2014. Vol.5, No.1, 11-14
Published Online January 2014 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2014.51004
Location as a Factor in the Prediction of Performance in Botswana
Junior School Certificate Agriculture Examinations by Continuous
Tapela Bulala1, Mogomotsi Ramatlala2, H. Johnson Nenty3
1Department of Agricultural Economics, Education & Extension, Botswana
College of Agriculture, Gaborone, Botswana.
2Department of Curriculum & Instruction, Central China Normal University, Wuhan, China
3Department of Educational Foundations, University of Botswana, Gaborone, Botswana
Email: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Received October 14th, 2013; revised November 14th, 2013; accepted November 21st, 2013
Copyright © 2014 Tapela Bulala et al. This is an open access article distributed under the Creative Commons
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The study looked at the location of schools as it relates to the academic performance of students in Bot-
swana. The study population was the results of Junior Certificate (JC) Agriculture Examinations con-
ducted in 2009 among 206 secondary schools in rural, peri-urban and urban areas of Botswana. One hy-
pothesis 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 per-
formance in rural, peri-urban and urban secondary schools in agriculture junior school certificate exami-
nations. The study has proven that school location has no bearing on student academic performance in ag-
riculture junior school certificate examinations in Botswana.
Keywords: School Location; Academic Performance; Junior Secondary School; Continuous Assessment;
JC Agricultural Examinations
One of the most topical debates in the education system is
the prediction of success in examinations. There is no certainty
if there are any predictors that accurately determine whether a
student will be an academic genius, a drop out, or an average
performer (Golding, 2006). The task to develop effective pre-
dictors of academic success is a critical issue for educators.
Conducting examinations within and at the end of each and
every school year is a part of the school curriculum in Bots-
wana and other countries; learners take public examinations to
determine their academic standing at each level of education
Ministry of Education (MOE) in collaboration with Botswa-
na Examination Council (BEC) and Department of Curriculum
and Evaluation has recommended school-based continuous
assessment (CA) for practical subjects, such as agriculture.
Schools assess practical activities carried out by students and
grade them for assessment of learning purposes (Ramatlala,
2009). At the end of three years at junior secondary schools, all
marks earned by students from practical continuous assessment
are combined with theory-based assessment to determine the
final grade. Thobega & Masole (2008) in their study on use of
forecast grades at Botswana General Certificate of Secondary
Education (BGCSE) found out that the mean mark for agricul-
ture practical at BGCSE was skewed towards the highest possi-
ble mark whereas for theoretical papers were around the me-
dian. They further said that this scenario puts the professional-
ism of teachers who are entrusted with the production of
coursework marks on the spotlight. Their assertion culminates
into skepticism on the validity, reliability, and monitoring of
the school-based examinations. McGaw (2006) also unders-
cored the importance of reliability and validity of examinations
irrespective of the type, format or even purpose of the examina-
tion during his work on assessment fit for a purpose study.
According to Mkpugbe (1998), on whether the location of a
school influences performance in mathematics tests, the result
showed that students from rural schools against all odds per-
formed higher than those from urban schools. The results are in
consonance with those of Simmelkjaer (1979), Friedman (1962)
and Kostman (1977) who reported that urban schools shared
common features of learning impediments, such as reading
retardations, high absenteeism, drug abuse, students vandalism,
apathy and overcrowding which have manifested in their poor
performance. The results ran parallel with those of Ogunlade
(1973), Lawin (1973), Obot (1991), and Ajayi (1988) in which
they found that students from urban schools performed better
than those from rural schools. Their reasons were that urban
schools are better staffed, with better facilities, so students are
T. Bulala ET AL.
exposed to good study habits, and highly motivated to study
with conducive learning environment; hence these factors en-
courage the students from urban schools to perform better than
those from rural schools. Young (2001) concluded that rural
students perform less well than urban students on standardized
tests of educational achievement. Mkpugbe (1998) noted that
different aspects of school environment influence students’
achievement. She further stated that the individual students’
academic behavior is influenced not only by the motivating
forces of his home, scholastic ability, and academic values, but
also by the social pressure applied by the participants in the
school setting. In Botswana, most rural-based schools which
lack enough qualified teachers, are poorly equipped and lack
basic amenities and all these serve as inhibiting factors of good
academic performance. Combining school location and other
variables on science performance may or may not produce the
desired result. This study therefore seeks to evaluate the effect
of gender, socio-economic status and school location on per-
formance in integrated science. This finding was earlier on
elaborated by Okunrontifa (1973) who observed that most stu-
dents living in rural environments of Nigeria have significantly
lower entry behaviour than their urban centered counterparts. In
his/her study, he exhibited significant differences in the aca-
demic performance and research involvement of students com-
ing from urban and rural backgrounds. Students from highly
urbanized background reported the best academic indicators,
while students from rural and remote backgrounds reported the
poorest. He opined that this could be influenced by the poor class
attendance due to students travelling long distances.
