FROM ABSTRACTION TO UNDERSTANDING: THE IMPACT OF COMPUTER SIMULATIONS ON GENETICS LEARNING AND STUDENT PERCEPTIONS IN NZEMA EAST MUNICIPALITY
DOI:
https://doi.org/10.46827/ejes.v12i11.6306کلمات کلیدی:
computer simulations, genetics education, quasi-experimental design, student perceptions, Nzema East Municipalityچکیده
Genetics is a challenging area of senior high school biology due to its abstract molecular concepts, often difficult for students to grasp through conventional teaching. This study examined the effectiveness of computer simulation–based instruction in improving achievement and perceptions of genetics using a quasi-experimental one-group pretest–posttest design within a mixed-methods framework. The sample included 120 Form Three Biology students from three senior high schools in Nzema East Municipality. Data were collected using the Students’ Knowledge in Genetics Test (SKGT) and Students’ Achievement in Genetics Test (SAGT), both showing good reliability (α = 0.79 and 0.84; κ = 0.75 and 0.64). Wilcoxon Signed Rank Test results indicated significant improvement, with mean scores rising from 8.80 to 23.30 (z = 9.52, p = 0.001, r = 0.87). Interviews with 15 students revealed positive perceptions, including enhanced clarity, motivation, retention, engagement, and real-life application. The study concludes that computer simulations significantly enhance performance and attitudes, recommending curriculum integration and teacher training.
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مراجع
Adelana, O. P., Ayanwale, M. A., & Sanusi, I. T. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring-based system. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2310976
Appiah, S., & Essiam, C. (2022). Effect of computer-assisted instruction on students’ performance in selected cell division topics: A quasi-experimental study at Adisadel College, Ghana. Journal of Advocacy, Research and Education, 9(2), 86-91. Retrieved from https://www.mendeley.com/catalogue/416ff069-404f-30f1-9b72-c392ea25fa57/
Bada, S. O., & Olusegun, S. (2015). Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education, 5(6), 66-70. Retrieved from https://iosrjournals.org/iosr-jrme/papers/Vol-5%20Issue-6/Version-1/I05616670.pdf
Borrego, M., & Henderson, C. (2014). Increasing the use of evidence‐based teaching in STEM higher education: A comparison of eight change strategies. Journal of Engineering Education, 103(2), 220-252. Retrieved from https://doi.org/10.1002/jee.20040
Byukusenge, C., Nsanganwimana, F., & Tarmo, A. P. (2024). Investigating the effect of virtual laboratories on students’ academic performance and attitudes towards learning biology. Education and Information Technologies, 29(1), 1147-1171. https://doi.org/10.1007/s10639-023-12351-x
Chang, H. Y., Liang, J. C., & Tsai, C. C. (2020). Students’ context-specific epistemic justifications, prior knowledge, engagement, and socioscientific reasoning in a mobile augmented reality learning environment. Journal of Science Education and Technology, 29(3), 399-408. https://doi.org/10.1007/s10956-020-09825-9
Chu, Y. C. (2008). Learning difficulties in genetics and the development of related attitudes in Taiwanese junior high schools (Doctoral dissertation, University of Glasgow). Retrieved from https://eleanor.lib.gla.ac.uk/record=b2624722
Clapper, T. C. (2015). Cooperative-based learning and the zone of proximal development. Simulation & Gaming, 46(2), 148-158. Retrieved from https://doi.org/10.1177/1046878115569044
Clark, R., & Clark, V. P. (2022). The use of mixed methods to advance positive psychology: a methodological review. International Journal of Wellbeing, 12(3). https://doi.org/10.5502/ijw.v12i3.2017
Darrah, M., Humbert, R., Finstein, J., Simon, M., & Hopkins, J. (2014). Are virtual labs as effective as hands-on labs for undergraduate physics? A comparative study at two major universities. Journal of science education and technology, 23(6), 803-814. https://doi.org/10.1007/s10956-014-9513-9
De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of educational research, 68(2), 179-201. https://doi.org/10.3102/00346543068002179
