Volume 7, Issue 3 (9-2022)                   IJREE 2022, 7(3): 1-15 | Back to browse issues page

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University of the Immaculate Conception, Philippines
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This study aims to determine whether the perceived ease of use and perceived usefulness can significantly predict the students’ intention to use video conferencing applications in an online classroom. This study utilized a descriptive predictive quantitative research design. This study used the survey questionnaires adopted from the study of Salloum et al. (2019). The researchers conducted an online survey using Google form for over a month. Out of 153 target respondents, 130 responded to the survey questionnaire. Linear regression was initiated using JASP The findings revealed that the two variables perceived ease of use and perceived usefulness can both significantly predict the students’ intention to use video conferencing applications in an online classroom. The results further showed that the perceived ease of use can better predict the students’ intention to use video conferencing applications in an online classroom as compared to perceived usefulness. The findings imply that video conferencing applications used in the teaching and learning process should be user-friendly and pedagogically relevant to support students’ desire to use video conferencing applications.
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1. Alrajawy, I., & Isaac, O., Ghosh, A., Nusari, M., Al-Shibami, A., & Ameen, A. (2018). Determinants of student's intention to use mobile learning in Yemeni public universities: Extending the technology acceptance model (TAM) with anxiety. International Journal of Management and Human Science (IJMHS), 2(2), 1-9. Retrieved from https://ejournal.lucp.net/index.php/ijmhs/article/view/819
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. [DOI:10.1016/0749-5978(91)90020-T]
3. Ajzen, I., & Fishbein, M. (1980). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1), 1-33. [DOI:10.1080/14792779943000116]
4. Armstrong, K. J., & Mulvihill, T. M. (2007). Undergraduate students' perceptions of online learning. 26th Annual Midwest Research-to-Practice Conference in Adult, Continuing, Community, and Extension Education , 7-12.
5. Avsheniuk, N., Seminikhyna, N., Svyrydiuk, T., & Lutsenko, O. (2021). ESP students' satisfaction with online learning during the COVID-19 pandemic in Ukraine. Arab World English Journal, 1, 222-234. [DOI:10.24093/awej/covid.17]
6. Bailey, D., Almusharraf, A., & Almusharraf, A. (2022). Video conferencing in the e‑learning context: explaining learning outcome with the technology acceptance model. Educ Inf Technol 27, 7679-7698 (2022). [DOI:10.1007/s10639-022-10949-1]
7. Baki, R., Birgoren, B., & Aktepe, A. (2018). A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in the Adoption of E-Learning Systems. Turkish Online Journal of Distance Education, 19(4), 4-42. https://files.eric.ed.gov/fulltext/EJ1192753.pdf [DOI:10.17718/tojde.471649]
8. Buabeng-Andoh, C. (2018). Predicting students' intention to adopt mobile learning. A combination of theory of reasoned action and technology acceptance model. Department of Information Technology, Pentencost University College. [DOI:10.1108/JRIT-03-2017-0004]
9. Chazen, D. (2021). Video conferencing in education: Additional alternative or future of education. verbit. https://verbit.ai/video-conferencing-in-education/
10. Chiu, C. M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information and Management, 45(3), 194-201. https://doi.org/10.1016/j.im.2008.02.003 [DOI:10.1016/J.IM.2008.02.003]
