Research Article

Investigating Factors Influencing Students’ Behavioral Intentions Towards Mobile Learning Devices in Higher Educational Institutions

Gopolang Ditlhokwa 1 2 *
More Detail
1 Communication University of China, Beijing, CHINA2 University of Botswana, Gaborone, BOTSWANA* Corresponding Author
European Journal of Interactive Multimedia and Education, 3(2), July 2022, e02215, https://doi.org/10.30935/ejimed/12519
Submitted: 12 June 2022, Published: 10 October 2022
OPEN ACCESS   921 Views   1069 Downloads
Download Full Text (PDF)

ABSTRACT

This study adopted two key variables of the technology acceptance model, thus perceived usefulness, self-efficacy, and (gratification) variable of uses and gratification theory to understand how the three variables predict students’ behavioral intentions towards the use of mobile learning devices (MLDs). The sample was drawn from 447 selected participants from four private universities in Gaborone, Botswana. The researcher analyzed the data and presented the findings by testing the suggested research model and the hypotheses through structural equation modeling. Regression analysis was carried out with SmartPLS to assess the path coefficient of the data collected for the model. The findings suggest that two of the key variables tested, thus self-efficacy and perceived usefulness of MLDs positively influenced students’ gratification and were statistically significant. However, two out of the three of the determinant variables of perceived usefulness (information seeking, and social connections) all had positive relations with students’ perceptions of gratification, and behavioral intentions towards MLDs. This study concludes that, information seeking, and social connections variables of the perceived usefulness, connote the positive relationships with students’ perceptions of gratification with MLDs. Furthermore, the findings suggest that students could improve behavioral intentions concerning the relevance of MLDs application in institutions of higher learning by applying varied MLDs at their disposal.
Keywords: smartphones, mobile learning, higher education, Botswana

CITATION (APA)

Ditlhokwa, G. (2022). Investigating Factors Influencing Students’ Behavioral Intentions Towards Mobile Learning Devices in Higher Educational Institutions. European Journal of Interactive Multimedia and Education, 3(2), e02215. https://doi.org/10.30935/ejimed/12519

