The development and validation of a scale measuring mobile phone use in an academic environment

Yıl 2022, Cilt: 9 Sayı: 4, 931 - 948, 22.12.2022

Nehir Yasan Ak Soner Yıldırım

https://doi.org/10.21449/ijate.946609

Cited By: 2

Öz

The purpose of this study was to fill the gap in current research on educational mobile phone use within the framework for the rational analysis of the mobile education (FRAME) model. The paper developed and validated the Mobile Phone Use in Academic Environment Scale (MPUAES) to measure both positive and negative aspects of educational use of mobile phones. The data was collected from all faculties and all grade levels of Middle East Technical University in Ankara. The inclusion criterion for the participation in the study was owning a smartphone. The exploratory and confirmatory factor analyses were run with two different groups in a total of 1887 undergraduate students. Three factors structure with 18 items were obtained, which were labeled as facilitator, distractor, and connectedness. These three factors explained 63.42% of the total variance. For confirmation of the factor structure, confirmatory factor analysis was performed with the second sample. The Cronbach alpha coefficient of each factor ranged between .90 and .74. To conclude, the findings of the study proposed that the scores obtained from the developed scale were valid and reliable in measuring undergraduate students’ mobile phone use in an academic environment.

Anahtar Kelimeler

Mobile phone affinity, Scale development, Educational mobile phone use, Smartphone, Educational technology

Destekleyen Kurum

Middle East Technical University

Proje Numarası

Scientific Research Project Coordinator (Project No:1416).

