Research Article

Fostering technology integration and adaptability in higher education: Insights from the COVID-19 pandemic

Premika Farsawang 1 , Noawanit Songkram 1 2 *
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1 Department of Educational Technology and Communication, Faculty of Education, Chulalongkorn University, Bangkok, THAILAND2 Learning Innovation for Thai Society Research Unit (LIFTS), Chulalongkorn University, Bangkok, THAILAND* Corresponding Author
Contemporary Educational Technology, 15(4), October 2023, ep456, https://doi.org/10.30935/cedtech/13513
Published Online: 31 July 2023, Published: 01 October 2023
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ABSTRACT

The COVID-19 pandemic led to a rapid transition to online learning, thereby significantly impacting higher education. This study examines the experiences of students, instructors, and university administrators from 22 Thai universities during the pandemic and explores the potential consequences for the future of higher education. Utilizing a mixed-methods approach, data were gathered through focus group discussions with 30 participants and a survey conducted with 510 undergraduate, graduate, and postgraduate students. The findings highlight the importance of flexibility, technology integration, and adaptability in curricula and instructional methods to enable effective online learning. Additionally, the study emphasizes the need for continuous improvement in the education sector, driven by the rapidly changing demands of the job market and the evolving nature of technology. Practical steps to be taken include prioritizing student learning outcomes, fostering digital literacy among instructors and students, and promoting collaboration across disciplines. Future research should examine the long-term impact of the pandemic on higher education and explore additional strategies for supporting students and instructors in the next normal.
Keywords: online learning, higher education, flexibility, technology integration, COVID-19, adaptability

CITATION (APA)

Farsawang, P., & Songkram, N. (2023). Fostering technology integration and adaptability in higher education: Insights from the COVID-19 pandemic. Contemporary Educational Technology, 15(4), ep456. https://doi.org/10.30935/cedtech/13513

