Skip to main content

Advertisement

Log in

Framework design of university communication model (UCOM) to enhance continuous intentions in teaching and e-learning process

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The technology enhancement learning (TEL) needs continuous use and high perception from learners with collaborative of technologies and multi-media applications. The problem of continuous intention in e-learning applications relies on the type of technology used that changes from one university to another. This study aims to design a framework developed from University Communication Model (UCOM) model to enhance the teaching and learning process of universities. This framework gives a high rate of significance and accurate results by examining the relationship between the factors of e-learning application and technology acceptance model (TAM). In additions, an extra factor used for continuous intention to use e-learning applications. The method used shows a survey distributed between 297 participants to validate the hypothesis proposed. The results evaluated by partial-least-structure (PLS) program, suggest and prove the significant use of (UCOM) as a continuous intention in e-learning based on 16 tested hypotheses of model item constructs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abdullah, S., Abd Wahab, D., & Hussein, S. M. (2012). Development of a quality assurance plan in line with UKM's status as a self-accreditation institution and research university. Procedia - Social and Behavioral Sciences, 59, 95–104.

    Google Scholar 

  • Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. In Thirty fourth international conference on information systems, Milan, 2013

  • Adwan, J. (2016). Dynamic online peer evaluations to improve group assignments in a nursing E-learning environment. Nurse Education Today, 41, 67–72.

    Google Scholar 

  • Albelbisi, N. A. (2019). The role of quality factors in supporting self-regulated learning (SRL) skills in MOOC environment. Education and Information Technologies, 24(2), 1681–1698.

    Google Scholar 

  • Aldiab, A., Chowdhury, H., Kootsookos, A., & Alam, F. (2017). Prospect of eLearning in higher education sectors of Saudi Arabia: A review. Energy Procedia, 110, 574–580.

    Google Scholar 

  • Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students acceptance of Google classroom: An exploratory study using PLS-SEM approach. International Journal of Emerging Technologies in Learning (iJET), 13(06), 112–123.

    Google Scholar 

  • Al-Qirim, N., Tarhini, A., Rouibah, K., Mohamd, S., Yammahi, A. R., & Yammahi, M. A. (2018). Learning orientations of IT higher education students in UAE University. Education and Information Technologies, 23(1), 129–142.

    Google Scholar 

  • Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28–38.

    Google Scholar 

  • AlYahya, S. A., & Abo El-Nasr, A.-B. A. (2012). Outcomes-Based Assessment Of The Engineering Programs At Qassim University For Abet Accreditation. International Conference on Interactive Mobile and Computer Aided Learning (IMCL) (pp. 22–31). Amman, Jordan: 978–1–4673-4925-3/12/$31.00 ©2012 IEEE.

  • Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260.

  • Barak, M., & Levenberg, A. (2016a). Flexible thinking in learning: An individual difference measure for learning in technology-enhanced environments. Computers & Education, 99, 39–52.

    Google Scholar 

  • Barak, M., & Levenberg, A. (2016b). 2016, flexible thinking in learning: An individual differences measure for learning in technology-enhanced environments. Computers & Education, 99, 39–52.

    Google Scholar 

  • Beleche, T., Fairris, D., & Marks, M. (2012). Do course evaluations truly reflect student learning? Evidence from an objectively graded post-test. Economics of Education Review, 31, 709–719.

    Google Scholar 

  • Benson, P. (2013). Teaching and researching: Autonomy in language learning. London: Routledge.

    Google Scholar 

  • Bookstaver, P., Rudisill, C. N., Bickley, A., McAbee, C., Miller, A. D., Piro, C., et al. (2011). An evidence-based medicine elective course to improve student performance in advanced pharmacy practice experiences. American Journal of Pharmaceutical Education, 75(1), 9.

    Google Scholar 

  • Bringula, R. P. (2013). Influence of faculty- and web portal design-related factorson web portal usability: a hierarchical regression analysis. Computers & Education, 68(10), 187–198.

  • Cavanagh, M., Bower, M., Moloney, R., & Sweller, N. (2014). The effect over time of a video-based reflection system on preservice teachers' oral presentations. Australian Journal of Teacher Education, 39(6), 1–16.

