A meta-analysis of the effectiveness of mobile supported collaborative learning

Olga V. Sergeeva 1, Marina R. Zheltukhina 2 * , Izida I. Ishmuradova 3, Nataliia A. Kondakchian 4, Natalya S. Erokhova 5, Sergei P. Zhdanov 6 7
More Detail
1 Kuban State University, Krasnodar, RUSSIA
2 Scientific and Educational Center «Person in Communication», Pyatigorsk State University, Pyatigorsk, RUSSIA
3 Kazan (Volga region) Federal University, Kazan, RUSSIA
4 Sechenov First Moscow State Medical University, Moscow, RUSSIA
5 Peoples’ Friendship University of Russia, Moscow, RUSSIA
6 National Research University “Moscow Power Engineering Institute”, Moscow, RUSSIA
7 Russian University of Transport, Moscow, RUSSIA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 15, Issue 1, Article No: e202508. https://doi.org/10.30935/ojcmt/15948
OPEN ACCESS   374 Views   84 Downloads   Published online: 11 Feb 2025
Download Full Text (PDF)

ABSTRACT

The objective of this meta-analysis study is to investigate learning results under mobile supported collaborative learning (MSCL). Robust Bayesian meta-analysis was applied to eleven studies from Scopus, Web of Science, and ERIC databases. The results reveal that MSCL has a modest but favorable effect generally (d = 0.26, 95% confidence interval [CI] [–0.34, 0.89]). Studies revealed substantial degrees of heterogeneity (τ = 0.556, 95% CI [0.305, 1.027], implying that contextual elements might influence the efficacy of MSCL. Moderator analyses showed that the MSCL was more successful at the high school level and had a greater and consistent influence especially on student motivation. Moderate publication bias was identified. These results highlight the value of MSCL as a potential improvement tool in education but suggest that its effectiveness may vary by context. Future research should examine in more detail the specific factors that increase or decrease the effectiveness of MSCL. Educators and policy makers should consider the potential benefits and limitations of this approach when implementing MSCL.

CITATION

Sergeeva, O. V., Zheltukhina, M. R., Ishmuradova, I. I., Kondakchian, N. A., Erokhova, N. S., & Zhdanov, S. P. (2025). A meta-analysis of the effectiveness of mobile supported collaborative learning. Online Journal of Communication and Media Technologies, 15(1), e202508. https://doi.org/10.30935/ojcmt/15948