11. Cigdem, H., & Ozturk, M. (2016). Factors affecting students' behavioral intention to use LMS at a Turkish post-secondary vocational school. The International Review of Research in Open and Distributed Learning, 17(3), 276-295. https://doi.org/10.19173/irrodl.v17i3.2253 [DOI:10.19173/IRRODL.V17I3.2253]
12. Cigdem, H., & Topcu, A. (2015). Predictors of instructors' behavioral intention to use learningmanagement system: A Turkish vocational college example. Computers in Human Behavior, 52, 22-28. doi:10.1016/j.chb.2015.05.049 [DOI:10.1016/j.chb.2015.05.049]
13. Coman, C., Țîru, L. G., Meseșan-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online teaching and learning in higher education during the coronavirus pandemic: Students' perspective. Sustainability (Switzerland), 12(24), 1-22. [DOI:10.3390/su122410367]
14. COVID Live Update: 248,795,426 Cases and 5,036,594 Deaths from the Coronavirus - Worldometer. (2021). Worldometer. https://www.worldometers.info/coronavirus/
15. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user 13
16. information systems: theory and results. Massachusetts Institute of Technology. Retrieved
17. from https://dspace.mit.edu/handle/1721.1/15192#files-area
18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of [DOI:10.2307/249008]
19. information technology. MIS Quarterly, 13(3), 319. [DOI:10.2307/249008]
20. Davis, F. D., & Venkatesh, V. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. [DOI:10.1111/j.1540-5915.1996.tb00860.x]
21. Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5-22. [DOI:10.1177/0047239520934018]
22. Doggett, A. M. (2008). The videoconferencing classroom: What do students think? Journal of Industrial Teacher Education, 44(4), 29-41. https://digitalcommons.wku.edu/cgi/viewcontent.cgi?article=1002&context=arch_mfg_fac_pub
23. Easton, V. J., & McHoll John H. (n.d.). Statistics Glossary - sampling.
24. Faul, F. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://doi.org/10.3758/BF03193146 [DOI:10.3758/bf03193146]
25. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
26. 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, 644-649. https://doi.org/10.1016/S2212-5671(16)00079-4 [DOI:10.1016/s2212-5671(16)00079-4]
27. Ibrahim, H., Hassan, M. R., Abdu, S. B., Chidinma, F., Aliyu, Z. I., Bello, S. S., & Ishiaku, Y. M. (2018). Blood biochemical profile and carcass characteristics of weaner rabbits fed varying inclusion levels of gamba grass (Andropogon gayanus kunth.) forage. Nigerian J. Anim. Sci., 20(4), 552-560.
28. Kassymova, G., Arpentieva, M., Kosherbayeva, A., Triyono, M., & Sangilbayev, O. (2019b). Science, education & cognitive competence based on e-learning. Bulletin of National academy of sciences of the Republic of Kazakhstan, 1(377), 269-278. [DOI:10.32014/2019.2518-1467.31]
29. Kassymova, G., Bekalaeva, A., Yershimanova, D., Flindt, N., Gadirova, T., & Duisenbayeva, S. H. (2020). E-Learning environments and their connection to the human brain. International Journal of Advanced Science and Technology, 29(9s), 947-954. https://www.researchgate.net/publication/341162228_E-Learning_Environments_and_Their_Connection_to_the_Human_Brain
30. Kassymova, G. K., Duisenbayeva Sh. S., Adilbayeva U. B., Khalenova, A. R, Kosherbayeva, A. N., Triyono, M. B., Sangilbayev, O. S. (2019). Cognitive competence based on the E-learning. International Journal of Advanced Science and Technology, 28(18), 167-177. https://www.researchgate.net/publication/338138876_Cognitive_Competence_Based_on_the_E-Learning
31. Lai, P. (2017). The Literature Review of Technology Adoption Models and Theories for the Novelty Technology. Journal of Information Systems and Technology Management, 14(1), 21-38.https://www.researchgate.net/publication/317412296_THE_LITERATURE_REVIEW_OF_TECHNOLOGY_ADOPTION_MODELS_AND_THEORIES_FOR_THE_NOVELTY_TECHNOLOGY [DOI:10.4301/S1807-17752017000100002]