    Google Scholar 

  • Chen, H.-J. (2010). Linking employees’ E-learning system use to their overall job outcomes: An empirical study based on the IS success model. Computers in Education, 55, 1628–1639.

    Google Scholar 

  • Chmiel, A. S., Shaha, M., & Schneider, D. K. (2017). Introduction of blended learning in a master program: Developing an integrative mixed-method evaluation framework. Nurse Education Today/Science direct- Elsevier, 172-179.

  • Christensen, R., & Knezek, G. (2017). Readiness for integrating mobile learning in the classroom: Challenges, preferences and possibilities. Computers in Human Behavior, 76, 112–121.

  • Clark, R. E. (1999). Yin and yang cognitive motivational processes operating in multimedia learning environments. In J. J. G. Van Merrienboer (Ed.), Cognition and multimedia design (pp. 1–38). Heleen: Open University Press.

    Google Scholar 

  • Dargham, J. A., Chekima, A., Chin, R. K. Y, & Wong, F. (2013). A Direct Assessment Method of the Achievement of the Program Outcomes from the Courses Outcomes. IEEE 5th Conference on Engineering Education (ICEED) (pp. 131–135).

  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).

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

    Google Scholar 

  • Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215e235.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Fricker, R. D., & Matthias, S. (2002). Advantages and disadvantages of internet research surveys: Evidence from the literature. Field Methods, 14(4), 347–367.

  • Graffigna, A. M., Ghilardi, L., Fraca, C., Morell, M. D., Simonassi, M. L., Bartol, R., et al. (2014). University evaluation. From the program's accreditation to the institutional evaluation. 5th World Conference on Educational Sciences - WCES 2013. Procedia - Social and Behavioral Sciences, 116, 2635–2639.

    Google Scholar 

  • Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925–955.

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results, and higher acceptance. Long range planning, 46(1–2), 1–12.

  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Jr., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.

    Google Scholar 

  • Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168.

    Google Scholar 

  • Hong, J. Y., Suh, E. H., & Kim, S. J. (2009a). Context-aware systems: A literature review and classification. Expert Systems with Applications, 36(4), 8509e8522.

  • Hong, J. Y., Suh, E. H., & Kim, S. J. (2009b). Context-aware systems: A literature review and classification. Expert Systems with Applications, 36(4), 8509e8522.

  • Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84–91.

    Google Scholar 

  • Hutchinson, D., & Wells, J. (2013). An inquiry into the effectiveness of student generated MCQs as a method of assessment to improve teaching and learning. Creative Education, 4(07), 117.

  • Hwang, W. Y., Li, Y. H., & Shadiev, R. (2018). Exploring effects of discussion on visual attention, learning performance, and perceptions of students learning with STR-support. Computers & Education, 116, 225–236.

  • Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates’ perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics, 35(1), 93–102.

  • Ioannou, A., Brown, S., & Artino, A. R. (2015). Wikis and forums for collaborative problem-based activity: A systematic comparison of learners' interactions. The Internet and Higher Education, 24, 35–45.

    Google Scholar 

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

    Google Scholar 

  • Janićijević, N. (2015). The reactions of universities to imposing new an institutional pattern: The case of higher education in Serbia. Procedia - Social and Behavioral Sciences, 174, 1550–1559.

    Google Scholar 

  • Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers in Education, 122, 260272.

    Google Scholar 

  • Kitchenham, B., & Pfieeger, S. L. (2002). Principles of survey research part 4: Questionnaire evaluation. SIGSOFT Software Engineer Notes, 27(3), 20–23.

    Google Scholar 

  • Kitchenham, B., & Pfleeger, S. L. (2002). Principles of survey research part 5: Populations and samples. ACM SIGSOFT, Software Engineering Notes, 27(5), 17–20.

    Google Scholar 

  • Kleebbua, C., & Siriparp, T. (2016). Effects of Education and Attitude on Essential Learning Outcomes. Procedia - Social and Behavioral Sciences, 217, 941–949 Future Academy®‘s Multidisciplinary Conference.

    Google Scholar 

  • Lee, Y., & Choi, J. (2013). A structural equation model of predictors of online learning retention. The Internet and Higher Education, 16, 36–42.

