i-manager Publications

Using the Technology Acceptance Model to Examine Technology Acceptance of Online Learning Technologies by Non-Traditional Students

Tony Robinson*

Department of Business and Technology, Mountain Empire Community College, USA.

Periodicity:April - June'2019
DOI : https://doi.org/10.26634/jet.16.1.15826

Abstract

Online learning has changed higher education, emerging as a primary source for delivering courses and programs to students. As online learning has grown, more non-traditional students have entered college, many for the first time. Consequently, many of these non-traditional are experiencing online learning, and the technologies that deliver them, for the first time. This quantitative research study developed and tested technology acceptance of online learning technologies using the Technology Acceptance Model (TAM) with variables perceived ease of use, perceived usefulness, attitude, and intention to use. Findings from 40 valid responses in an online survey at Mountain Empire Community College (2016) USA, showed that perceived ease of use had a significant effect on perceived usefulness, which is consistent with TAM. Findings showed that perceived ease of use did have a significant effect on attitude. Further, perceived usefulness had a significant effect on attitude and attitude had a significant effect on intention to use, which is consistent with TAM.

Keywords

Technology, Acceptance, Non-traditional, Students, Online Learning

How to Cite this Article?

Robinson, T. (2019). Using the Technology Acceptance Model to Examine Technology Acceptance of Online Learning Technologies by Non-Traditional Students. i-manager’s Journal of Educational Technology, 16(1), 21-32. https://doi.org/10.26634/jet.16.1.15826

References

[1]. Capra, T. (2014). Online education from the perspective of community college students within the community of inquiry paradigm. Community College Journal of Research and Practice, 32(2-3), 108-121.

[2]. Chyung, S. Y. (2007). Age and gender differences in online behavior, self-efficacy, and academic performance. Quarterly Review of Distance Education, 8(3), 213-222.

[3]. Cole, M. T., Shelley, D. J., & Swartz, L. B. (2014). Online instruction, e-learning, and student satisfaction: A three year study. International Review of Research in Open & Distance Learning, 15(6), 111-131.

[4]. Dalcher, I., & Shine, J. (2003). Extending the new technology acceptance model to measure the end user information systems satisfaction in a mandatory environment: A bank's treasury. Technology Analysis & Strategic Management, 15 (4), 441-455.

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

[6]. Deschacht, N., & Goeman, K. (2015). The effect of blended learning on course persistence and performance of adult learners: A difference-indifferences analysis. Computers & Education, 87, 83-89.

[7]. Dumciene, A., Saulius, T., & Capskas, A. (2016). University students' attitudes towards e-learning. Baltic Journal of Sport & Health Sciences, 100(1), 2-9.

[8]. Edmunds, R., Thorpe, M., & Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: A technology acceptance model approach. British Journal of Educational Technology, 43(1), 71-84.

[9]. Ellis, R. A., Pardo, A., & Han, F. (2016). Quality in blended learning environments - Significant differences in how students approach learning collaborations. Computers & Education, 102, 90-102.

[10]. Fador, A. G. (2014). Innovation and technology acceptance model (TAM): A theoretical approach. Romanian Journal of Marketing, 2, 59-65.

[11]. Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) In higher education institutions. Journal of Online Learning & Teaching, 11(2), 210-232.

[12]. Gagnon, R. J., Stone, G. A., & Garst, B. A. (2017). Beyond composite scores and cronbach's alpha: Advancing methodological rigor in recreation research. Journal of Outdoor Recreation, Education & Leadership, 9(4), 383-403.

[13]. Gao, Y., & Wu, X. (2015). User acceptance of learning technology: The case of using moodle. International Journal of Learning: Annual Review, 21, 1-8.

[14]. Gregory, C. B., & Lampley, J. H. (2016). Community college student success in online versus equivalent faceto- face courses. Journal of Learning in Higher Education, 12(2), 63-72.

[15]. Hubona, G. S., & Whisenand, T. G. (1995). External variables and the technology acceptance. Americas Conference on Information Systems. Pittsburgh: Association for Information Systems.

[16]. Islam, A. N. (2016). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 33 (1), 48-55.

[17]. Kasim, N. N. M., & Khalid, F. (2016). Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. International Journal of Emerging Technologies in Learning, 11(6), 55-61.

