DOI QR코드

DOI QR Code

Validating a Structural Model of Using Mobile Technology for Learning among High School Students

  • Received : 2020.02.06
  • Accepted : 2020.04.01
  • Published : 2020.04.30

Abstract

Despite the existing body of literature focusing on the effects of one-to-one mobile technology integration in teaching and learning, research discussed that the determinants of mobile technology acceptance and use in secondary school settings are still unclear. Hence, this study examined the extent to which determinants influence high school students' behavioral intention to use one-to-one mobile technology for learning. The newly proposed model incorporated three additional constructs beyond those in the unified theory of acceptance and use of technology (UTAUT) model, including computer self-efficacy, attitude toward using technology and computer anxiety, as suggested by recent literature. Data were collected from 247 U.S. Midwestern high school students who participated in an online survey. Using a structural equation modeling approach, this study established construct validity for the nine-construct extended UTAUT model to assess high school students' intention to use mobile technology. The results of structural relations in the proposed model showed that their behavioral intention to use mobile technology was significantly predicted by social influence and attitude toward using technology. Also, their strong behavioral intention and facilitating conditions were associated with frequent use of mobile technology in learning. Discussion, implications, and conclusion were addressed in this study.

Keywords

Acknowledgement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

  1. Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389-412. https://doi.org/10.1016/j.compedu.2018.06.008
  2. Allison, P. D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112(4), 545-557. https://doi.org/10.1037/0021-843X.112.4.545
  3. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall.
  4. Bervell, B., & Umar, I. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of Exogenous variables in higher education technology acceptance research. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6471-6490.
  5. Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. EURASIA Journal of Mathematics, Science and Technology Education, 5(3), 235-245. https://doi.org/10.12973/ejmste/75275
  6. Birch, A., & Irvine, V. (2009). Preservice teachers' acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295-315. https://doi.org/10.1080/09523980903387506
  7. Brown, T. (2015). Confirmatory factor analysis for applied research. New York, NY: Guilford Publications Inc.
  8. Caudill, J. G. (2007). The growth of m-learning and the growth of mobile computing: Parallel developments. International Review of Research in Open and Distance Learning, 8(2), 1-13. https://doi.org/10.19173/irrodl.v8i2.348
  9. Cazan, A. M., Cocorada, E., & Maican, C. I. (2016). Computer anxiety and attitudes towards the computer and the internet with Romanian high-school and university students. Computers in Human Behavior, 55, 258-267. https://doi.org/10.1016/j.chb.2015.09.001
  10. Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148-158. https://doi.org/10.1016/j.compedu.2012.06.008
  11. Chou, C. C., Block, L., & Jesness, R. (2012). A case study of mobile learning pilot project in K-12 schools. Journal of Educational Technology Development and Exchange, 5(2), 11-26.
  12. Chua, S. L., Chen, D., & Wong, A. F. L. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15(5), 609-623. https://doi.org/10.1016/S0747-5632(99)00039-4
  13. Cilliers, L. (2017). Wiki acceptance by university students to improve collaboration in higher education. Innovations in Education and Teaching International, 54(5), 485-493. https://doi.org/10.1080/14703297.2016.1180255
  14. Compeau, D. R., & Higgins, C. A. (1995a). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143. https://doi.org/10.1287/isre.6.2.118
  15. Compeau, D. R., & Higgins, C. A. (1995b). Computer self-efficacy: Development of a measure and initial test. Management Information Systems Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
  16. Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. Management Information Systems Quarterly, 23(2), 145-158. https://doi.org/10.2307/249749
  17. Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK-12 education: A systematic review. Computers & Education, 110, 51-63. https://doi.org/10.1016/j.compedu.2017.03.013
  18. Crompton, H., & Keane, J. (2012). Implementation of a one-to-one iPod Touch program in a middle school. Journal of Interactive Online Learning, 11(1), 1-18.
  19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-339. https://doi.org/10.2307/249008
  20. Decman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272-281. https://doi.org/10.1016/j.chb.2015.03.022
  21. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.2307/3150980
  22. Groves, M. M., & Zemel, P. C. (2000). Instructional technology adoption in higher education: An action research case study. International Journal of Instructional Media, 27(1), 57-65.
  23. Gu, X., Zhu, Y., & Guo, X. (2013). Meeting the "Digital Natives": Understanding the acceptance of technology in classrooms. Journal of Educational Technology & Society, 16(1), 392-402.