    Google Scholar 

  • 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.

  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers in Education, 61, 193–208.

    Google Scholar 

  • Lee, C., Yeung, A. S., & Ip, T. (2017). University English language learners’ readiness to use computer technology for self-directed learning. System, 67, 99–110.

    Google Scholar 

  • Lin, C. S., & Wu, R. Y. W. (2016). Effects of web-based creative thinking teaching on students’ creativity and learning outcome. Eurasia Journal of Mathematics, Science & Technology Education, 12(6), 1675–1684.

    Google Scholar 

  • Lin, M. H., Chen, H. C., & Liu, K. S. (2017). A Study of the Effects of Digital Learning on Learning Motivation and Learning Outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553–3564. https://doi.org/10.12973/eurasia.2017.00744a.

  • Liu, Y. (2016). The path choice of the localization course of MOOC in Chinese colleges and universities in the view of the disputes behind the MOOC. Open Journal of Social Sciences, 4(08), 54–59.

    Google Scholar 

  • Liu, C., & Chen, L.-M. (2012 ). Selective and objective assessment calculation and automation. ACMSE'12, Tuscaloosa, AL, USA.

  • Lonka, K., & Ahola, K. (1995). Activating instruction: How to foster study and thinking skills in higher education. European Journal of Psychology of Education, 10(4), 351–368.

  • Lytras, M. D., Mathkour, H. I., Abdalla, H., Al-Halabi, W., Yanez-Marquez, C., & Siqueira, S. W. M. (2015). An emerging–social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high effective next-generation learning systems for the knowledge society. Computers in Human Behavior, 51, 557–561.

    Google Scholar 

  • Maas, A., Heather, C., Do, C. T., Brandman, R., Koller, D., & Ng, A. (2014). Offering verified credentials in massive open online courses: MOOCs and technology to advance learning and learning research (ubiquity symposium). Ubiquity, 2014(May), 2.

    Google Scholar 

  • Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29(4), 531e563.

  • Miri Barak, Ariella Levenberg, 2016, Flexible thinking in learning: An individual differences measure for learning in technology-enhanced environments, Computers & Education 99 (2016) 39-52.

  • Mohammadi, R., Eshaghi, F., & Arefi, M. (2012). Internal Evaluation: Appropriate Strategic for Quality Evaluation and Improvement of Management in Departments at Universities(The Case of Iran). International Conference on Education and Educational Psychology (ICEEPSY 2012) (pp. 719–728). Procedia - Social and Behavioral Sciences 69/Elsevier.

  • Mullen, J., Byun, C., Gadepally, V., Samsi, S., Reuther, A., & Kepner, J. (2017). Learning by doing, high-performance computing education in the MOOC era. Journal of Parallel and Distributed Computing, 105, 105–115.

    Google Scholar 

  • Moloo, R. K., Khedo, K. K., & Prabhakar, T. V. (2018). Critical evaluation of existing audio learning systems using a proposed TOL model.Computers & Education, 117, 102-115.

  • Nawrot, I., & Doucet, A. (2014). Building engagement for MOOC students: Introducing support for time management on online learning platforms. In Proceedings of the 23rd International Conference on World Wide Web (pp. 1077-1082). ACM.

  • O’Bannon, B., & Britt, V. G. (2011). Creating/developing/using a wiki study guide: Effects on student achievement. Journal of Research on Technology in Education, 44(4), 293–312.

    Google Scholar 

  • Peltier, J. W., Drago, W., & Schibrowsky, J. A. (2003). Virtual communities and the assessment of online marketing education. Journal of Marketing Education, 25(3), 260–276.

    Google Scholar 

  • Pilditch, T. D., & Custers, R. (2018). Communicated beliefs about action-outcomes: The role of initial confirmation in the adoption and maintenance of unsupported beliefs. Acta Psychologica, 184, 46–63.

    Google Scholar 

  • Posey, L., & Pintz, C. (2016). Transitioning a bachelor of science in nursing program to blended learning: Successes, challenges & outcomes. Nurse Education in Practice, 26, 126-133.