REFERENCES

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
  3. Alhabeeb, A., & Rowley, J. (2018). Critical success factors for eLearning in Saudi Arabian universities. International Journal of Educational Management, 32(2), 374-390. https://doi.org/10.1108/IJEM-01-2016-0006
  4. Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148. https://doi.org/10.1080/01587919.2018.1553562
  5. Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
  6. Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438. https://doi.org/10.3390/su12208438
  7. Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2020). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): A multi-perspective framework. Interactive Learning Environments, 30(8), 1475-1497. https://doi.org/10.1080/10494820.2020.1734028
  8. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
  9. Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S., Al-Freih, M., Pete, J., Olcott, Jr., D., Rodes, V., Aranciaga, I., Bali, M., Alvarez, A. J., Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., de Coëtlogon, P., ... & Paskevicius, M. (2020). A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1-126.
  10. Chou, C.-P., Chen, K.-W., & Hung, C.-J. (2021). A study on flipped learning concerning learning motivation and learning attitude in language learning. Frontier in Psychology, 12, 753463. https://doi.org/10.3389/fpsyg.2021.753463
  11. Chu, A. M. Y., Liu, C. K. W., So, M. K. P., & Lam, B. S. Y. (2021). Factors for sustainable online learning in higher education during the COVID-19 pandemic. Sustainability, 13(9), 5038. https://doi.org/10.3390/su13095038
  12. Colucci, E., Smidt, H., Devaux, A., Vrasidas, C., Safarjalani, M., & Castaño Muñoz, J. (2017). Free digital learning opportunities for migrants and refugees: An analysis of current initiatives and recommendations for their further use. Publications Office of the European Union. https://doi.org/10.2760/684414
  13. Corbalán, M. F., Plaza, I. G., Hervas, E., Aldabas, E., & Arcega, F. (2013). Reduction of the students’ evaluation of education quality questionnaire. In Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (pp. 713-719). IEEE.
  14. Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P. A., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 9-28. https://doi.org/10.37074/jalt.2020.3.1.7
  15. Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research. SAGE.
  16. Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49, 91-96. https://doi.org/10.1007/s11125-020-09464-3
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  18. Ding, Y., & Zhao, T. (2020). Emotions, engagement, and self-perceived achievement in a small private online course. Journal of Computer Assisted Learning, 36(4), 449-457. https://doi.org/10.1111/jcal.12410
  19. Duarte, P. O., Raposo, M. B., & Alves, H. B. (2012). Using a satisfaction index to compare students’ satisfaction during and after higher education service consumption. Tertiary Education and Management, 18(1), 17-40. https://doi.org/10.1080/13583883.2011.609564
  20. El-Sayad, G., Saad, N. H., & Thurasamy, R. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Journal of Computers in Education, 8(4), 527-550. https://doi.org/10.1007/s40692-021-00191-y
  21. Farsawang, P., & Songkram, N. (2022, March 8-11). Factors influence students' continuance intention toward online learning transition after the COVID-19 pandemic unfolds. In N. Callaos, S. Lunsford, B. Sanchez, & A. Tremante (Eds.), ICSIT 2022 13th International Conference on Society and Information Technologies (pp. 91-96). International Institute of Informatics and Cybernetics, IIIC. https://doi.org/10.54808/ICSIT2022.01.91
  22. Ferrer, J., Ringer, A., Saville, K., Parris, M. A., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83, 317-338. https://doi.org/10.1007/s10734-020-00657-5
  23. Field, A. (2013). Discovering statistics using IBM SPSS statistics. SAGE.
  24. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
  25. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
  26. Garrison, D. R., & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. John Wiley & Sons. https://doi.org/10.1002/9781118269558
  27. Greiner, K. R. (2000). A study of academic service quality and instructional quality in a midwestern higher education environment [Doctoral dissertation, Drake University].
  28. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Pearson.
  29. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 3, 1-12.
  30. Horzum, M. B., Kaymak, Z. D., & Gungoren, O. C. (2015). Structural equation modeling towards online learning readiness, academic motivations, and perceived learning. Educational Sciences: Theory & Practice, 15(3), 759-770. https://doi.org/10.12738/estp.2015.3.2410
  31. Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090. https://doi.org/10.1016/j.compedu.2010.05.004
  32. Inthason, S. (2020). Kho wid-19: Kạb kār reīyn kār s̄xn xxnlịn̒ krṇī ṣ̄ụks̄ʹā rāywichā kār k̄heīyn porkærm wĕb [COVID-19 and online teaching case study: Web programming course]. Journal of Management Science Review, 22(2), 203-214.
  33. Johnson, N., Veletsianos, G., & Seaman, J. (2019). U.S. faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learning, 24(2), 6-21. https://doi.org/10.24059/olj.v24i2.2285
  34. Ketut, A., Suryani, N., & Putra, I. (2021). The influence of information system quality, e-learning quality, and motivation on students’ satisfaction and loyalty. Journal of Physics: Conference Series, 1807(1), 012062.
  35. Khalil, R., Mansour, A. E., Fadda, W. A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A., & Al-Wutayd, O. (2020). The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education, 20, 285. https://doi.org/10.1186/s12909-020-02208-z