Deep, A., Murthy, S., & Bhat, J. (2020). Geneticus Investigatio: a technology-enhanced learning environment for scaffolding complex learning in genetics. Research and Practice in Technology Enhanced Learning, 15(1), 24. https://doi.org/10.1186/s41039-020-00145-5
Duncan, R. G., & Reiser, B. J. (2007). Reasoning across ontologically distinct levels: Students' understandings of molecular genetics. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 44(7), 938-959. https://doi.org/10.1002/tea.20186
Elendu, C., Amaechi, D. C., Okatta, A. U., Amaechi, E. C., Elendu, T. C., Ezeh, C. P., & Elendu, I. D. (2024). The impact of simulation-based training in medical education: A review. Medicine, 103(27). https://doi.org/10.1097/md.0000000000038813
Fabeku, O. E., & Enyeasi, S. C. (2024). Computer simulation and video media instructional packages in improving learning outcomes of Chemistry students in Ife Central Local Government Area. Disciplinary and Interdisciplinary Science Education Research, 6(1), 19. Retrieved from https://diser.springeropen.com/articles/10.1186/s43031-024-00109-5
Ferguson, S. (2012). Like a bridge: Scaffolding as a means of assisting low-attaining students in mathematics during cognitively challenging tasks (Doctoral dissertation, Australian Catholic University). https://doi.org/10.4226/66/5A96271DC6888
George, D., & Mallery, P. (2018). Descriptive statistics. In IBM SPSS Statistics 25 Step by Step (pp. 126-134). Routledge. Retrieved from https://www.taylorfrancis.com/chapters/edit/10.4324/9781351033909-14/descriptive-statistics
Gottheiner, D. M., & Siegel, M. A. (2012). Experienced middle school science teachers’ assessment literacy: Investigating knowledge of students’ conceptions in genetics and ways to shape instruction. Journal of Science Teacher Education, 23(5), 531-557. https://doi.org/10.1007/s10972-012-9278-z
Hertel, J. P., & Millis, B. (2023). Using simulations to promote learning in higher education: An introduction. Routledge. https://doi.org/10.4324/9781003448594
Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in nursing & health, 31(2), 180-191. Retrieved from https://doi.org/10.1002/nur.20247
Huang, C. (2013). Gender differences in academic self-efficacy: A meta-analysis. European journal of psychology of education, 28(1), 1-35. https://doi.org/10.1007/s10212-011-0097-y
Jalmo, T., & Suwandi, T. (2018). Biology Education Students' Mental Models on Genetic Concepts. Journal of Baltic Science Education, 17(3), 474-485. Retrieved from https://www.scientiasocialis.lt/jbse/?q=node/675
Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 26(1), 16-30. Retrieved from https://www.jstor.org/stable/48707964
Krüger, J. (2022). How to communicate complex research: effects of hands-on experiments and computer simulations in marine ecology outreach activities (Doctoral dissertation). Retrieved from https://books.google.ro/books/about/How_to_Communicate_Complex_Research.html?id=rsCa0AEACAAJ&redir_esc=y
Landis, J. R., & Koch, G. G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 363-374. Retrieved from https://doi.org/10.2307/2529786
Ledger, S., Burgess, M., Rappa, N., Power, B., Wong, K. W., Teo, T., & Hilliard, B. (2022). Simulation platforms in initial teacher education: Past practice informing future potentiality. Computers & Education, 178. https://doi.org/10.1016/j.compedu.2021.104385
Liu, Z., Zhang, N., Peng, X., Liu, S., & Yang, Z. (2023). Students’ social-cognitive engagement in online discussions. Educational Technology & Society, 26(1), 1-15. Retrieved from https://www.jstor.org/stable/48707963
Mackenzie, N., & Knipe, S. (2006). Research dilemmas: Paradigms, methods and methodology. Issues in educational research, 16(2), 193-205. Retrieved from https://www.iier.org.au/iier16/mackenzie.html
Mayer, R. E. (2014). Incorporating motivation into multimedia learning. Learning and instruction, 29, 171-173. https://doi.org/10.1016/j.learninstruc.2013.04.003
Mayer, R. E. (2021). Evidence-based principles for how to design effective instructional videos. Journal of Applied Research in Memory and Cognition, 10(2), 229-240. https://doi.org/10.1016/j.jarmac.2021.03.007