REFERENCES

  • Aghajani, M., & Adloo, M. (2018). The effect of online cooperative learning on students’ writing skills and attitudes through telegram application. International Journal of Instruction, 11(3), 433–448. https://doi.org/10.12973/iji.2018.11330a
  • Bartoš, F., Maier, M., Quintana, D. S., & Wagenmakers, E. J. (2022). Adjusting for publication bias in JASP and R: Selection models, PET-PEESE, and robust Bayesian meta-analysis. Advances in Methods and Practices in Psychological Science, 5(3). https://doi.org/10.1177/25152459221109259
  • Benvenuti, M., Wright, M., Naslund, J., & Miers, A. C. (2023). How technology use is changing adolescents’ behaviors and their social, physical, and cognitive development. Current Psychology, 42(19), 16466–16469. https://doi.org/10.1007/s12144-023-04254-4
  • Borenstein, M., Hedges, L. V, Higgins, J. P. T., & Rothstein, H. R. (2021). Introduction to meta-analysis. John Wiley & Sons. https://doi.org/10.1002/9781119558378
  • Bringula, R. P., & Atienza, F. A. L. (2023). Mobile computer-supported collaborative learning for mathematics: A scoping review. Education and Information Technologies, 28(5), 4893–4918. https://doi.org/10.1007/s10639-022-11395-9
  • Cerratto Pargman, T., Nouri, J., & Milrad, M. (2018). Taking an instrumental genesis lens: New insights into collaborative mobile learning. British Journal of Educational Technology, 49(2), 219–234. https://doi.org/10.1111/bjet.12585
  • Chang, J. H., Chiu, P. S., & Huang, Y. M. (2018). A sharing mind map-oriented approach to enhance collaborative mobile learning With digital archiving systems. International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.3168
  • Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers and Education, 123, 53–64. https://doi.org/10.1016/j.compedu.2018.04.007
  • Fabian, K., Topping, K. J., & Barron, I. G. (2018). Using mobile technologies for mathematics: Effects on student attitudes and achievement. Educational Technology Research and Development, 66(5), 1119–1139. https://doi.org/10.1007/s11423-018-9580-3
  • Fanelli, D. (2012). Negative results are disappearing from most disciplines and countries. Scientometrics, 90(3), 891–904. https://doi.org/10.1007/s11192-011-0494-7
  • Fu, Q. K., & Hwang, G. J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers and Education, 119, 129–143. https://doi.org/10.1016/j.compedu.2018.01.004
  • Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E. J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760
  • Hanafi, H. F. Bin, Said, C. S., Ariffin, A. H., Zainuddin, N. A., & Samsuddin, K. (2016). Using a collaborative mobile augmented reality learning application (CoMARLA) to improve student learning. IOP Conference Series: Materials Science and Engineering, 160, Article 012111. https://doi.org/10.1088/1757-899X/160/1/012111
  • Huang, P. Sen, Chiu, P. S., Huang, Y. M., Zhong, H. X., & Lai, C. F. (2020). Cooperative mobile learning for the investigation of natural science courses in elementary schools. Sustainability, 12(16), Article 6606. https://doi.org/10.3390/su12166606
  • Jiang, D., & Zhang, L. J. (2020). Collaborating with ‘familiar’ strangers in mobile-assisted environments: The effect of socializing activities on learning EFL writing. Computers and Education, 150, Article 103841. https://doi.org/10.1016/j.compedu.2020.103841
  • Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09339057
  • Johnson, D. W., & Johnson, R. T. (2014). Using technology to revolutionize cooperative learning: An opinion. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.01156
  • Lee, W. C., & Lai, C. L. (2024). Facilitating mathematical argumentation by gamification: A gamified mobile collaborative learning approach for math courses. International Journal of Science and Mathematics Education, 22, 11–35. https://doi.org/10.1007/s10763-024-10462-6
  • Lin, C. C., Barrett, N. E., & Liu, G. Z. (2021). English outside the academic sphere: A mobile-based context-aware comparison study on collaborative and individual learning. Journal of Computer Assisted Learning, 37(3), 657–671. https://doi.org/10.1111/jcal.12514
  • Liu, I. F. (2024). Gamified mobile learning: Effects on English learning in technical college students. Computer Assisted Language Learning, 37(5–6), 1397–1420. https://doi.org/10.1080/09588221.2022.2080717
  • Liu, S.-H. (2015). The perceptions of participation in a mobile collaborative learning among pre-service teachers. Journal of Education and Learning, 5(1), 87–94. https://doi.org/10.5539/jel.v5n1p87
  • Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods, 28(1), 107–122. https://doi.org/10.1037/met0000405
  • Nikou, S. A., & Economides, A. A. (2021). A framework for mobile-assisted formative assessment to promote students’ self-determination. Future Internet, 13(5), Article 116. https://doi.org/10.3390/fi13050116
  • Rashtchi, M., & Porkar, R. (2020). Brainstorming revisited: Does technology facilitate argumentative essay writing? Language Teaching Research Quarterly, 18, 1–20. https://doi.org/10.32038/ltrq.2020.18.01
  • Reychav, I., & Wu, D. (2016). The interplay between cognitive task complexity and user interaction in mobile collaborative training. Computers in Human Behavior, 62, 333–345. https://doi.org/10.1016/j.chb.2016.04.007
  • Salhab, R., & Daher, W. (2023). University students’ engagement in mobile learning. European Journal of Investigation in Health, Psychology and Education, 13(1), 202–216. https://doi.org/10.3390/ejihpe13010016
  • Sung, H. Y., Hwang, G. J., & Chang, Y. C. (2016). Development of a mobile learning system based on a collaborative problem-posing strategy. Interactive Learning Environments, 24(3), 456–471. https://doi.org/10.1080/10494820.2013.867889
  • Sung, Y. T., Yang, J. M., & Lee, H. Y. (2017). The effects of mobile-computer-supported collaborative learning: Meta-analysis and critical synthesis. Review of Educational Research, 87(4), 768–805. https://doi.org/10.3102/0034654317704307
  • Tiantian, Y., Razali, A. B., Zulkifli, N. N., & Jeyaraj, J. J. (2024). The effects of collaborative mobile learning approach on academic performance: The mediating role of social interaction, and learning motivation. Journal of Pedagogical Research, 8(3), 209–229. https://doi.org/10.33902/JPR.202426264
  • Van der Linden, J., Erkens, G., Schmidt, H., & Renshaw, P. (2000). Collaborative learning. In R. J. Simons, J. van der Linden, & T. Duffy (Eds.), New learning (pp. 37–54). Springer. https://doi.org/10.1007/0-306-47614-2_3
  • Zhang, D., & Hwang, G.-J. (2023). Effects of interaction between peer assessment and problem-solving tendencies on students’ learning achievements and collaboration in mobile technology-supported project-based learning. Journal of Educational Computing Research, 61(1), 208–234. https://doi.org/10.1177/07356331221094250
  • Zou, W., & Li, X. (2015). Enhancing primary school students’ story writing by mobile-assisted collaborative learning: A case study. In W. Ma, A. Yuen, J. Park, W. Lau, & L. Deng (Eds.), New media, knowledge practices and multiliteracies (pp. 249–258). Springer Singapore. https://doi.org/10.1007/978-981-287-209-8_23