Teşekkür

Dr. Sacip Toker and Dr. Halil Yurdugül

Kaynakça

• Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: an investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5). https://doi.org/10.19173/irrodl.v14i5.1631
• Bernacki, M.L., Greene, J.A., & Crompton, H. (2020). Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology, 60, 101827. https://doi.org/10.1016/j.cedpsych.2019.101827
• Bock, B.C., Lantini, R., Thind, H., Walaska, K., Rosen, R.K., Fava, J.L., ... & Scot Sheldon, L.A. (2016). The Mobile Phone Affinity Scale: Enhancement and Refinement. JMIR mHealth and uHealth, 4(4). https://doi.org/10.2196/mhealth.6705
• Bryant, E.C., (2016). Graduate student perceptions of multi-modal tablet use in academic environments [Doctoral dissertation, University of South Florida]. The USF Libraries. https://core.ac.uk/download/pdf/154477153.pdf
• Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J.S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage. https://psycnet.apa.org/record/1993-97481-000
• Büyüköztürk, Ş. (2007). Sosyal bilimler için veri analizi el kitabı (7. Baskı). [Data analysis handbook for social sciences]. Pegem Akademi Yayınları. https://www.pegem.net/kitabevi/109-Sosyal-Bilimler-icin-Veri-Analizi-El-Kitabi-Istatistik-Arastirma-Deseni-SPSS-Uygulamalari-ve-Yorum-kitabi.aspx
• Byrne, B.M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge. https://doi.org/10.4324/9780203805534
• 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. http://doi.org/10.1016/j.compedu.2012.04.015
• Costello, A.B., & Osborne, J.W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment Research and Evaluation, 10(7). http://pareonline.net/pdf/v10n7.pdf
• Crompton, H. (2017), Moving toward a mobile learning landscape: presenting a mlearning integration framework. Interactive Technology and Smart Education, 14(2), 97-109. https://doi.org/10.1108/ITSE-02-2017-0018
• Cudeck, R., & Browne, M.W. (1983). Cross-validation of covariance structures. Multivariate Behavioral Research, 18(2), 147-167. https://doi.org/10.1207/s15327906mbr1802_2
• Fabrigar, L.R., Wegener, D.T., MacCallum, R.C., & Strahan, E.J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological methods, 4(3), 272-299. https://doi.apa.org/doi/10.1037/1082-989X.4.3.272
• Field. A. (2009). Discovering statistics using by SPSS (3rd ed.). London: Sage Publication. https://uk.sagepub.com/en-gb/eur/discovering-statistics-using-sas/book234095
• Ford, J.R. (2016). Learners’ perspectives on how mobile computing devices usage interacts with their learning [Doctoral dissertation, Northcentral University]. ProQuest Library. https://search.proquest.com/dissertations-theses/learners-perspectives-on-how-mobile-computing/docview/1846531179/se-2?accountid=13014
• Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Gikas J., & Grant. M.M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education. 19. 18-26. https://doi.org/10.1016/j.iheduc.2013.06.002
• Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall, Inc. https://doi.org/10.1016/j.iheduc.2013.06.002
• Han, S., & Yi, Y.J. (2019). How does the smartphone usage of college students affect academic performance? Journal of Computer Assisted Learning, 35(1), 13 22. https://doi.org/10.1111/jcal.12306
• Hatcher, L. (1994). A step-by-step approach to using the SAS® system for factor analysis and structural equation modeling. Cary, NC, USA: SAS Institute, Inc. https://www.sas.com/storefront/aux/en/spsxsfactor/61314_excerpt.pdf
• Herrington, A., & Herrington, J. (2007, November). Authentic mobile learning in higher education [Paper presentation]. Australian Association for Research in Education Conference, Fremantle, Western Australia. https://www.aare.edu.au/07pap/her07131.pdf
• Hu, L.H. & 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), 1-15. https://doi.org/10.1080/10705519909540118
• Huang, G. (2016) Using mobile phones for teaching and learning in Chinese traditional undergraduate education [Doctoral dissertation, Nova Southeastern University]. ProQuest Library. https://search.proquest.com/dissertations-theses/using-mobile-phones-teaching-learning-chinese/docview/1844448002/se-2?accountid=13014
• Humphreys, L.G. & Montanelli, R.G. (1975). An investigation of the parallel analysis criterion for determining the number of common factors. Multivariate Behavioral Research, 10, 193-206. https://doi.org/10.1207/s15327906mbr1002_5
• Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. Sage. https://uk.sagepub.com/en-gb/eur/the-multivariate-social-scientist/book205684
• Iqbal, S., & Qureshi, I.A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147-164. https://doi.org/10.19173/irrodl.v13i3.1152
• Jöreskog, K.G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago: Scientific Software International. https://psycnet.apa.org/record/1993-97878-000
• Keegan, D. (2005, October). The incorporation of mobile learning into mainstream education and training [Paper presentation]. 4th World Conference on mLearning, Cape Town, South Africa. https://www.cin.ufpe.br/~mlearning/intranet/mlearning/mlearn2005/Mainstream%20Education%20and%20Training.pdf
• Kite, M.E., & Whitley, B.E. (2018). Principles of Research in Behavioral Science (4th ed.). Routledge. https://doi.org/10.4324/9781315450087
• Kline, R.B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press. ftp://158.208.129.61/suzuki/PP_SEM_3e.pdf
• Koole, M.L. (2006). The framework for the rational analysis of mobile education (FRAME) model: An evaluation of mobile devices for distance education [Master’s thesis, Athabasca University]. Athabasca University Library. http://hdl.handle.net/2149/543
