American Journal of Educational Research. 2017, 5(12), 1223-1227
DOI: 10.12691/education-5-12-9
Open AccessArticle
Elizabeth Boby Samuel1, Beauty Choobe2, Boby Samuel3, , George Kasali3 and Tailoka Frank P1
1The Copperbelt University, School of Mathematics and Natural Sciences, Department of Mathematics, Kitwe, Zambia
2The Copperbelt University, School of Mathematics and Natural Sciences, Department of Mathematics and Science Education, Kitwe, Zambia
3The Copperbelt University, School of Mathematics and Natural Sciences, Department of Biological Sciences, Kitwe, Zambia
Pub. Date: December 26, 2017
Cite this paper:
Elizabeth Boby Samuel, Beauty Choobe, Boby Samuel, George Kasali and Tailoka Frank P. Exploring the Factors Affecting the Integration of Mathematical Skills into Biology Learning in Copperbelt Secondary Schools of Zambia. American Journal of Educational Research. 2017; 5(12):1223-1227. doi: 10.12691/education-5-12-9
Abstract
21st century biology is becoming increasingly quantitative, requiring students to master mathematical knowledge for them to fully understand and apply the new emerging biological concepts and skills for socio-economic development. This study, therefore, aimed to determine the factors affecting the integration of mathematical skills into biology curricula at secondary school level in Zambia. Primary data was collected using a quantitative biology assessment test for biology class pupils and survey questionnaires for both biology teachers and pupils. Secondary data was obtained from past biology examinations papers of the Zambia General Certificate of Secondary Education/O-level (GCSE) and the Cambridge University International General Certificate of Secondary Education (IGCSE) for the years 2010 – 2016. The study sample was 140 biology class students and 16 biology teachers selected from 8 secondary schools in the Copperbelt province of Zambia. Statistical analyses using descriptive statistics, one-way ANOVA and factor analysis were conducted using SPSS and Excel. The results of this study revealed a mean pass rate in the quantitative biology test of merely 14.3%, with the lowest mean score (6%) obtained for questions in the higher order cognitive levels of Bloom’s taxonomy. The coverage of mathematics related questions in the national biology examinations was low at 7.1% and 18.9% for GCSE and IGCSE, respectively. Overall the pupils had inadequate skills for tackling quantitative biology questions. The contributing factors for this situation included the lack of quantitative competencies amongst biology teachers and the unavailability of appropriate textbooks to guide teachers and students. The biology syllabi and assessment requirements do not compel teachers to teach mathematical biology. Consequently, students exhibited phobia and a negative attitude towards quantitative biology. Overall, Zambia requires a new educational policy framework that should drive the integration of mathematics and biology throughout the Zambian educational system.Keywords:
students mathematics biology integration teachers
This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
References:
[1] | Thomas, G. 2017. Beyond D'Arcy Thompson: Future challenges for quantitative biology, Mechanisms of Development, 145. pp. 10-12. |
|
[2] | Jason F, Helen V and Jose H. 2013. On the edge of mathematics and biology integration: Improving quantitative skills in undergraduate biology education, CBE-Life Sciences Education, Vol. 12, 124-128. |
|
[3] | Nieuwenhuis S, Forstmann B and Wagenmakers E. 2011. Erroneous analyses of interactions in neuroscience: a problem of significance, Nature Neuroscience, Vol. 14, 1105-1107. |
|
[4] | Gross L. J., Brent R., and Hoy R. 2004. Points of view: the interface of mathematics and biology. Cell Biology Education, Vol. 3, 85-92. |
|
[5] | Hillel J. C., Jeffrey M. and Kendrick M. S. 2010. From biology to mathematical models and back: teaching modeling to biology students, and biology to math and engineering students, CBE-Life Sciences Education Vol. 9(3), 248-265. |
|
[6] | White, C.J. 2005. Research: A Practical Guide. Pretoria: Ithuthuko Investment. |
|
[7] | Susan H, Sanlyn B, Lisa E and Lisa N. 2013. Integrating quantitative thinking into an introductory biology course improves students’ mathematical reasoning in biological contexts, CBE-Life Sciences Education Vol. 13, 54-64. |
|
[8] | Cambridge International Examinations, 2014. Syllabus Cambridge IGCSE Biology, 0610.University of Cambridge, London, UK. |
|
[9] | Curriculum Development Centre, 2013. Biology syllabus, Grade 10-12, Ministry of Education, Science, Vocational, Training and Early Education, Lusaka, Zambia. |
|
[10] | Lebata M.C. Mudau A.V. 2014. Exploring Factors Affecting Performance in Biology 5090 at Selected High Schools in Lesotho, Mediterranean Journal of Social Sciences, V5(8), pp 217-278. |
|
[11] | Mbugua Z. K, Komen K. Muthaa G. M and Nkonke G. R. 2012. Factors contributing to students’ poor performance in mathematics at Kenya certificate of secondary education in Kenya: a case of Baringo county, Kenya, American International Journal of Contemporary Research Vol. 2 No. 6; June 2012. |
|
[12] | Robeva, R and Laubenbacher R. 2009. Mathematical biology education: beyond calculus, Science, Vol. 325, Issue 5940, pp. 542-543. |
|