International Journal of


EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

line decor
line decor

 Volume 10, Issue 4 (April 2023), Pages: 128-135


 Original Research Paper

Attitudes of Saudi female students toward the use of mobile devices in learning computer programming: An empirical study


 Afrah Alanazi 1, *, Alice Li 2, Ben Soh 1


 1Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
 2Department of Management, Sport and Tourism, La Trobe University, Melbourne, Australia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile:

 Digital Object Identifier:


The purpose of this study is to explore the attitudes of Saudi Arabian female students toward mobile learning approaches pertaining to their learning experiences. Our methodology involved two groups–one that was subjected to a traditional teaching approach and the other (treatment group) that was subjected to a teaching approach with an intervention involving the ViLLE visualization tool during a semester in a programming class. We employed the Mobile Learning for Computer Programming framework to evaluate the perception of the use of mobile devices pertaining to the learning experience of female programming students in Saudi Arabia. Overall, the treatment group had positive attitudes toward mobile-based learning. This approach can promote engagement in learning systems, enhance the learning experience, improve the quality of learning, and help explain learner behavior.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Mobile learning, Attitude of students, Programming, Enhancement, Learning experience

 Article History: Received 24 September 2022, Received in revised form 1 January 2023, Accepted 27 January 2023


No Acknowledgment.

 Compliance with ethical standards

 Ethical consideration: Ethical approval was obtained before conducting the study (Ethics reference: HEC19520) at La Trobe University.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


 Alanazi A, Li A, and Soh B (2023). Attitudes of Saudi female students toward the use of mobile devices in learning computer programming: An empirical study. International Journal of Advanced and Applied Sciences, 10(4): 128-135

 Permanent Link to this page


 Fig. 1 Fig. 2 Fig. 3


 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6


 References (36)