[18]. Ke, F., & Kwak, D. (2013a). Constructs of studentcentered online learning on learning satisfaction of a diverse online student body: A structural equation modeling approach. Journal of Educational Computing Research, 48(1), 97-122.

[19]. Ke, F., & Kwak, D. (2013b). Online learning across ethnicity and age: A study on learning interaction participation, perception, and learning satisfaction. Computers & Education, 61, 43-51.

[20]. Kuo, Y.-C., & Belland, B. R. (2016). An exploratory study of adult learners' perceptions of online learning: Minority students in continuing education. Educational Technology Research and Development, 64(4), 661-680.

[21]. Kuo, Y.-C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and selfregulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20(1), 35-50.

[22]. Ladyshewsky, R., & Pettapiece, R. G. (2015). Exploring adult learners usage of information communication technology during a virtual peer coaching experience. Online Learning, 19(2), 107-121.

[23]. Lamar, S. B. C., Samms-Brown, C., & Brown, J. U. (2016). Technology acceptance in a sample of college students. International Journal of Education Research, 11(1), 15-26.

[24]. Liaw, S. (2008). Investigating Students' Perceived satisfaction, behavioral intention, and effectiveness of elearning: A case study of the blackboard System. Computers & Education, 51 (2), 864-873.

[25]. Mallya, J., & Lakshminarayanan, S. (2017). Factors influencing usage of internet for academic purposes using technology acceptance model. Journal of Library & Information Technology, 37 (2), 119-124.

[26]. Mountain Empire Community College. (2016). Retrieved May 11, 2017, from Mountain Empire Community College.

[27]. National Center for Education Statistics. (2016). Percentage of 18- to 24-year-olds enrolled in degreegranting postsecondary institutions, by level of institution and sex and race/ethnicity of student: 1970 through 2015 : Digest of Education Statistics. Retrieved August 22, 2017, from https://nces.ed.gov/programs/digest/d16/tables/dt16_302.60.asp

[28]. Naveh, G., Tubin, D., & Pliskin, N. (2012). Student satisfaction with learning management systems: A lens of critical success factors. Technology Pedagogy and Education, 21(3), 337-350.

[29]. Parkes, M., Stein, S., & Reading, C. (2015). Student preparedness for university e-learning environments. The Internet and Higher Education, 25, 1-10.

[30]. Pratt, T. (2017). The open access dilemma: How can community colleges better serve underprepared students? Education Next, 17(4), 34-41.

[31]. Rhode, J., Richter, S., Gowen, P., Miller, T., & Wills, C. (2017). Understanding faculty use of the learning management system. Online Learning, 21(3), 68-86.

[32]. Sondakh, J. J. (2017). Behavioral intention to use ETax service system: An application of technology acceptance model. European Research Studies Journal, 20(2A), 48-64.

[33]. Stantchev, V., Colomo-Palacios, R., Soto-Acosta, P., & Misra, S. (2014). Learning management systems and cloud file hosting services: A study on students' acceptance. Computers in Human Behavior, 31, 612- 619.

[34]. Swanke, J., & Zeman, L. D. (2015). Evaluation of nontraditional age learners' experiences in internetbased clinical social work courses. College Quarterly, 18(4), 1-9.

[35]. Tan, M., & Shao, P. (2015). An ECM-ISC based study on learners' continuance intention toward e-learning. International Journal of Emerging Technologies in Learning, 10(4), 22-27.

[36]. Thompson, N. L., Miller, N. C., & Franz, D. P. (2013). Comparing online and face-to-face learning experiences for non-tradional students. Quarterly Review of Distance Education, 14(4), 233-251.

[37]. Travers, S. (2016). Supporting Online Student Retention in Community Colleges. Quarterly Review of Distance Education, 17(4), 49-61.

[38]. Virginia Community College System. (2017). Finding a Virginia Communiity College. Retrieved December 19, 2017, from VCCS: http://www.vccs.edu/about/where-weare/college-locator/

[39]. Wingo, N. P., Ivankova, N. V., & Moss, J. A. (2017). Faculty perceptions about teaching online: exploring the literature using the technology acceptance model as an organizing framework. Online Learning, 21(1), 15-35.

[40]. Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students' course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46-57.

Using the Technology Acceptance Model to Examine Technology Acceptance of Online Learning Technologies by Non-Traditional Students

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25