Location as a Factor in the Prediction of Performance in Botswana Junior School Certificate Agriculture Examinations by Continuous Assessment Scores
- 1 Department of Agricultural Economics, Education & Extension, Botswana College of Agriculture, Gaborone, Botswana.
- 2 Department of Curriculum & Instruction, Central China Normal University, Wuhan, China
- 3 Department of Educational Foundations, University of Botswana, Gaborone, Botswana
Abstract
The study looked at the location of schools as it relates to the academic performance of students in Botswana. The study population was the results of Junior Certificate (JC) Agriculture Examinations conducted in 2009 among 206 secondary schools in rural , peri-urban and urban areas of Botswana. One hypothesis was formulated and tested. Data were analysed using regression. When comparing three pairs (urban and peri-urban), (urban and rural) and (peri-urban and rural), the results show that the beta weights when transformed into z-values are not significantly different and are less than the critical values (1.96) given alpha level of 0 .05. The results showed no significant difference between students’ academic performance in rural, peri-urban and urban secondary schools in agriculture junior school certificate examinations. The study has proven that school location has no bearing on student academic performance in agriculture junior school certificate examinations in Botswana.
- Ajayi, I. A. (1998). Unit cost of secondary education and students’ academic achievement in Ondo State (1991-1995). Ph.D. Seminar Paper, Ibadan: University of Ibadan.
- Alokan, F. B. (2010). Influence of sex and location on relationship between student problems and academic performance. The Social Sciences (TSS), 5, 340-345. http://dx.doi.org/10.3923/sscience.2010.340.345
- Axtell, B. and Bowers, J. (1972). Rural urban effects on the common entrance examination. TEDRO RP, 104.
- Considine, G. and Zappala, G. (2002). The influence of social economic disadvantage in the academic performance of school students in Australia. Journal Sociology, 38, 127-148. http://dx.doi.org/10.1177/144078302128756543
- Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis. Practical Assessment Research & Evaluation, 10. http://pareonline.net/getvn.asp?v=10&n=7
- Friedman, M. (1962). Capitalism and freedom. Chicago: University of Chicago Press.
- Gana, E, S. (1997). Effects of using visual designed training models on the learning of mathematics at J.S.S. Ph.D. Thesis, Ibadan: University of Ibadan.
- Kostman, S. (1977). The EDC school partnership project and the school self-renewal project. A collaborative model between New York City high schools and the business community. High Points, 12, 55-57.
- Lawin, S. D. (1973). Environmental background and student learning behaviours. M.Ed. Thesis. Uyo: University of Uyo. http://www.informaworld.com
- Masole, T. M., & Utlwang, A. (2005). The reliability of forecast grades in predicting students’ performance in the final Botswana General Certificate of Secondary Education Examinations. http://www.freedocumentsearch.com/doc/cox-cei-4.html
- McGaw, B. (2006). Assessment fit for purpose. Singapore: The International Association for Educational Assessment. http://www.ascilite.org.au
- Mkpughe, M. L. (1998). The interaction of gender, location, and socio-economic status on students’ academic performance in home economics at the junior secondary school level. M. Ed Dissertation, Abraka-Nigeria: DELSU.
- Morgan, R. (1989). Analysis of the predictive of the SAT and high school grades from 1976 to 1985. http://professionals.collegeboard.com
- Obot, C. S. (1991). Influence of school factors and quality of education in Nigeria: A study of AkwaIbom State. M.Ed. Thesis. Uyo: University of Uyo. http://www.informaworld.com