• Koole, M., & Ally, M. (2006, April). Framework for the rational analysis of mobile education (FRAME) model: Revising the ABCs of educational practices [Paper presentation]. International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies, Morne, Mauritius. https://doi.org/10.1109/ICNICONSMCL.2006.103
• Koole, M.L. (2009). A model for framing mobile learning. In M. Ally (Ed.), Mobile learning: Transforming the delivery of education and training (pp. 25-47). Athabasca University Press. https://www.aupress.ca/app/uploads/120155_99Z_Mohamed_Ally_2009-MobileLearning.pdf#page=45
• Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20(3), 271-289. https://doi.org/10.1017/S0958344008000335
• Lockhart, K.S. (2016). A comparison of the attitudes of administrators and teachers on cell phone use as an educational tool [Doctoral dissertation, University of Southern Mississippi]. ProQuest Library. https://search.proquest.com/dissertations-theses/comparison-attitudes-administrators-teachers-on/docview/1777350856/se-2?accountid=13014
• Losh, E. (2014). The war on learning: Gaining ground in the digital university. USA: MIT Press. https://mitpress.mit.edu/books/war-learning
• Lowenthal, J.N. (2010). Using mobile learning: Determinates impacting behavioral intention. The American Journal of Distance Education, 24(4), 195 206. https://doi.org/10.1080/08923647.2010.519947
• Merriam, S.B., & Tisdell, E.J. (2015). Qualitative research: A guide to design and implementation. San Francisco, CA: John Wiley & Sons. https://www.wiley.com/en-ae/Qualitative+Research%3A+A+Guide+to+Design+and+Implementation%2C+4th+Edition-p-9781119003618
• Motiwalla, L. (2007). Mobile learning: A framework and evaluation. Computers and Education, 49(3). 581-596. https://doi.org/10.1016/j.compedu.2005.10.011
• Nunnally, J.C. (1978). Psychometric theory (2nd ed.). McGraw Hill. https://www.semanticscholar.org/paper/Psychometric-theory-%2F-Jum-C.-Nunnally-Nunnally/3d976f16eab45d60d4a3269d1d8512b2ea44a511
• Obringer. J., & Coffey. K. (2007). Cell phones in American high schools: A national survey. The Journal of Technology Studies, 33(1), 41-47. https://eric.ed.gov/?id=EJ847358
• Purba, M., & Setyarini, S. (2020, October). Mobile Learning through WhatsApp: EFL Students’ Perceptions. In 2020 12th International Conference on Education Technology and Computers (pp. 27-32). https://doi.org/10.1145/3436756.3437016
• Park, Y. (2011). A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78 102. https://doi.org/10.19173/irrodl.v12i2.791
• Parsons, D., & Ryu, H. (2006, April). A framework for assessing the quality of mobile learning [Paper presentation]. 11th International Conference for Process Improvement, Research and Education, Southampton, UK. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.581.8490&rep=rep1&type=pdf
• Peters, K. (2007). M-Learning: Positioning educators for a mobile, connected future. International Review of Research in Open and Distance Learning, 8(2). https://doi.org/10.19173/irrodl.v8i2.350
• Preacher, K.J., & MacCallum, R.C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding statistics: Statistical Issues in Psychology, Education, and the Social Sciences, 2(1), 13-43. https://doi.org/10.1207/S15328031US0201_02
• Quaglia, R., & Corso, M. (2014). Student voice: The instrument of change. Corwin: A Sage Company: Thousand Oaks, CA. https://us.corwin.com/en-us/nam/student-voice/book243538
• Stevens, J.P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Erlbaum. https://psycnet.apa.org/record/1992-98099-000
• Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics. Needham Heights, MA: Allyn and Bacon. https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf
• Traxler, J. (2007). Defining, discussing and evaluating mobile learning: The moving finger writes and having writ... The International Review in Open and Distance Learning, 8(2), 1 13. http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip&db=eue&AN=507950980&site=eds-live&authtype=ip,uid
• 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
• Willis, G.B. (2004). Cognitive interviewing: A tool for improving questionnaire design. Sage Publications. https://www.jstor.org/stable/27641121
• Valk, J.H., Rashid, A.T., & Elder, L. (2010). Using mobile phones to improve educational outcomes: An analysis of evidence from Asia. The International Review of Research in Open and Distributed Learning, 11(1), 117 140. https://doi.org/10.19173/irrodl.v11i1.794
• 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
• Vosloo, S. (2012). Mobile learning and policies: Key issues to consider. Paris, France: UNESCO. http://unesdoc.unesco.org/images/ 0021/002176/217638E.pdf
• Yurdugül, H. (2007). The Effects of Different Correlation Types on Goodness-of-Fit Indices in First Order and Second Order Factor Analysis for Multiple Choice Test Data. İlköğretim Online, 6(1), 154-179. https://ilkogretim-online.org/?mno=121223
• Yurdugül, H., & Aşkar, P. (2008). An investigation of the factorial structures of pupils’ attitude towards technology (PATT): A Turkish sample. Elementary Education Online, 7(2), 288–309. http://ilkogretim-online.org/fulltext/218-1596636637.pdf?1612882858
• Yurdugül, H., & Sırakaya, D.A. (2013). The scale of online learning readiness: A study of validity and reliability. Eğitim ve Bilim [Education and Science], 38(169), 391-406. https://hdl.handle.net/20.500.12513/1731