REFERENCES

  1. Al-Hunaiyyan, A., T Bimba, A., Idris, N., & Al-Sharhan, S. (2017). A cognitive knowledge-based framework for social and metacognitive support in mobile learning. Interdisciplinary Journal of Information, Knowledge, and Management, 12, 075-098. https://doi.org/10.28945/3670
  2. Ally, M., & Prieto-Blázquez, J. (2014). Quin és el futur de l’aprenentatge mòbil en l’educació? [What is the future of mobile learning in education?] RUSC. Revista de Universidad y Sociedad Del Conocimiento [RUSC. Journal of University and Knowledge Society], 11(1), 142. https://doi.org/10.7238/rusc.v11i1.2033
  3. Al-Mashhadani, M. A., & Al-Rawe, M. F. (2018). The future role of mobile learning and smartphone applications in the Iraqi private universities. Smart Learning Environments, 5(1), 28. https://doi.org/10.1186/s40561-018-0077-7
  4. Alpert, F. (2016). Revitalizing the live lecture class with instructor-created videos. SAGE Open, 6(4), 215824401668068. https://doi.org/10.1177/2158244016680686
  5. Alzaza, N. S., Yaakub, A. R. (2011). Students’ awareness and requirements of mobile learning services in the higher education environment. American Journal of Economics and Business Administration, 3(1), 95-100. https://doi.org/10.3844/ajebasp.2011.95.100
  6. Baliyan, S. P., & Moorad, F. R. (2018). Teaching effectiveness in private higher education institutions in Botswana: Analysis of students’ perceptions. International Journal of Higher Education, 7(3), 143. https://doi.org/10.5430/ijhe.v7n3p143
  7. Barrett, N. E., Liu, G.-Z., & Wang, H.-C. (2021). Student perceptions of a mobile learning application for English oral presentations: The case of EOPA. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2021.1881975
  8. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
  9. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
  10. Blumler, J. G., & Katz, E. (Eds.). (1974). The uses of mass communications: Current perspectives on gratifications research. SAGE.
  11. Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Research, 17(3), 303-316. https://doi.org/10.1177/0049124189017003004
  12. Brame, C. J. (2015). Effective educational videos. Vanderbilt University. https://cft.vanderbilt.edu/guides-sub-pages/effective-educational-videos
  13. Brunner, M., & SÜβ, H.-M. (2005). Analyzing the reliability of multidimensional measures: An example from intelligence research. Educational and Psychological Measurement, 65(2), 227-240. https://doi.org/10.1177/0013164404268669
  14. Castro, J., Yamada, G., & Arias, O. (2016). Higher education decisions in Peru: On the role of financial constraints, skills, and family background. Higher Education, 72(4), 457-486. https://doi.org/10.1007/s10734-016-0040-x
  15. Chand, P., & Arora, J. (2008). Access to scholarly communication in higher education in India: Trends in usage statistics via INFLIBNET. The Program, 42(4), 382-390. https://doi.org/10.1108/00330330810912061
  16. Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the utaut model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
  17. Chen, B., Wang, Y., Xiao, L., Xu, C., Shen, Y., Qin, Q., Li, C., Chen, F., Leng, Y., Yang, T., & Sun, Z. (2021). Effects of mobile learning for nursing students in clinical education: A meta-analysis. Nurse Education Today, 97, 104706. https://doi.org/10.1016/j.nedt.2020.104706
  18. 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, 12(24), 10367. https://doi.org/10.3390/su122410367
  19. Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), 4111. https://doi.org/10.3390/app11094111
  20. Criollo-C, S., Lujan-Mora, S., & Jaramillo-Alcazar, A. (2018). Advantages and disadvantages of m-learning in current education. In Proceedings of the 2018 IEEE World Engineering Education Conference (pp. 1-6). https://doi.org/10.1109/EDUNINE.2018.8450979
  21. Davis, F. D. (1989). A technology acceptance model for empirically testing new end-user information systems: Theory and results [PhD thesis, Massachusetts Institute of Technology].
  22. Demuyakor, J. (2021). COVID-19 pandemic and higher education: Leveraging on digital technologies and mobile applications for online learning in Ghana. Shanlax International Journal of Education, 9(3), 26-38. https://doi.org/10.34293/education.v9i3.3904
  23. Dintoe, S. S. (2018). Educational technology adopters: A case study in the University of Botswana. International Journal of Education and Development using Information and Communication Technology, 14(1), 52-90.
  24. Distler, V., Lallemand, C., & Koenig, V. (2020). How acceptable is this? How user experience factors can broaden our understanding of the acceptance of privacy trade-offs. Computers in Human Behavior, 106, 106227. https://doi.org/10.1016/j.chb.2019.106227
  25. dos Santos, P. M., & Cirillo, M. Â. (2021). Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics-Simulation and Computation, 1-13. https://doi.org/10.1080/03610918.2021.1888122
  26. Edumadze, J., Ditlhokwa, G., & Demuyakor, J. (2022). Students’ acceptance and perceptions of perceived usefulness of mobile learning devices in higher educational institutions. Online Journal of Communication and Media Technologies, 12(2), e202209. https://doi.org/10.30935/ojcmt/11539
  27. Idrus, R. M., & Ismail, I. (2010). Role of institutions of higher learning towards a knowledge-based community utilizing mobile devices. Procedia-Social and Behavioral Sciences, 2(2), 2766-2770. https://doi.org/10.1016/j.sbspro.2010.03.412
  28. Kadimo, K., Kebaetse, M. B., Ketshogileng, D., Seru, L. E., Sebina, K. B., Kovarik, C., & Balotlegi, K. (2018). Bring-your-own-device in medical schools and healthcare facilities: A review of the literature. International Journal of Medical Informatics, 119, 94-102. https://doi.org/10.1016/j.ijmedinf.2018.09.013
  29. Kadimo, K., Mutshewa, A., & Kebaetse, M. B. (2022). Understanding the role of the bring-your-own-device policy in medical education and healthcare delivery at the University of Botswana’s Faculty of Medicine. Information and Learning Sciences, 123(3/4), 199-213. https://doi.org/10.1108/ILS-09-2021-0077
  30. Kukulska-Hulme, A. (2009). Will mobile learning change language learning? ReCALL, 21(2), 157-165. https://doi.org/10.1017/S0958344009000202
