Leveraging on Virtual Laboratory-Based Instruction to Achieve Active Classroom Interaction in Teaching and Learning of Physics in Secondary Schools in Kenya





Virtual Laboratory (VL), ICT integration in learning, Active learning, Experiments


The philosophy that advances the study of physics as one of the learning areas in secondary schools cannot be overlooked due to the realization of the contributions of physics towards the industrial and vocational development of the country. The major concern of this particular study is the consistent poor average scores in the subject currently witnessed at the Kenya Certificate of Secondary Education (KCSE) level in Physics, which can be attributed to low student motivation and traditional teaching strategies. For instance, in the years 2016, 2017, 2018, and 2019, Kisumu County registered low mean scores of 4.23, 4.98, 4.67, and 4.10, respectively, in physics in KCSE. The use of Information and Communications Technology (ICT) in the teaching and learning of physics can be used to promote learners’ centered teaching and learning of physics and eventually improve physics scores. This study therefore underscores the need for virtual laboratory-based instruction (VLBI) to realize active classroom interaction during the teaching and learning of physics. Technology-based enhanced learning leverages all learners, irrespective of their economic and demographic background. The study therefore aimed at establishing the effect of VLBI on students’ level of interaction in the classroom in learning physics in secondary schools in Kenya. The study is supported by behaviorism learning theory and adopted quasi-experimental research designs. The physics teachers were purposefully sampled from each selected school. Physics teachers were purposefully sampled from each of the schools selected. There were 358 students and 72 teachers in the sample. The data was analyzed using both descriptive and inferential statistics. The study concluded that virtual laboratories enhanced the use of experimental teaching approaches and that there was no statistical significance between the knowledge of the teacher about the selected ICT framework and the use of virtual experiments. There is a need to develop a prototype for a technology-enriched ICT framework for active learning of physics that will provide teachers and students with an interactive platform to promote active physics learning.


Abdurrahman, A., Saregar, A., & Umam, R. (2018). The effect of feedback as soft scaffolding on ongoing assessment toward the quantum physics concept mastery of the prospective physics teachers. Jurnal Pendidikan IPA Indonesia, 7(1), 4-7.

https://doi.org/10.15294/jpii.v6i2.7239 DOI: https://doi.org/10.15294/jpii.v6i2.7239

Beatty, B. J., & Albert, M. (2016). Student perceptions of a flipped classroom management course. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-09-2015-0069 DOI: https://doi.org/10.1108/JARHE-09-2015-0069

Bergman, P. (2015). Parent-child information frictions and human capital investment: Evidence from a field experiment. The University of Chicago Press Journals.

https://doi.org/10.2139/ssrn.2622034 DOI: https://doi.org/10.2139/ssrn.2622034

Conklin, W. (2011). Higher-order thinking skills to develop 21st century learners. Teacher Created Materials.

Desman, N & ,Telaumb, D. (2017). Experimental Method Application In Teaching Physics Education. Asian Journal of Social Sciences & Humanities, 6(4), 84-90.

Holmes, M. R., Tracy, E. M., Painter, L. L., Oestreich, T., & Park, H. (2015). Moving from flipcharts to the flipped classroom: Using technology driven teaching methods to promote active learning in foundation and advanced masters social work courses. Clinical social work journal, 43(2), 215-224.

https://doi.org/10.1007/s10615-015-0521-x DOI: https://doi.org/10.1007/s10615-015-0521-x

Kothari, C. (2017). Research methodology methods and techniques. New Age International (P) Ltd., Publishers, 91.

Lam, J. (2015, July). Collaborative learning using social media tools in a blended learning course. In International conference on hybrid learning and continuing education (pp. 187-198). Springer, Cham.

https://doi.org/10.1007/978-3-319-20621-9_15 DOI: https://doi.org/10.1007/978-3-319-20621-9_15

Liu, C. Y., Wu, C. J., Wong, W. K., Lien, Y. W., & Chao, T. K. (2017). Scientific modeling with mobile devices in high school physics labs. Computers & Education, 105, 44-56.

https://doi.org/10.1016/j.compedu.2016.11.004 DOI: https://doi.org/10.1016/j.compedu.2016.11.004

Manisha, P. (2012). Leukemia: a review article. International Journal of Advanced Research in Pharmaceutical & Bio Sciences, 1(4), 397-408.

Minishi-Majanja, M. K., & Kiplang'at, J. (2005). The diffusion of innovations theory as a theoretical framework in library and information science research. South African journal of libraries and information science, 71(3), 211-224.

https://doi.org/10.7553/71-3-586 DOI: https://doi.org/10.7553/71-3-586

Papadopoulos, P., Natsis, A., & Obwegeser, N. (2018, June). Response Justifications as Feedback in Clicker Activities: A Case Study on Student Performance and Calibration. In EdMedia+ Innovate Learning (pp. 408-413). Association for the Advancement of Computing in Education (AACE).

Quintana, M. G. B., & Zambrano, E. P. (2014). E-mentoring: The effects on pedagogical training of rural teachers with complex geographical accesses. Computers in Human Behavior, 30, 629-636.

https://doi.org/10.1016/j.chb.2013.07.042 DOI: https://doi.org/10.1016/j.chb.2013.07.042

Semela, T. (2010). Who Is Joining Physics and Why? Factors Influencing the Choice of Physics among Ethiopian University Students. International Journal of Environmental and Science Education, 5(3), 319-340.

Smith, M. K., & Knight, J. K. (2020). Clickers in the Biology Classroom: Strategies for Writing and Effectively Implementing Clicker Questions That Maximize Student Learning. In Active Learning in College Science (pp. 141-158). Springer, Cham.

https://doi.org/10.1007/978-3-030-33600-4_10 DOI: https://doi.org/10.1007/978-3-030-33600-4_10

Stowell, J. I., & Gostjev, F. A. (2018). Immigration and Crime Rates: Lasting Trends and New Understandings. In Routledge Handbook on Immigration and Crime (pp. 81-92). Routledge.

https://doi.org/10.4324/9781317211563-7 DOI: https://doi.org/10.4324/9781317211563-7

Villers, R. (2007). The six C's framework for e-learning. N. Buzzetto-More, Advanced principles of effective e-learning, 1-25. Informing Science Press

Wu, Y. C. J., Wu, T., & Li, Y. (2019). Impact of using classroom response systems on students' entrepreneurship learning experience. Computers in Human Behavior, 92, 634-645.

https://doi.org/10.1016/j.chb.2017.08.013 DOI: https://doi.org/10.1016/j.chb.2017.08.013

Yamane, T., & Israel, G. D. (1992). Determining sample size. Program evaluation and organizational development, Florida cooperative extension service. Gainesville (FL): Institute of Food and Agricultural Sciences, University of Florida.

Zamani, G., & Rezvani, R. (2015). 'HOTS'in Iran's Official Textbooks: Implications for Material Design and Student Learning. Journal of Applied Linguistics and Language Research, 2(5), 138-151.




How to Cite

Okono, E., Abenga, E., & Ayoti, C. (2023). Leveraging on Virtual Laboratory-Based Instruction to Achieve Active Classroom Interaction in Teaching and Learning of Physics in Secondary Schools in Kenya. African Journal of Empirical Research, 4(2), 1152–1156. https://doi.org/10.51867/ajernet.4.2.117