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Journal of Computing in Higher Education

, Volume 30, Issue 3, pp 426–451 | Cite as

Acceptance and usage of mobile assisted language learning by higher education students

  • Gustavo García Botero
  • Frederik Questier
  • Sebastiano Cincinnato
  • Tao He
  • Chang Zhu
Article

Abstract

Research on mobile learning indicates that students perceive mobile devices mainly as communication and entertainment tools. Therefore, a key factor in successful mobile learning implementation is the initial measurement of students’ acceptance of those devices into their learning. Countless language applications available suggest that mobile devices can be ideal tools for language learning. Surprisingly, there are few studies reporting students’ acceptance of mobile assisted language learning (MALL), let alone MALL acceptance in developing countries. By adapting and extending the unified theory of acceptance and use of technology model, the study assesses the dimensions affecting behavioral intentions and actual use of MALL. Data were collected and analyzed using structural equation modeling. Results show that performance expectancy, social influence, and facilitating conditions influence students’ attitudes towards using MALL. Accordingly, attitude is the factor that affects behavioral intention the most. The model also shows that behavioral intention has an effect on MALL use. The study concludes that students enrolled in higher education in developing countries such as Colombia have a positive attitude towards MALL. However, an improvement of facilitating conditions, along with a more influential role of the educational community is needed for a successful MALL integration in education.

Keywords

Acceptance Higher education Mobile assisted language learning UTAUT 

Notes

Acknowledgements

This study is funded by the European Commission-Erasmus Mundus Action 2-Eureka SD Project under the Grant Number 2013-2591/001-001. The data of this study can be accessed upon request. The participants of this study were told that their participation was voluntary and their personal data were protected by using a code to replace their personal information.

Compliance with ethical standards

Conflict of interest

The authors would like to state that there is no potential conflict of interest in this study and that the paper is not under consideration for publication elsewhere.

