Use of mind mapping in developing students’ comprehension skills in Kiswahili in secondary schools in Kakamega North Sub-County, Kenya
DOI:
https://doi.org/10.51867/ajernet.7.4.50Keywords:
Comprehension, Kiswahili, Mind Mapping, Students, TeachingAbstract
Kiswahili is among the most widely used languages for communication in the most parts of the world. Nevertheless, a considerable number of secondary school students continue to experience difficulties in comprehension, including understanding important ideas in written texts. Conventional teaching methods seem inadequate to assist as they limit students’ engagement. This study therefore determined the effect of mind mapping as an instructional strategy on developing Kiswahili comprehension skills among secondary school students. The study was anchored in Jerome Bruner’s (1966) Constructivist Learning Theory and employed a Solomon Four-Group quasi-experimental design. A target population was form three students from Kakamega North Sub-County, in Kenya with a sample size of 205 was used as experimental and control groups. Purposive Random sampling technique was employed to four secondary schools. Data was collected using Kiswahili performance tests as pretest and posttest. Data was analyzed using descriptive statistics that involved the use of means and standard deviations and inferential statistics which involved t-test and two-way Analysis of Variance (ANOVA) to compare pretest and posttest. Results of the study indicate significant differences between control groups and experimental groups in Kiswahili Achievement Test. The finding showed that experimental groups outperformed the control groups in Kiswahili achievement posttest. The study concluded that mind mapping improved students achievement in Kiswahili. It was recommended that the use of mind mapping be encouraged in teaching and learning of Kiswahili due to its positive effect on developing students’ comprehension skills. It is hoped that the findings of this study would help secondary school teachers to successfully apply mind mapping strategy in teaching to develop students’ comprehension skills.
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References
African Development Bank. (2020, April). Road safety framework and guidelines. Infrastructure and Urban Development Department.
Almaiah, M. A., Al-Otaibi, S., Shishakly, R., Hassan, L., Lutfi, A., Alrawad, M., et al. (2023). Investigating the role of perceived risk, perceived security and perceived trust on smart m-banking application using SEM. Sustainability, 15(13), Article 9908. https://doi.org/10.3390/su15139908
Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 211-218. https://doi.org/10.17705/1jais.00126
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Burton-Jones, A., & Straub, D. W., Jr. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246. https://doi.org/10.1287/isre.1060.0096
Chang, S.-C., & Tung, F.-C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
https://doi.org/10.1111/j.1467-8535.2007.00742.x
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
https://doi.org/10.1287/mnsc.35.8.982
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249689
Hair, J. F., Babin, J. B., Anderson, R. E., & Black, W. C. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://www.cengage.uk/c/multivariate-data-analysis-8e-hair-babin-anderson-black/9781473756540/
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
Land Transport Regulatory Authority. (2024). Annual performance report 2023/2024.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
https://doi.org/10.1016/S0378-7206(01)00143-4
Matemba, E. D., Li, G., Gogan, I. C. W., & Maiseli, B. J. (2020). Technology acceptance model: Recent developments, future directions, and proposal for hypothetical extensions. International Journal of Technology Intelligence and Planning, 12(4), 315-348.
https://doi.org/10.1504/IJTIP.2020.109769
Motsi, L., Chipangura, B., & Musanhi, N. (2023). A model for the adoption of transport management systems in the South African taxi industry. South African Journal of Information Management, 25(1), Article a1713. https://doi.org/10.4102/sajim.v25i1.1713
Ngumbi, D. N. (2016). A model for vehicle tracking and monitoring systems adoption in the Kenya Police Service [Master's thesis, Strathmore University]. https://su-plus.strathmore.edu/handle/11071/4845
Nyamawe, A. S., & Mbosso, E. C. (2014). Road safety: Adoption of ICT for tracking vehicles' over-speeding in Tanzania. International Journal of Computer Applications, 96(16), 12-15. https://doi.org/10.5120/16877-6876
Saunders, M., Lewis, P., & Thornhill, A. (2011). Research methods for business students (4th ed.). Pearson.
Sumuer, E., & Yildirim, S. (2015). Exploring user acceptance of an electronic performance support system. Performance Improvement Quarterly, 27(4), 29-48. https://doi.org/10.1002/piq.21178
Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296.
https://doi.org/10.1007/s11165-016-9602-2
Tavasoli, M., Sarrafzadeh, A., Khaleghi, M., Zakaria, M., Barahouei Pasandi, H., & Karimoddini, A. (2025). Data communication challenges of connected and automated vehicles in rural areas. IEEE Access, 13, 29220-29251. https://doi.org/10.1109/ACCESS.2025.3539598
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
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.
https://doi.org/10.2307/30036540
World Bank. (2017). The high toll of traffic injuries: Unacceptable and preventable. https://hdl.handle.net/10986/29129
World Health Organization. (2023). Global status report on road safety 2023. https://www.who.int/publications/i/item/9789240086517
Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper and Row.
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