  31. Lai, Y., Saab, N., & Admiraal, W. (2022). University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction. Computers & Education, 179, 104413. https://doi.org/10.1016/j.compedu.2021.104413
  32. Lekopanye, C. M., & Mogwe, A. (2014). Information communication technology adoption in higher education sector of Botswana: A case of Botho University. International Journal of Advanced Computer Science and Applications, 5(8), 145-148. https://doi.org/10.14569/IJACSA.2014.050822
  33. Looi, C.-K., & Toh, Y. (2014). Orchestrating the flexible mobile learning classroom. In M. Ally, & A. Tsinakos (Eds.), Increasing access through mobile learning (pp. 161-174). Commonwealth of Learning and Athabasca University.
  34. Lucas, G., Cao, G., Waltemeyer, S., Mandernach, B. J., & Hammond, H. G. (2021). The value of instructor interactivity in the online classroom. https://doi.org/10.26077/62F4-21AA
  35. Marquina, P. F. (2018). Developing world-class students in Peru. GlobalFocus. https://www.globalfocusmagazine.com/developing-world-class-students-in-peru/
  36. Mehdipour, Y., & Zerehkafi, H. (2013). Mobile learning for education: Benefits and challenges. International Journal of Computational Engineering Research, 3(6), 93-101.
  37. Moakofhi, M., Leteane, O., Phiri, T., Pholele, T., & Sebalatlheng, P. (2017). Challenges of introducing e-learning at the Botswana University of Agriculture and Natural Resources: Lecturers’ perspective. International Journal of Education and Development using Information and Communication Technology, 13(2), 4-20.
  38. Mohammadi, M., Sarvestani, M. S., & Nouroozi, S. (2020). Mobile phone use in education and learning by faculty members of technical-engineering groups: Concurrent mixed methods design. Frontiers in Education, 5, 16. https://doi.org/10.3389/feduc.2020.00016
  39. Moremi, M. (2018). Factors shaping higher education in Botswana: A recipe for policy formulation and implementation? International Journal of Learning and Teaching, 64-69. https://doi.org/10.18178/ijlt.4.1.64-69
  40. Mutula, S. M. (2002). E‐learning initiative at the University of Botswana: Challenges and opportunities. Campus-Wide Information Systems, 19(3), 99-109. https://doi.org/10.1108/10650740210431916
  41. Ntereke, B. B., Conteh, B. G., Ramoroka, B. T., & Tlhobogang, K. B. (2021). Teaching and learning online during the pandemic: lecturers’ perceptions and experiences, MOSENODI: International Journal of the Educational Studies, 24(2), 22-47.
  42. Ntshwarang, P. N., Malinga, T., & Losike-Sedimo, N. (2021). eLearning tools at the University of Botswana: Relevance and use under COVID-19 crisis. Higher Education for the Future, 8(1), 142-154. https://doi.org/10.1177/2347631120986281
  43. O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, 160940691989922. https://doi.org/10.1177/1609406919899220
  44. Okoye, K., Rodriguez-Tort, J. A., Escamilla, J., & Hosseini, S. (2021). Technology-mediated teaching and learning process: A conceptual study of educators’ response amidst the COVID-19 pandemic. Education and Information Technologies, 26, 7225-7257. https://doi.org/10.1007/s10639-021-10527-x
  45. Ontiveros, M., & Pazos, J. R. C. (2013). Education and technology in Mexico and Latin America: Outlook and challenges. RUSC. Universities and Knowledge Society Journal, 10(2), 163-169 | 407-413. https://doi.org/10.7238/rusc.v10i2.1848
  46. Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 564294. https://doi.org/10.3389/fpsyg.2020.564294
  47. Peters, K. (2007). M-learning: Positioning educators for a mobile, connected future. The International Review of Research in Open and Distributed Learning, 8(2). https://doi.org/10.19173/irrodl.v8i2.350
  48. Ragusa, A. T., & Crampton, A. (2017). Online learning: Cheap degrees or educational pluralization?: Cheap degrees or educational pluralization? British Journal of Educational Technology, 48(6), 1208-1216. https://doi.org/10.1111/bjet.12489
  49. Rigdon, E. E. (1998). The equal correlation baseline model for comparative fit assessment in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 5(1), 63-77. https://doi.org/10.1080/10705519809540089
  50. Romero-Rodríguez, J.-M., Aznar-Díaz, I., Hinojo-Lucena, F.-J., & Cáceres-Reche, M.-P. (2020). Models of good teaching practices for mobile learning in higher education. Palgrave Communications, 6(1), 80. https://doi.org/10.1057/s41599-020-0468-6
  51. Rudhumbu, N., Dziva, D., & Plessis, E. (2021). Level of integration of mobile device technology in teaching and learning in universities in Botswana. International Journal of Education and Development using Information and Communication Technology, 17(1), 21-33.
  52. Rudhumbu, N., Dziva, D., & Plessis, E. (2021). Level of integration of mobile device technology in teaching and learning in universities in Botswana. International Journal of Education and Development using Information and Communication Technology, 17(1), 21-33.
  53. Shadfar, S., & Malekmohammadi, I. (2013). Application of structural equation modeling (SEM) in restructuring state intervention strategies toward paddy production development. International Journal of Academic Research in Business and Social Sciences, 3(12), 576-618. https://doi.org/10.6007/IJARBSS/v3-i12/472
  54. Sharma, S. K., Joshi, A., & Sharma, H. (2016). A multi-analytical approach to predict Facebook usage in higher education. Computers in Human Behavior, 55, 340-353. https://doi.org/10.1016/j.chb.2015.09.020
  55. Thomas, P. Y. (2010). Towards developing a web-based blended learning environment at the University of Botswana. http://hdl.handle.net/10500/4245
  56. Thorsen, S. V., & Bjorner, J. B. (2010). Reliability of the Copenhagen psychosocial questionnaire. Scandinavian Journal of Public Health, 38(3_suppl), 25-32. https://doi.org/10.1177/1403494809349859
  57. Wang, J., & Lei, L. (2021). Construction of college English mobile learning model based on distributed terminal. Scientific Programming, 2021, 1-7. https://doi.org/10.1155/2021/2220096
  58. Yu, Z., Yu, L., Xu, Q., Xu, W., & Wu, P. (2022). Effects of mobile learning technologies and social media tools on student engagement and learning outcomes of English learning. Technology, Pedagogy, and Education, 1-18. https://doi.org/10.1080/1475939X.2022.2045215