Mills Shaw, K. R., Van Horne, K., Zhang, H., & Boughman, J. (2008). Essay contest reveals misconceptions of high school students in genetics content. Genetics, 178(3), 1157-1168. https://doi.org/10.1534/genetics.107.084194
Nkosi, T. P. (2019). The Effects of Virtual Reality and Physical Models on Grade 11 Learner Understanding of Geometric Shapes in the Chemistry Classroom (Master's thesis, University of South Africa (South Africa). https://ir.unisa.ac.za/handle/10500/29900
Nsabayezu, E., Habimana, O., Nzabalirwa, W., & Niyonzima, F. N. (2025). Leveraging multimedia-supported flipped classrooms approach to modernize organic chemistry instruction: Exploring students' engagement and motivation. Education for Chemical Engineers. https://doi.org/10.1016/j.ece.2025.08.001
O'Connor, E., McDonald, F., & Ruggiero, M. (2014). Scaffolding complex learning: Integrating 21st century thinking, emerging technologies, and dynamic design and assessment to expand learning and communication opportunities. Journal of Educational Technology Systems, 43(2), 199-226. https://doi.org/10.2190/ET.43.2.g
Omona, J. (2013). Sampling in qualitative research: Improving the quality of research outcomes in higher education. Makerere Journal of Higher Education, 4(2), 169-185. https://doi.org/10.4314/majohe.v4i2.4
Orman, T. F. (2016). “Paradigm” as a central concept in Thomas Kuhn’s thought. International Journal of Humanities and Social Science, 6(10), 47-52. Retrieved from https://www.ijhssnet.com/journals/Vol_6_No_10_October_2016/8.pdf
Patton, C., Sawicki, D., & Clark, J. (2015). Basic methods of policy analysis and planning. Routledge. Retrieved from https://www.researchgate.net/profile/Jennifer-Clark-19/publication/49249379_Basic_Methods_of_Policy_Analysis_and_Planning/links/58000a2808ae32ca2f5db852/Basic-Methods-of-Policy-Analysis-and-Planning.pdf
Pretorius, L. (2023). An altered flipped class pedagogy as intervention strategy to address passive learning in a teacher-centred classroom (Doctoral dissertation, University of South Africa (South Africa)). https://hdl.handle.net/10500/30641
Reen, F. J., Jump, O., McEvoy, G., McSharry, B. P., Morgan, J., Murphy, D., ... & Supple, B. (2025). Student informed development of virtual reality simulations for teaching and learning in the molecular sciences. Journal of Biological Education, 59(4), 604-620. https://doi.org/10.1080/00219266.2024.2386250
Reddi, R. S. K., Alekhya, B., & Ambareesha, K. (2025). Traditional chalk-and-talk vs. PowerPoint presentations: analysing the most effective teaching method for mbbs students. Int J Acad Med Pharm, 7(2), 190-197.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational psychology, 61. https://doi.org/10.1016/j.cedpsych.2020.101860
Silumesi, L. (2022). An investigation of the difficulties in teaching and learning of genetics and related topics (Master's thesis, University of South Africa (South Africa)). Retrieved from https://ir.unisa.ac.za/handle/10500/29712
Singh-Pillay, A. (2024). Exploring science and technology teachers’ experiences with integrating simulation-based learning. Education Sciences, 14(8), 803. https://doi.org/10.3390/educsci14080803
Sun, Y., Yan, Z., & Wu, B. (2022). How differently designed guidance influences simulation‐based inquiry learning in science education: A systematic review. Journal of Computer Assisted Learning, 38(4), 960-976. https://doi.org/10.1111/jcal.12667
Tashakkori, A., & Teddlie, C. (2010). Putting the human back in ‘‘human research methodology’’: The researcher in mixed methods research. Journal of mixed methods research, 4(4), 271-277. https://doi.org/10.1177/1558689810382532
Weyant, E. (2022). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches: by John W. Creswell and J. David Creswell, Los Angeles, CA: SAGE, 2018, $38.34, 304pp. https://doi.org/10.1080/15424065.2022.2046231
Yarden, A., & Levkovich, O. (2021). Teaching and Learning Biology Using Authentic Tools and Databases. Long-term Research and Development in Science Education, 71. Retrieved from https://weizmann.elsevierpure.com/en/publications/teaching-and-learning-biology-using-authentic-tools-and-databases
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