  36. Kim, J., Park, J. H., & Cozart, J. (2014). Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology, 45(1), 171-185. https://doi.org/10.1111/j.1467-8535.2012.01382.x
  37. Kirmizi, O. (2015). The influence of learner readiness on student satisfaction and academic achievement in an online program at higher education. Turkish Online Journal of Educational Technology, 14(1), 133-142.
  38. Kline, R. B. (2011). Principles and practice of structural equation modeling. The Guilford Press.
  39. Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001
  40. Lin, C.-L., Jin, Y. Q., Zhao, Q., Yu, S.-W., & Su, Y.-S. (2021). Factors influence students’ switching behavior to online learning under COVID-19 pandemic: A push-pull-mooring model perspective. The Asia–Pacific Education Researcher, 30(3), 229-245. https://doi.org/10.1007/s40299-021-00570-0
  41. Liu, N., & Pu, Q. (2020). Factors influencing learners’ continuance intention toward one-to-one online learning. Interactive Learning Environments, 31(3), 1742-1763. https://doi.org/10.1080/10494820.2020.1857785
  42. Luo, N., Zhang, M., & Qi, D. (2017). Effects of different interactions on students’ sense of community in e-learning environment. Computers & Education, 115, 153-160. https://doi.org/10.1016/j.compedu.2017.08.006
  43. Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 26, 6629-6649. https://doi.org/10.1007/s10639-021-10465-8
  44. Mamun, M. A., Lawrie, G., & Wright, T. (2020). Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers & Education, 144, 103695. https://doi.org/10.1016/j.compedu.2019.103695
  45. Marsh, H. W. (1987). Students’ evaluations of university teaching: Research findings, methodological issues, and directions for future research. International Journal of Educational Research, 11(3), 253-388. https://doi.org/10.1016/0883-0355(87)90001-2
  46. Milosievski, M., Zemon, D., Stojkovska, J., & Popovski, K. (2020, 19 May). Learning online: Problems and solutions. UNICEF Young Reporters. https://www.unicef.org/northmacedonia/stories/learning-online-problems-and-solutions
  47. Nachmias, R., & Nachmias, C. (1993). Research methods in the social sciences. St. Martin’s Press.
  48. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.2307/3150499
  49. Ostrom, T. M. (1969). The relationship between the affective, behavioral, and cognitive components of attitude. Journal of Experimental Social Psychology, 5(1), 12-30. https://doi.org/10.1016/0022-1031(69)90003-1
  50. Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229. https://doi.org/10.1016/j.compedu.2009.08.005
  51. Pereira, F. A. d. M., Ramos, A. S. M., Gouvêa, M. A., & da Costa, M. F. (2015). Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, 46, 139-148. https://doi.org/10.1016/j.chb.2015.01.016
  52. Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitudes. In M. J. Rosenberg, C. I. Hovland, W. J. McGuire, R. P. Abelson, & J. W. Brehm (Eds.), Attitude organization and change: An analysis of consistency among attitude components (pp. 1-14). Yale University Press.
  53. Sahu, P. (2020). Closure of universities due to coronavirus disease 2019 (COVID-19): Impact on education and mental health of students and academic staff. Cureus, 12(4), e7541. https://doi.org/10.7759/cureus.7541
  54. Selwyn, N. (2011). Education and technology: Key issues and debates. A&C Black.
  55. Sneader, K., & Singhal, S. (2020). Beyond coronavirus: The path to the next normal. In The path to the next normal: Leading with resolve through the coronavirus pandemic (pp. 6-10). McKinsey & Company.
  56. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
  57. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
  58. Thanuttamanon, P., & Onputtha, S. (2019). The effect of motivation, behavior and confidence on learning performance of students in the faculty of business administration, RMUTT, in learning English through social media. International Journal of Applied Computer Technology and Information Systems, 9(1), 20-28.
  59. Thongsri, N., Chootong, C., Tripak, O., Piyawanitsatian, P., & Saengae, R. (2021). Predicting the determinants of online learning adoption during the COVID-19 outbreak: A two-staged hybrid SEM-neural network approach. Interactive Technology and Smart Education, 18(3), 362-379. https://doi.org/10.1108/ITSE-08-2020-0165
  60. Tsai, Y.-H., Lin, C.-H., Hong, J.-C., & Tai, K.-H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29. https://doi.org/10.1016/j.compedu.2018.02.011
  61. 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
  62. Virtič, M. P., Dolenc, K., & Šorgo, A. (2021). Changes in online distance learning behavior of university students during the coronavirus disease 2019 outbreak, and development of the model of forced distance online learning preferences. European Journal of Educational Research, 10(1), 393-411. https://doi.org/10.12973/eu-jer.10.1.393
  63. Watjatrakul, B. (2016). Online learning adoption: Effects of neuroticism, openness to experience, and perceived values. Interactive Technology and Smart Education, 13(3), 229-243. https://doi.org/10.1108/ITSE-06-2016-0017
  64. Wu, Y.-C., Hsieh, L.-F., & Lu, J.-J. (2015). What’s the relationship between learning satisfaction and continuing learning intention? Procedia-Social and Behavioral Sciences, 191, 2849-2854. https://doi.org/10.1016/j.sbspro.2015.04.148
  65. Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak. Journal of Risk and Financial Management, 13(3), 55. https://doi.org/10.3390/jrfm13030055
  66. Zhou, X., Chai, C. S., Jong, M. S.-Y., & Xiong, X. B. (2021). Does relatedness matter for online self-regulated learning to promote perceived learning gains and satisfaction? The Asia–Pacific Education Researcher, 30(3), 205-215. https://doi.org/10.1007/s40299-021-00579-5