Mobile Technology Acceptance Scale for Learning Mathematics: Development, Validity, and Reliability Studies
DOI:
https://doi.org/10.19173/irrodl.v21i4.4834Keywords:
mobile technology, technology acceptance, learning mathmatics, UTAUT2Abstract
The purpose of this study is to develop a valid, reliable, and useful scale to measure high school students’ levels of acceptance of mobile technologies in learning mathematics based on the second version of the unified theory of acceptance and use of technology (UTAUT2) model. The study was designed based on a sequential exploratory mixed-method research design. To this end, both qualitative (interviews with students, review of literature, and expert panel evaluation) and quantitative procedures (Lawshe content validity technique, exploratory and confirmatory factor analysis, convergent validity, discriminant validity, nomological validity, criterion validity, internal consistency reliability, and temporal reliability) were used to develop and validate the Mobile Technology Acceptance Scale for Learning Mathematics (m-TASLM). As a result, a 5-point Likert scale with 36 items grouped under 8 factors was developed and confirmed. Both validity and reliability studies yielded favorable results.
References
Al-Hujran, O., Al-Lozi, E., & Al-Debei, M. M. (2014). “Get ready to mobile learning”: Examining factors affecting college students’ behavioral intentions to use m-learning in Saudi Arabia. Jordan Journal of Business Administration, 10(1), 1-18. doi: 10.12816/0026186
Al-Khateeb, M. A. (2018). The effect of teaching mathematical problems solving through using mobile learning on the seventh grade students’ ability to solve them in Jordan. International Journal of Interactive Mobile Technologies, 12(3), 178-191. doi: 10.3991/ijim.v12i3.8713
Attard, C., & Northcote, M. (2012). Mathematics on the move: Using mobile technologies to support student learning (Part 2). Australian Primary Mathematics Classroom, 17(1), 29-32. Retrieved from https://files.eric.ed.gov/fulltext/EJ978132.pdf
Awadhiya, A. K., & Miglani, A. (2016). Mobile learning: Challenges for teachers of Indian open universities. Journal of Learning for Development-JL4D, 3(2), 35-46. Retrieved from https://files.eric.ed.gov/fulltext/EJ1108182.pdf
Barati, M., & Zolhavarieh, S. (2012). Mobile learning and multi mobile service in higher education. International Journal of Information and Education Technology, 2(4), 297-299. doi: 10.7763/IJIET.2012.V2.135
Baya’a, N., & Daher, W. (2009, April). Students’ perceptions of mathematics learning using mobile phones. In Proceedings of the International Conference on Mobile and Computer Aided Learning (Vol. 4, pp. 1-9). Retrieved from https://staff-old.najah.edu/sites/default/files/Students%20Perceptions%20of%20Mathematics%20Learning%20Using%20Mobile%20Phones.pdf
Bharati, V. J., & Srikanth, R. (2018). Modified UTAUT2 model for m-learning among students in India. International Journal of Learning and Change, 10(1), 5-20. doi: 10.1504/IJLC.2018.089532
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: The Guilford Press.
Cayton-Hodges, G. A., Feng, G., & Pan, X. (2015). Tablet-based math assessment: What can we learn from math apps? Journal of Educational Technology and Society, 18(2), 3–20. Retrieved from https://drive.google.com/file/d/1E27925VbJPhsMtGNKd4SAyx_BQhxZloU/view
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 and Education, 59(3), 1054-1064. doi: 10.1016/j.compedu.2012.04.015
Choon-Keong, T., Ing, N. S. & Kean-Wah, L. (2013). Readiness for mobile learning at a public university in East Malaysia. In M. A. Embi & N. M. Nordin (Eds.), Mobile Learning: Malaysian Initiatives & Research Findings (pp. 27-38). Bangi: Centre for Academic Advancement.
Chung, C. J., Hwang, G. J., & Lai, C. L. (2019). A review of experimental mobile learning research in 2010–2016 based on the activity theory framework. Computers and Education, 129, 1-13. doi: 10.1016/j.compedu.2018.10.010
Creswell, J. W., & Plano Clark, V. L. (2011) Designing and conducting mixed methods research (2nd ed.). Thousand Oaks, CA: Sage Publications.
Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK-12 education: A systematic review. Computers and Education, 110, 51-63. doi: 10.1016/j.compedu.2017.03.013
Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers and Education, 123, 53-64. doi: https://doi.org/10.1016/j.compedu.2018.04.007
Fabian, K., Topping, K. J., and Barron, I. G. (2016). Mobile technology and mathematics: Effects on students’ attitudes, engagement, and achievement. Journal of Computers in Education, 3(1), 77-104. doi: 10.1007/s40692-015-0048-8
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, ıntention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.
Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones, and social media. The Internet and Higher Education, 19, 18-26. doi: 10.1016/j.iheduc.2013.06.002
Gökdaş, İ., Torun, F., & Bağrıaçık, A. (2014). Teacher candidates’ mobile phones educational use situations and opinions on mobile learning. Adnan Menderes University Journal of Educational Sciences, 5(2), 43-61.