References

  1. Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82–107.CrossRefGoogle Scholar
  2. Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314.  https://doi.org/10.1016/j.chb.2013.10.035.CrossRefGoogle Scholar
  3. Arning, K., & Ziefle, M. (2007). Understanding age differences in PDA acceptance and performance. Computers in Human Behavior, 23(6), 2904–2927.  https://doi.org/10.1016/j.chb.2006.06.005.CrossRefGoogle Scholar
  4. Avci, U., & Askar, P. (2012). The comparison of the opinions of the university students on the usage of blog and wiki for their courses. Educational Technology and Society, 15(2), 194–205.Google Scholar
  5. Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 7.CrossRefGoogle Scholar
  6. Böhm, S., & Constantine, G. P. (2016). Impact of contextuality on mobile learning acceptance: An empirical study based on a language learning app. Interactive Technology and Smart Education, 13(2), 107–122.CrossRefGoogle Scholar
  7. Burston, J. (2015). Twenty years of MALL project implementation: A meta-analysis of learning outcomes. ReCALL, 27(01), 4–20.CrossRefGoogle Scholar
  8. Cavus, N., & Ibrahim, D. (2009). M-learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology, 40(1), 78–91.CrossRefGoogle Scholar
  9. Chan, W., Chi, S., & Lin, C. (2011). Students’ perceptions of and attitudes towards podcast-based learning: A comparison of two language podcast projects. Electronic Journal of Foreign Language Teaching, 8, 312–335.Google Scholar
  10. Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826.CrossRefGoogle Scholar
  11. Chen, X.-B. (2013). Tablets for informal language learning: student usage and attitudes. Language Learning & Technology, 17(1), 20–36.Google Scholar
  12. Chen, N. S., Hsieh, S. W., & Kinshuk, (2008). Effects of short-term memory and content representation type on mobile language learning. Language Learning & Technology, 12(3), 93–113.  https://doi.org/10.1016/S0006-291X(05)80942-8.CrossRefGoogle Scholar
  13. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.  https://doi.org/10.1016/j.compedu.2012.04.015.CrossRefGoogle Scholar
  14. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRefGoogle Scholar
  15. Demouy, V., & Kukulska-Hulme, A. (2010). On the spot: Using mobile devices for listening and speaking practice on a French language programme. Open Learning, 25(3), 217–232.  https://doi.org/10.1080/02680513.2010.511955.CrossRefGoogle Scholar
  16. Deng, H. (2011). Self-directed English vocabulary learning with a mobile application in everyday context. In 10th world conference on mobile and contextual learning: mLearn 2011 conference proceedings (pp. 24–31).Google Scholar
  17. Duman, G., Orhon, G., & Gedik, N. (2015). Research trends in mobile assisted language learning from 2000 to 2012. ReCALL, 27(02), 197–216.  https://doi.org/10.1017/S0958344014000287.CrossRefGoogle Scholar
  18. Escobar-Rodríguez, T., Carvajal-Trujillo, E., & Monge-Lozano, P. (2014). Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australasian Journal of Educational Technology, 30(2), 136–151.CrossRefGoogle Scholar
  19. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). London: SAGE.Google Scholar
  20. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.Google Scholar
  21. Gefen, D., Straub, D. W., & Rigdon, E. E. (2011). An update and extension to SEM guidelines for administrative and social science research. Management Information Systems Quarterly, 35(2), 3–14.CrossRefGoogle Scholar
  22. Godwin-Jones, R. (2011). Emerging technologies mobile apps for language learning. Language Learning & Technology, 15(2), 2–11.Google Scholar
  23. Gordon, M. (2013). India, China and the map to two billion connected devices. Retrieved from http://flurrymobile.tumblr.com/page/8. Accessed 25 May 2017.
  24. Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners’ intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46, 381–390.  https://doi.org/10.1111/bjet.12148F.CrossRefGoogle Scholar
  25. Hsu, H. H. (2013). The acceptance of Moodle: An empirical study based on UTAUT. Creative Education, 3(08), 44.CrossRefGoogle Scholar
  26. Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. International Review of Research in Open and Distance Learning, 13(3), 147–164.CrossRefGoogle Scholar
  27. Jairak, K., Praneetpolgrang, P., & Mekhabunchakij, K. (2009). An acceptance of mobile learning for higher education students in Thailand. In Paper presented at the sixth international conference on eLearning for knowledge-based society. Bangkok.Google Scholar
  28. Kim, G. M., & Lee, S. J. (2016). Korean Students’ Intentions to use mobile-assisted language learning: Applying the technology acceptance model. International Journal of Contents.  https://doi.org/10.5392/IJoC.2016.12.3.047.CrossRefGoogle Scholar
  29. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press.Google Scholar
  30. Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110.  https://doi.org/10.1016/j.chb.2008.07.007.CrossRefGoogle Scholar
  31. Lan, Y. J., Sung, Y. T., & Chang, K. E. (2007). A mobile-device-supported peer-assisted learning system for collaborative early EFL reading. Language Learning & Technology, 11(3), 130–151.Google Scholar
  32. Li, Z., & Hegelheimer, V. (2013). Mobile-assisted grammar exercises: Effects on self-editing in L2 writing. Language Learning & Technology, 17(3), 135–156.Google Scholar
  33. Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: The differing perspectives of teachers and students. Computers & Education, 68, 416–428.  https://doi.org/10.1016/j.compedu.2013.06.003.CrossRefGoogle Scholar
  34. Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219.  https://doi.org/10.1016/j.compedu.2010.05.018.CrossRefGoogle Scholar
  35. López-Bonilla, L. M., & López-Bonilla, J. M. (2017). Explaining the discrepancy in the mediating role of attitude in the TAM. British Journal of Educational Technology, 48, 940–949.CrossRefGoogle Scholar