  1. Abdüsselam MS (2014). Fizik öğretiminde artırılmış gerçeklik ortamlarının kullanımlarına ilişkin öğretmen ve öğrenci görüşleri: 11. sınıf manyetizma konusu örneği. Pegem Eğitim ve Öğretim Dergisi, 4(1): 59-74.   [Google Scholar]
  2. Alfarani LAK (2016). Exploring the influences on faculty members’ adoption of mobile learning at King Abdulaziz University, Saudi Arabia. Ph.D. Dissertation, University of Leeds, Leeds, UK.   [Google Scholar]
  3. Al-Kandari AJ, Al-Hunaiyyan AA, and Al-Hajri R (2016). The influence of culture on Instagram use. Journal of Advances in Information Technology, 7(1): 54-57.   [Google Scholar]
  4. Alsaggaf W, Hamilton M, Harland J, and D'Souza D (2012). The use of laptop computers in programming lectures. In the Proceedings of the 23rd Australasian Conference on Information Systems, ACIS, Geelong, Australia: 1-11.   [Google Scholar]
  5. Alsaggaf WAO (2013). A constructivist, mobile and principled approach to the learning and teaching of programming. Ph.D. Dissertation, RMIT University, Melbourne, Australia.   [Google Scholar]
  6. Bertolotti DS (1984). Culture and technology. Bowling Green State University Popular Press, Bowling Green, USA.   [Google Scholar]
  7. Binsahl H, Chang S, and Bosua R (2020). Cross-cultural digital information-seeking experiences: The case of Saudi Arabian female international students. Journal of International Students, 10(4): 872-891.   [Google Scholar]
  8. Brereton P, Turner M, and Kaur R (2009). Pair programming as a teaching tool: A student review of empirical studies. In the 22nd Conference on Software Engineering Education and Training, IEEE, Hyderabad, India: 240-247.   [Google Scholar]
  9. Briggs SR and Cheek JM (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1): 106-148.   [Google Scholar]
  10. Brown JD (2002). The Cronbach alpha reliability estimate. JALT Testing and Evaluation SIG Newsletter, 6(1): 17-19.   [Google Scholar]
  11. Camilleri MA and Camilleri AC (2019). The students’ readiness to engage with mobile learning apps. Interactive Technology and Smart Education, 17(1): 28-38.   [Google Scholar]
  12. Chickering AW and Gamson ZF (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7): 3–7.   [Google Scholar]
  13. Davis FD (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Ph.D. Dissertation, Massachusetts Institute of Technology, Cambridge, USA.   [Google Scholar]
  14. Fishbein M, Ajzen I, and Hinkle R (1980). Predicting and understanding voting in American elections: Effects of external variables. In: Azjen I (Ed.), Understanding attitudes and predicting social behaviour: 173-195. Prentice-Hall, Englewood Cliffs, USA.   [Google Scholar]
  15. Halim NFA and Phon DNE (2020). Mobile learning application impact towards student performance in programming subject. In the IOP Conference Series: Materials Science and Engineering, IOP Publishing, Chennai, India, 769(1): 012056.   [Google Scholar]
  16. Jakkaew P and Hemrungrote S (2017). The use of UTAUT2 model for understanding student perceptions using Google classroom: A case study of introduction to information technology course. In the International Conference on Digital Arts, Media and Technology, IEEE, Chiang Mai, Thailand: 205-209.   [Google Scholar]
  17. Klawe MM (1998). When does the use of computer games and other interactive multimedia software help students learn mathematics? Department of Computer Science the University of British Columbia, Vancouver, Canada.   [Google Scholar]
  18. Krejcie RV and Morgan DW (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3): 607-610.   [Google Scholar]
  19. Loch KD, Straub DW, and Kamel S (2003). Diffusing the internet in the Arab world: The role of social norms and technological culturation. IEEE Transactions on Engineering Management, 50(1): 45-63.   [Google Scholar]
  20. Nunnally JC (1994). Psychometric theory. 3rd Edition, McGraw-Hill, New York, USA.   [Google Scholar]
  21. Packer J (2006). Learning for fun: The unique contribution of educational leisure experiences. Curator: The Museum Journal, 49(3): 329-344.   [Google Scholar]
  22. Pallant J (2020). SPSS: Survival manual: A step by step guide to data analysis using IBM SPSS. 7th Edition, Routledge, London, UK.   [Google Scholar]
  23. 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.   [Google Scholar] PMid:33192838 PMCid:PMC7653185
  24. Papadakis S, Vaiopoulou J, Sifaki E, Stamovlasis D, and Kalogiannakis M (2021). Attitudes towards the use of educational robotics: Exploring pre-service and in-service early childhood teacher profiles. Education Sciences, 11(5): 204.   [Google Scholar]
  25. Rajala T, Laakso MJ, Kaila E, and Salakoski T (2008). Effectiveness of program visualization: A case study with the ViLLE tool. Journal of Information Technology Education, Innovations in Practice, 7: 15-32.   [Google Scholar]
  26. Rittgen P (2010). Quality and perceived usefulness of process models. In the 2010 ACM Symposium on Applied Computing, Association for Computing Machinery, Sierre, Switzerland: 65-72.   [Google Scholar]
  27. Sahin D and Yilmaz RM (2020). The effect of augmented reality technology on middle school students' achievements and attitudes towards science education. Computers and Education, 144: 103710.   [Google Scholar]
  28. Salloum SA, Alhamad AQM, Al-Emran M, Monem AA, and Shaalan K (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7: 128445-128462.   [Google Scholar]
  29. Schepers J and Wetzels M (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1): 90-103.   [Google Scholar]
  30. Scherer R, Tondeur J, Siddiq F, and Baran E (2018). The importance of attitudes toward technology for pre-service teachers' technological, pedagogical, and content knowledge: Comparing structural equation modeling approaches. Computers in Human Behavior, 80: 67-80.   [Google Scholar]
  31. Smith KA, Sheppard SD, Johnson DW, and Johnson RT (2005). Pedagogies of engagement: Classroom‐based practices. Journal of Engineering Education, 94(1): 87-101.   [Google Scholar]
  32. Tan PJB (2019). An empirical study of how the learning attitudes of college students toward English e-tutoring websites affect site sustainability. Sustainability, 11(6): 1748.   [Google Scholar]
  33. Taylor S and Todd PA (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2): 144-176.   [Google Scholar]
  34. Traxler J (2007). Defining, discussing and evaluating mobile learning: The moving finger writes and having writ. International Review of Research in Open and Distance Learning, 8(2): 1-12.   [Google Scholar]
  35. Walabe E (2020). E-learning delivery in Saudi Arabian universities. Ph.D. Dissertation, University of Ottawa, Ottawa, Canada.   [Google Scholar]
  36. Yallihep M and Kutlu B (2020). Mobile serious games: Effects on students’ understanding of programming concepts and attitudes towards information technology. Education and Information Technologies, 25(2): 1237-1254.   [Google Scholar]