32. Lanlan, Z., Ahmi, A., & Popoola O. M. (2009). Perceived ease of use, perceived usefulness and the usage of computerized accounting systems: A performance of micro and small enterprises (MSEs) in China. (2019). International Journal of Recent Technology and Engineering, 8(2S2), 324-331. https://doi.org/10.35940/ijrte.B1056.0782S219 [DOI:10.35940/ijrte.b1056.0782s219]
33. Lew, L. C., Hor, Y. Y., Yusoff, N. A. A., Choi, S. B., Yusoff, M. S. B., Roslan, N. S., Ahmad, A., Mohammad, J. A. M., Abdullah, M. F. I. L., Zakaria, N., Wahid, N., Sun, Z., Kwok, L. Y., Zhang, H., & Liong, M. T. (2019). Probiotic Lactobacillus plantarum P8 alleviated stress and anxiety while enhancing memory and cognition in stressed adults: A randomised, double-blind, placebo-controlled study. Clinical Nutrition (Edinburgh, Scotland), 38(5), 2053-2064. [DOI:10.1016/j.clnu.2018.09.010]
34. Liaw, S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning A case study of the Blackboard system. Computers & Education, 51, 864-873. - References - Scientific Research Publishing. (n.d.). Www.scirp.org. [DOI:10.1016/j.compedu.2007.09.005]
35. Mobo, F. D. (2020). The impact of video conferencing platform in all educational sectors amidst Covid-19 pandemic. Aksara: Jurnal Ilmu Pendidikan Nonformal, 7(1), 15. [DOI:10.37905/aksara.7.1.15-18.2021]
36. Nguyen, X. A., Pho, D. H., Luong, D. H., & Cao, X. T. A. (2021). Vietnamese students' acceptance of using video conferencing tools in distance learning in COVID-19 pandemic. Turkish Online Journal of Distance Education, 22(3), 139-162. https://eric.ed.gov/?id=EJ1301278 [DOI:10.17718/tojde.961828]
37. Nguyen et al. [2021]. (n.d.). Sci.esa.int. Retrieved September 19, 2022, from https://sci.esa.int/web/hubble/display-page-m-display-page-media/-/asset_publisher/34279/content/nguyen-et-al.-2021-
38. Oranburg, S. (2020, March 13). Distance education in the time of coronavirus: Quick and easy strategies for professors. Papers.ssrn.com. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3553911 [DOI:10.2139/ssrn.3553911]
39. Panergayo, A. A., & Aliazas, J. V. (2021). Students' behavioral intention to use learning management system: The mediating role of perceived usefulness and ease of use. (n.d.). Scholar.google.com. Retrieved September 19, 2022, from https://scholar.google.com/citations?view_op=view_citation&hl=en&user=5WB7XAkAAAAJ&citation_for_view=5WB7XAkAAAAJ:Tyk-4Ss8FVUC
40. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-Learning. Educational Technology & Society 12(3), 150-162. https://www.researchgate.net/publication/220374248_An_Analysis_of_the_Technology_Acceptance_Model_in_Under
41. Permatasari, A. N., & Oktiawati, U. Y. (2021). Preferred online learning method during COVID-19 pandemic: A students' perspective. Parole: Journal of Linguistics and Education, 11(1), 1-9. [DOI:10.14710/parole.v11i1.1-9]
42. Ping, L., & Liu, K. (2020). Using the technology acceptance model to analyze K-12 students' behavioral intention to use augmented reality in learning. Texas Education Review, 8(2), 37-51. http://dx.doi.org/10.26153/tsw/9204
43. Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T., Young, M. N. et al. (2021). Determining factors affecting acceptance of E-learning platforms during the COVID-19 pandemic: Integrating extended technology acceptance model and DeLone & McLean IS Success Model. Sustainability, 13(15), 8365. MDPI AG. http://dx.doi.org/10.3390/su13158365 [DOI:10.3390/su13158365]
44. Pratama, H., Azman, M. N. A., Kassymova, G. K., & Duisenbayeva, S. S. (2020). The Trend in Using Online Meeting Applications for Learning During the Period of Pandemic COVID-19: A Literature Review. Journal of Innovation in Educational and Cultural Research, 1(2), 58-68. https://doi.org/10.46843/jiecr.v1i2.15 [DOI:10.46843/JIECR.V1I2.15]