  36. Marchewka, J. T., Liu, C., & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93.Google Scholar
  37. Moran, M., Hawkes, M., & El Gayar, O. (2010). Tablet personal computer integration in higher education: Applying the unified theory of acceptance and use technology model to understand supporting factors. Journal of Educational Computing Research, 42(1), 79–101.  https://doi.org/10.2190/EC.42.1.d.CrossRefGoogle Scholar
  38. Nassuora, A. (2013). Students acceptance of mobile learning for higher education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1–9.  https://doi.org/10.12785/ijlms/010101.CrossRefGoogle Scholar
  39. Nistor, N., & Heymann, J. O. (2010). Reconsidering the role of attitude in the TAM: An answer to Teo (2009a). British Journal of Educational Technology, 41, E142–E145.  https://doi.org/10.1111/j.1467-8535.2010.01109.x.CrossRefGoogle Scholar
  40. Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43, 592–605.  https://doi.org/10.1111/j.1467-8535.2011.01229.x.CrossRefGoogle Scholar
  41. Pindeh, N., Suki, N. M., & Suki, N. M. (2016). User acceptance on mobile apps as an effective medium to learn Kadazandusun language. Procedia Economics and Finance, 37, 372–378.CrossRefGoogle Scholar
  42. Rosseel, Y. (2012). lavaan : An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.  https://doi.org/10.18637/jss.v048.i02.CrossRefGoogle Scholar
  43. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103.  https://doi.org/10.1016/j.im.2006.10.007.CrossRefGoogle Scholar
  44. Segaran, K., Ali, A. Z. M., & Hoe, T. W. (2014). Usability and user satisfaction of 3D talking-head mobile assisted language learning (MALL) app for non-native speakers. Procedia-Social and Behavioral Sciences, 131, 4–10.  https://doi.org/10.1016/j.sbspro.2014.04.069.CrossRefGoogle Scholar
  45. Seo, W., & Choi, I. (2014). The effect of using a smartphone application on middle school students’ English expression learning and satisfaction. Multimedia-Assisted Language Learning, 17, 34–57.Google Scholar
  46. Smarkola, C. (2011). A mixed-methodological technology adoption study. In Technology acceptance in education (pp. 9–41). Sense Publishers.Google Scholar
  47. Soleimani, E., Ismail, K., & Mustaffa, R. (2014). The acceptance of mobile assisted language learning (MALL) among post graduate ESL students in UKM. Procedia-Social and Behavioral Sciences, 118, 457–462.  https://doi.org/10.1016/j.sbspro.2014.02.062.CrossRefGoogle Scholar
  48. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1–11.  https://doi.org/10.1016/S0378-7206(97)00026-8.CrossRefGoogle Scholar
  49. Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602–620.CrossRefGoogle Scholar
  50. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English E-learning websites in Taiwan. SAGE Open.  https://doi.org/10.1177/2158244013503837.CrossRefGoogle Scholar
  51. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312.  https://doi.org/10.1016/j.compedu.2008.08.006.CrossRefGoogle Scholar
  52. Teo, T. (Ed.). (2011). Technology acceptance in education: Research and issues. Rotterdam: Sense Publishers.  https://doi.org/10.1007/978-94-6091-487-4.Google Scholar
  53. The E-Learning guild: 61 tips on m-learning. (2012). Retrieved from http://www-rohan.sdsu.edu/~ldt/edweb_web/Courses/EDTEC596/ML/ebook_mlearningtips2012.pdf. Accessed 10 June 2017.
  54. Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology, 9(3), 71–85.Google Scholar
  55. Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the unified theory of acceptance and use of technology. Computers & Education, 81, 169–178.CrossRefGoogle Scholar
  56. Van Schaik, P. (2009). Unified theory of acceptance and use for websites used by students in higher education. Journal of Educational Computing Research, 40(2), 229–257.  https://doi.org/10.2190/EC.40.2.e.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. Viberg, O., & Grönlund, Å. (2012, October). Mobile assisted language learning: A literature review. In Paper presented at the 11th international conference on mobile and contextual learning. Helsinki.Google Scholar
  59. Viberg, O., & Grönlund, Å. (2013). Cross-cultural analysis of users’ attitudes toward the use of mobile devices in second and foreign language learning in higher education: A case from Sweden and China. Computers & Education, 69, 169–180.  https://doi.org/10.1016/j.compedu.2013.07.014.CrossRefGoogle Scholar
  60. 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.CrossRefGoogle Scholar
  61. Wong, L.-H., Chai, C. S., Zhang, X., & King, R. B. (2015). Employing the TPACK framework for researcher-teacher co-design of a mobile-assisted seamless language learning environment. IEEE Transactions on Learning Technologies, 8(1), 31–42.  https://doi.org/10.1109/TLT.2014.2354038.CrossRefGoogle Scholar
  62. Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21, 680–698.CrossRefGoogle Scholar
  63. Wu, C. C., Li, C. C., & Tsai, C. F. (2013). Factors determining of effects of teachers’ web-based teaching platform usage-using UTAUT to explore. Journal of Internet Technology, 14(6), 919–928.Google Scholar
  64. Wu, W.-H., Wu, Y.-C. J., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817–827.  https://doi.org/10.1016/j.compedu.2012.03.016.CrossRefGoogle Scholar
  65. Yueh, H., Huang, J., & Chang, C. (2015). Exploring factors affecting students’ continued Wiki use for individual and collaborative learning : An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1), 16–31.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gustavo García Botero
    • 1
  • Frederik Questier
    • 1
    • 2
  • Sebastiano Cincinnato
    • 2
  • Tao He
    • 1
  • Chang Zhu
    • 1
  1. 1.Department of Educational SciencesVrije Universiteit Brussel (VUB)BrusselsBelgium
  2. 2.Teacher Education Departement (IDLO)Vrije Universiteit Brussel (VUB)BrusselsBelgium

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