The Effectiveness of WhatsApp Mobile Learning Activities Guided by Activty Theory on Students' Knowldege Management

Abstract

This research paper explores the effectiveness of using mobile technologies to support a blended learning course titled Scientific Research Methods in Information Science. Specifically, it discusses the effects of WhatsApp mobile learning activities guided by activity theory on students' knowledge Management (KM). During the 2014 academic year, the researcher adopted an experimental approach-based comparison between an experimental group (34 students) and a control group (34 students). The learning process of the experimental group was based on continuity between 2 hours of in-class learning and 1 hour of learning activities that were mediated by WhatsApp instant messaging each week. The control group’s experience was 100% in-class with no app mediation. The researcher used the t-test to compare the means of the control and experimental groups in the test and the students’ attitudes at 0.05 alpha levels. This research paper is useful for exploring the effectiveness of mobile technologies in supporting blended learning courses.

Keywords

• WhatsApp, Mobile teaching, Mobile learning, Mobile tools, Activity theory

References

1. Barhoumi, C. & Rossi, PG. (2013). The effectiveness of the instruction oriented hypertext systems compared to direct instruction in e-learning environment. Contemporary Educational Technology, 4(4), 281-308.
2. Baran, B. (2010). Experiences from the process of designing lessons with interactive whiteboard: ASSURE as a road map. Contemporary Educational Technology, 1(4), 367- 380.
3. Bertelsen, O. W. and S. Bodker. (2003). Activity theory. In J. M. Carroll (Ed.), HCI models, theories, and frameworks: Toward a multidisciplinary science (pp.291-324). San Francisco, CA: Morgan Kaufmann.
4. Bielaczyc, K. & Collins, A. (1999). Learning communities in classrooms: a reconceptualization of educational practice. In C. M. Reigeluth (Ed.). Instructional design theories and models, 2, 269–291. Mahwah, NJ: Lawrence Erlbaum Associates.
5. Chan, L. (2005). WebCT revolutionized e-learning. UBC Reports, 51(7). Retrieved on 10 July 2015 from http://news.ubc.ca/ubcreports/2005/05jul07/webct.html
6. Cheung, W. S., Hew, K. F., & Ng, S. L. (2008). Toward an understanding of why students contribute in asynchronous online discussions. Journal of Educational Computing Research, 38(1), 29–50.
7. Cross, K. P. (1998). Why learning communities? Why now. About Campus, 3(3), 4-11.
8. Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit Oy.

9. Gillingham, M. G. & Topper, A. (1999). Technology in teacher preparation: Preparing teachers for the future. Journal of Technology & Teacher Education, 7(4), 303-321.
10. Hewitt, J. (2004). An exploration of community in a knowledge forum classroom: an activity system analysis. In S. A. Barab, R. Kling, & J. H. Gray (Eds.), Designing for virtual communities in the service of learning (pp. 210–238). Cambridge: Cambridge University Press.
11. Inan, F. A., Flores, R., Grant, M.M. (2010). Perspectives on the design and evaluation of adaptive web based learning environments. Contemporary Educational Technology, 2010, 1(2), 148-159.
12. Jimoyiannis, A., Tsiotakis, P., Roussinos, D., & Siorenta, A. (2013) Preparing teachers to integrate mobile in school practice: Toward a framework for Pedagogy 2.0. Australasian Journal of Educational Technology, 29(2), 248-26.
13. Laningham, S. (2006). DeveloperWorks interviews: Tim Berners-Lee. Retrieved on 30 November 2014 from www.ibm.com/developersworks/podcast/dwi/cm-int082206txt.html.
14. Lu, J. & Churchill, D. (2014). Using social networking environments to support collaborative learning in a Chinese university class: Interaction pattern and influencing factors. Australasian Journal of Educational Technology, 30(4), 472-486.
15. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1–55.
16. Litchfield, A., et al. (2007). Directions for m-learning research to enhance active learning. Paper presented at ASCILITE. Singapore.
17. Nardi, B. (Ed.). (1996). Context and consciousness: Activity theory and human computer interaction. Cambridge, MA: MIT Press.
18. Nelson, J., Christopher, A., & Mims, C. (2009). TPACK and Mobile: Transformation of teaching and learning. TechTrends: Linking Research & Practice to Improve Learning, 53(5), 80–87.
19. Preston, G., Phillips, R., Gosper, M., McNeill, M., Woo, K., & Green, D. (2010). Web-based lecture technologies: Highlighting the changing nature of teaching and learning. Australasian Journal of Educational Technology, 26(6), 717-728.
20. Pence, H. E. (2007). Preparing for the real Web generation. Journal of Educational Technology Systems, 35(3), 347-356.
21. Rovai, A. P. (2002). Development of an instrument to measure classroom community. Internet and Higher Education, 5, 197–211.
22. Strijbos, J. W. & Fischer, F. (2007). Methodological challenges for collaborative learning research. Learning and Instruction, 17(4), 389-393.
23. Tillema, H. Orland-Barak, L. (2006). Constructing knowledge in professional conversations: The role of beliefs on knowledge and knowing. Learning and Instruction, 16, 592-608.
24. Williams, A., Birch, E., & Hancock, P. (2012). The impact of online lecture recordings on student performance. Australasian Journal of Educational Technology, 28(2), 199-213.
25. Yu, A. Y., Tian, S. W., Vogel, D., & Kwok, R. C. W. (2010). Can learning be virtually boosted? An investigation of online social networking impacts. Computers & Education, 55(4), 1494- 1503.
26. Correspondence: Assistant Professor, Department of Information Sciences and Learning
27. Resources, Faculty of Education and Human Sciences, Taibah University, Madinah
28. Mounawwara, Saudi Arabia