Güngören, Ö. C., Bektaş, M., Öztürk, E., & Horzum, M. B. (2014). Acceptance of TPC scale -validity and reliability study. Education and Science, 39(176), 69-79. Retrieved from http://egitimvebilim.ted.org.tr/index.php/EB/article/view/3497/846
Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis (7th ed.). London: Pearson New International Edition.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. doi: 10.1080/10705519909540118
Hung, C. M., Huang, I., & Hwang, G. J. (2014). Effects of digital game-based learning on students’ self-efficacy, motivation, anxiety, and achievements in learning mathematics. Journal of Computers in Education, 1(2-3), 151-166. doi: 10.1007/s40692-014-0008-8
Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers and Education, 56(4), 1023-1031. doi: 10.1016/j.compedu.2010.12.002
Hwang, G. J., & Wu, P. H. (2014). Applications, impacts and trends of mobile technology-enhanced learning: A review of 2008–2012 publications in selected SSCI journals. International Journal of Mobile Learning and Organisation, 8(2), 83-95. doi: 10.1504/IJMLO.2014.062346
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24(2), 1793-1817. doi: 10.1007/s10639-018-09858-z
Kyriakides, A. O., Meletiou-Mavrotheris, M., & Prodromou, T. (2016). Mobile technologies in the service of students’ learning of mathematics: The example of game application ALEX in the context of a primary school in Cyprus. Mathematics Education Research Journal, 28(1), 53-78. doi: 10.1007/s13394-015-0163-x
Lai, C. L., & Hwang, G. J. (2014). Effects of mobile learning time on students’ conception of collaboration, communication, complex problem-solving, meta-cognitive awareness and creativity. International Journal of Mobile Learning and Organisation, 8(3-4), 276-291. doi: 10.1504/IJMLO.2014.067029
Larkin, K., & Calder, N. (2016). Mathematics education and mobile technologies. Mathematics Education Research Journal, 28(1), 1-7. doi: 10.1007/s13394-015-0167-6
Larkin, K., & Calder, N. (2016). Mathematics education and mobile technologies. Mathematics Education Research Journal, 28(1), 1-7. doi: 10.1007/s13394-015-0167-6
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. doi: 10.1111/j.1744-6570.1975.tb01393.x
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737. doi: 10.2307/25148817
McMullen, J., Hannula‐Sormunen, M. M., Kainulainen, M., Kiili, K., & Lehtinen, E. (2017). Moving mathematics out of the classroom: Using mobile technology to enhance spontaneous focusing on quantitative relations. British Journal of Educational Technology, 50(2), 562-573. doi: 10.1111/bjet.12601
Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology, 35(4), 174-191. doi: 10.14742/ajet.4432
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(4), 592-605. doi: 10.1111/j.1467-8535.2011.01229.x
Pea, R., & Maldonado, H. (2006). WILD for learning: Interacting through new computing devices, anytime, anywhere. In K. Sawyer (Ed.), Cambridge Handbook of Learning Sciences (pp. 427-442). Cambridge: Cambridge University Press.
Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93. doi: 10.1016/j.jretconser.2019.04.018
Reychav, I., Dunaway, M., & Kobayashi, M. (2015). Understanding mobile technology-fit behaviors outside the classroom. Computers and Education, 87, 142-150. doi: 10.1016/j.compedu.2015.04.005
Riconscente, M. M. (2013). Results from a controlled study of the iPad fractions game Motion Math. Games and Culture, 8(4), 186-214. doi: 10.1177/1555412013496894
Subramanya, S., & Farahani, A. (2012). Point-of-view article on: Design of a smartphone app for learning concepts in mathematics and engineering. International Journal of Innovation Science, 4(3), 173-184. Retrieved from https://www.emerald.com/insight/content/doi/10.1260/1757-2223.4.3.173/full/html
Şad, S.N, Özer, N., Yakar, Ü., & Öztürk, F. (2020) Mobile or hostile? Using smartphones in learning English as a foreign language, Computer Assisted Language Learning. Advance online publication. doi: 10.1080/09588221.2020.1770292
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.
Tangney, B., & Bray, A. (2013, October). Mobile technology, maths education and 21C learning. In 12th World Conference on Mobile and Contextual Learning (mLearn 2013) (Vol. 2013, No. 3, p. 7). Hamad bin Khalifa University Press (HBKU Press).
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
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi: 10.2307/41410412
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. doi: 10.1111/j.1467-8535.2007.00809.x
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 and Education, 59(2), 817-827. doi: 10.1016/j.compedu.2012.03.016
Yang, S. (2013). Understanding undergraduate students’ adoption of mobile learning model: A perspective of the extended UTAUT2. Journal of Convergence Information Technology, 8(10), 969-979. Retrieved from https://pdfs.semanticscholar.org/95ff/fb4b6f2a59b13db3ec755b0ab4cd1911008a.pdf
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