n3p72
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in human behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79. https://doi.org/10.1080/10494820802231327
Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education, 81, 169-178. https://doi.org/10.1016/j.compedu.2014.10.009
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
Williams, M., & Williams, J. (2010). Evaluating a model of business school students’ acceptance of web-based course management systems. The International Journal of Management Education, 8(3), 59-70. https://doi.org/10.3794/ijme.83.264
Wong, G. K. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6). https://doi.org/10.14742/ajet.1890
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038. https://doi.org/10.1080/0144929X.2014.934291
Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779

Citation

Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709

Mailizar, M., Almanthari, A., and Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709

Mailizar M, Almanthari A, Maulina S. Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. CONT ED TECHNOLOGY. 2021;13(2), ep298. https://doi.org/10.30935/cedtech/9709

Mailizar, Mailizar, Abdulsalam Almanthari, and Suci Maulina. "Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model". Contemporary Educational Technology 2021 13 no. 2 (2021): ep298. https://doi.org/10.30935/cedtech/9709

Mailizar, Mailizar et al. "Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model". Contemporary Educational Technology, vol. 13, no. 2, 2021, ep298. https://doi.org/10.30935/cedtech/9709

Mailizar M, Almanthari A, Maulina S. Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. CONT ED TECHNOLOGY. 2021;13(2):ep298. https://doi.org/10.30935/cedtech/9709