45. Pratama et al. (2020). The use of Youtube as a learning tool in teaching listening skill. International Journal of Global Operations Research, 1(3), 123-129. [DOI:10.47194/ijgor.v1i3.50]
46. Price, D., Scadding, G., Ryan, D., Bachert, C., Canonica, G. W., Mullol, J., Klimek, L., Pitman, R., Acaster, S., Murray, R., & Bousquet, J. (2015). The hidden burden of adult allergic rhinitis: UK healthcare resource utilisation survey. Clinical and Translational Allergy, 5(1), 39. [DOI:10.1186/s13601-015-0083-6]
47. Rayhan, R. U. (2013). Administer and collect medical questionnaires with Google documents: a simple, safe, and free system. Applied Medical Informatics, 33(3), 12-21. https://pubmed.ncbi.nlm.nih.gov/24415903/
48. Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring students' acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445-128462. [DOI:10.1109/ACCESS.2019.2939467]
49. Samuel, N., Onasanya, S. A., & Olumorin, C. O. (2018). Perceived usefulness, ease of use and adequacy of use of mobile technologies by Nigerian university lecturers. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 14(3), 5-16. https://files.eric.ed.gov/fulltext/EJ1201530.pdf?fbclid=IwAR1IYC_EFDblAqSykPQFvEGbwpo-NfKHyhqV4jY-jihpCc10nxT3YbKPay8
50. Simbulan, N. P. (2020, June 20). The Philippines - COVID-19 and its impact on higher education in the Philippines. https://headfoundation.org/2020/06/04/covid-19-and-its-impact-on-higher-education-in-the-philippines/
51. Stankovska, G., Dimitrovski, D., Ibraimi, Z., & Memedi, I. (2021). Online learning, social presence and satisfaction among university students during the COVID-19 pandemic. Bulgarian Comparative Education Society, Paper presented at the Annual International Conference of the Bulgarian Comparative Education Society (BCES) (19th, Sofia, Bulgaria, Jun 2021). https://eric.ed.gov/?id=ED613967
52. Sumak, B., Hericko, M., Pusnik, M. & Polancic, G. (2011). Factors affecting acceptance and use of Moodle: An empirical study based on TAM. Informatica, 35, 91-100. https://www.researchgate.net/publication/266074838_Factors_Affecting_Acceptance_and_Use_of_Moodle_An_Empirical_Study_Based_on_TAM
53. Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use E-Filing: The role of technology readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537-547. [DOI:10.13106/jafeb.2020.vol7.no9.537]
54. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. http://www.jstor.org/stable/23011007 [DOI:10.1287/isre.6.2.144]
55. Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. Elsevier Ltd. Retrieved September 22, 2022 from https://www.learntechlib.org/p/50809/Teo, T., & van Schaik, P. (2012). Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188. [DOI:10.1080/10447318.2011.581892]
56. Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52(5), 463-479. [DOI:10.1016/j.infsof.2009.11.005]
57. UNICEF Annual Report. (2021). Www.unicef.org. https://www.unicef.org/reports/unicef-annual-report-2021
58. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. [DOI:10.1111/j.1540-5915.1996.tb00860.x]
59. Venkatesh, V., Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. http://dx.doi.org/10.1287/mnsc. [DOI:10.1287/mnsc.]
60. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. [DOI:10.2307/30036540]
61. Wong, K. T., Teo, T., Russo, S., & Russo, S. (2012). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28(7). [DOI:10.14742/ajet.796]
62. WorldOMeter. (2021). Coronavirus toll update: Cases & deaths by country. Worldometers. https://www.worldometers.info/coronavirus/
63. Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e‐learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229-243. [DOI:10.1080/13598660802232779]

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