  24. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis with readings (7th ed.). Upper Saddle River, NJ: Pearson.
  25. Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252. https://doi.org/10.1007/s11423-006-9022-5
  26. Howard, G. S., & Smith, R. D. (1986). Computer anxiety in management: Myth or reality? Communications of the Association for Computing Machinery, 29(7), 611-615. https://doi.org/10.1145/6138.6143
  27. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  28. Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology & Society, 16(2), 188-197.
  29. King, L. J., Gardner-McCune, C., Vargas, P., & Jimenez, Y. (2014). Re-discovering and re-creating African American historical accounts through mobile apps: The role of mobile technology in history education. The Journal of Social Studies Research, 38(3), 173-188. https://doi.org/10.1016/j.jssr.2013.12.005
  30. Kline, P. (1999). The handbook of psychological testing (2nd ed.). London, UK: Routledge.
  31. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: The Guilford Press.
  32. Lin, P.-C., Lu, H.-K., & Liu, S.-C. (2013). Towards an education behavioral intention model for e-learning systems: An extension of UTAUT. Journal of Theoretical and Applied information Technology, 47(3), 1120-1127.
  33. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130
  34. McDonald, R. P. (1978). Generalizability in factorable domains: "Domain validity and generalizability" 1. Educational and Psychological Measurement, 38(1), 75-79. https://doi.org/10.1177/001316447803800111
  35. Muthen, L. K., & Muthen, B. O. (2015). Mplus user's guide (7th ed.). Los Angeles, CA: Muthen & Muthen.
  36. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. https://doi.org/10.1016/j.compedu.2017.02.005
  37. Pearson Education. (2015). Student mobile device survey 2015, national report: Students in grades 4-12. Retrieved from https://www.pearsoned.com/wp-content/uploads/2015-Pearson-Student-Mobile-Device-Survey-Grades-4-12.pdf
  38. Rahman, A., Jamaludin, A., & Mahmud, Z. (2011). Intention to use digital library based on modified UTAUT model: Perspectives of Malaysian postgraduate students. International Journal of Social, Management, Economics and Business Engineering, 5(3), 51-57.
  39. Russell, M., Bebell, D., & Higgins, J. (2004). Laptop learning: A comparison of teaching and learning in upper elementary classrooms equipped with shared carts of laptops and permanent 1:1 laptops. Journal of Educational Computing Research, 30(4), 313-330. https://doi.org/10.2190/6E7K-F57M-6UY6-QAJJ
  40. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
  41. Sharples, M., Taylor, J., & Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews & C. Haythornthwaite (Eds.), The Sage handbook of e-learning research (pp. 221-247). London, UK: Sage.
  42. Shin, W. S., & Kang, M. (2015). The use of a mobile learning management system at an online university and its effect on learning satisfaction and achievement. The International Review of Research in Open and Distributed Learning, 16(3), 110-130.
  43. Song, D., & Kim, P. (2015). Inquiry-based mobilized math classroom with stanford mobile inquiry-based learning environment (SMILE). In H. Crompton, & J. Traxler (Eds.), Mobile learning and STEM: Case studies in practice (pp. 150-161). New York: Routledge.
  44. Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649. https://doi.org/10.3102/0034654308325896
  45. Sung, Y. T., Chang, K. E., & Liu, T. C. (2016). The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252-275. https://doi.org/10.1016/j.compedu.2015.11.008
  46. Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51-66. https://doi.org/10.1080/10494820.2011.641674
  47. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.
  48. Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. Management Information Systems Quarterly, 32(3), 483-502. https://doi.org/10.2307/25148853
  49. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 45(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  50. Venkatesh, V., Morris, M. G., Davis, G. B, & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  51. Wang, D., Xu, L., & Chan, H. C. (2015). Understanding the continuance use of social network sites: a computer self-efficacy perspective. Behaviour & Information Technology, 34(2), 204-216. https://doi.org/10.1080/0144929X.2014.952778
  52. Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. West Sussex, UK: John Wiley & Sons.
  53. Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
  54. Wrycza, S., Marcinkowski, B., & Gajda, D. (2017). The enriched UTAUT model for the acceptance of software engineering tools in academic education. Information Systems Management, 34(1), 38-49. https://doi.org/10.1080/10580530.2017.1254446
  55. Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers' self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789-801. https://doi.org/10.1111/bjet.12453
  56. Zainab, B., Awais Bhatti, M., & Alshagawi, M. (2017). Factors affecting e-training adoption: an examination of perceived cost, computer self-efficacy and the technology acceptance model. Behaviour & Information Technology, 36(12), 1261-1273. https://doi.org/10.1080/0144929X.2017.1380703