  • Ralph, N., Birks, M., & Chapman, Y. (2015). The accreditation of nursing education in Australia. ScienceDirect-Collegian, 22, 3–7.

    Google Scholar 

  • Ringle, C.M., Wende, S., Will, A., 2005. Smart PLS 2.0 M3. The University of Hamburg, <www.smartpls.de>.

  • Salajan, F. D., & Mount, G. J. (2012). Leveraging the power of web 2.0 tools: A wiki platform as a multimedia teaching and learning environment in dental education. Journal of Dental Education, 76(4), 427–436.

    Google Scholar 

  • Schmid, R. F., Bernard, R. M., Borokhovski, E., Tamim, R. M., Abrami, P. C., Surkes, M. A., Wade, C. A., & Woods, J. (2014). The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education, 72, 271–291.

    Google Scholar 

  • Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach. John Wiley & Sons.

  • Serhani, M. A., Bouktif, S., Al-Qirim, N., & El Kassabi, H. T. (2019). Automated system for evaluating higher education programs. Education and Information Technologies, 1-22.

  • Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A re-examination of the determinants of Sorensen, C. W., Furst-Bowe, J. A., & Moen, D. M. (Eds.). (2005). Quality and performance excellence in higher education: Baldrige on campus (Vol. 53). Jossey-Bass.

  • Strang, K. D. (2013). University accreditation and benchmarking: Pedagogy that increases student achievement. International Journal of Educational Research/Science direct-Elsevier, 210-219.

    Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information on technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

  • Tawafak, R. M., Mohammed, M. N., Arshah, R. B. A., Shakir, M., & Mezhuyev, V. (2018a). Technology enhancement learning reflection on improving students’ satisfaction in Omani universities. Advanced Science Letters, 24(10), 7751–7757.

    Google Scholar 

  • Tawafak, R. M., Romli, A. B., bin Abdullah Arshah, R., & Almaroof, R. A. S. (2018b). Assessing the impact of technology learning and assessment method on academic performance. EURASIA Journal of Mathematics, Science and Technology Education, 14(6), 2241–2254.

    Google Scholar 

  • Tawafak, R. M., Mohammed, M. N., Arshah, R. B. A., & Romli, A. (2018c). Review on the effect of student learning outcome and teaching Technology in Omani's higher education Institution's academic accreditation process. In Proceedings of the 2018 7th International Conference on Software and Computer Applications (pp. 243-247). ACM.

  • Tawafak, R. M., Romli, A. B., & Arshah, R. B. A. (2018d). Continued intention to use UCOM: Four factors for integrating with a technology acceptance model to moderate the satisfaction of learning. IEEE Access, 6, 66481–66498.

    Google Scholar 

  • Tawafak, R. M., Romli, A. B., & Alsinani, M. (2018e). E-learning system of UCOM for improving student assessment feedback in Oman higher education. Education and Information Technologies, 24(2), 1311-1335.

  • Tawafak, R. M., Romli, A., Malik, S. I., Shakir, M., & Farsi, G. A. (2019). A systematic review of personalized learning: Comparison between E-learning and learning by coursework program in Oman. International Journal of Emerging Technologies in Learning, 14(9).

  • Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5–23.

    Google Scholar 

  • Watson, S. L., Watson, W. R., Yu, J. H., Alamri, H., & Mueller, C. (2017). Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study. Computers & Education, 114, 274–285.

  • Wilby, K. J., Zolezzi, M., & El-Kadi, A. (2017). Development of a college-level assessment framework in line with international accreditation standards: A Middle Eastern perspective. Currents in Pharmacy Teaching and Learning Methods, 14(4), 347–367.

    Google Scholar 

  • Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.

  • Wu, B., & Chen, X. (2017a). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

  • Wu, B., & Chen, X. (2017b). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

  • Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027–1038.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ragad M. Tawafak.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 11 Variables with detailed items and references

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tawafak, R.M., Romli, A.B.T., Arshah, R.b.A. et al. Framework design of university communication model (UCOM) to enhance continuous intentions in teaching and e-learning process. Educ Inf Technol 25, 817–843 (2020). https://doi.org/10.1007/s10639-019-09984-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-019-09984-2

Keywords

Navigation