However, Axtel and Bowers (1972) in their findings reported
that students from the rural areas perform significantly better
than their urban counterparts in verbal aptitude, English Lan-
guage and total score using the National Common Entrance as a
baseline. In another development, a research team at University
of Aston recorded that it had received several well-founded re-
ports that secondary schools have found pupils from small rural
schools not only as well prepared academically as pupils from
other schools, but they generally had a better attitude to work.
Having been accustomed to working most of the time on their
own, they could be given more responsibility for the organiza-
tion of their work. Size could not exert significant direct effect
on pupils’ attitude towards science. Similar view was expressed
by Gana (1997) in his study on the effect of using designed
visual teaching models on the learning of mathematics at Junior
Secondary level of Niger Stat e, who found out that there was no
significant difference in mathematics achievement scores of
students in urban and rural locations.
From the various reviews of literature on locational influence
on academic performance, it has been observed that the find-
ings are not the same. While some maintain that urban students
perform better in examinations than their rural counterparts,
other studies have found that rural students (in spite of all odds)
perform better. Some have submitted in their findings and con-
cluded that no particular setup (urban or rural) can claim supe-
riority over the other because their performances are the same.
Alokan (2010) found out that students’ problems are strongly
associated with poor performance and that sex and location do
not affect the negative relationship between student problems
and academic performance. In another development, Considine
and Zappala (2002) studied students in Australia and found out
that geographical location does not significantly predict out-
comes in school performance. Shield and Dockrell (2008) while
looking at the effects of classroom and environmental noise on
children’s academic performance, found out that both chronic
and acute exposure to environmental and classroom noise have
a detrimental effect upon children’s learning and performance.
Statement of the Problem
Morgan (1989) used the term predictive validity to refer to
the extent to which achievement tests can be appropriately used
to draw inferences regarding students' future performance.
There are numerous purposes of assessing students in agricul-
ture education, both in theory and in practical. Continuous as-
sessment can be motivational in that it motivates students to
study and practice hard. Students tend to study and practice
hard when they are told well in advance that they will be as-
sessed in the near future. Results of assessment do not only
help students on how much to prepare themselves, but also
provide useful feedback information on pupils’ strength and
weakness in different practical activities and also in different
areas of the subject. This information is useful to teachers, par-
ents and students themselves.Most students record high CA
scores, but then perform badly in JC examinations. Given that
CA assesses practical skills that are generated from the same
specific objectives as theory it would be expected that it should
enhance learning and as such be reflected with good overall
examination performance.In view of these inconclusive find-
ings, it is necessary to carry out further research to confirm or
annul the otherwise protracted issue on the effect of location
(urban, peri-urban and rural) as a factor in the prediction of
performance in Botswana Junior School Certificate Agriculture
examinations using continuous assessment scores.
Purpose and Objectives
The purpose of the study was to determine whether agricul-
ture CA scores predict academic performance in JSCE and
equally so for urban, peri-urban and rural schools. Some earlier
studies (Thobega & Masole, 2008; Masole & Utlwang, 2005)
have revealed coursework and forecast grades to be good pre-
dictors of BGCSE grades and in agriculture grades, but none
have been found trying to determine location-based predictive
validity. Hence this study aims at investigating the predictive
strength of these two variables on JC agricultural examinations.
Botswana being a developing country has schools spread in
rural and urban locations hence the study specifically aims at
determining the extent to which school location influences the
prediction of students’ performance in JC agricultural examina-
tion by CA.
Hence more specifically the objective of this study is to:
• Determine the extent to which school location has influ-
ence on the ability of CA scores to predict performance in JC
In the null form it states that:
• Location of school does not significantly influence the
ability of CA scores to predict performance in JC agricultural
The study population comprised all presented candidates for
T. Bulala ET AL.
the 206 junior secondary schools which are spread across the
country for the year 2009 Junior Secondary Certificate (JC)
examinations in Botswana. The total population under study
was 38101 students and using Comrey and Lee (1992) sample
size determination method a sample of 1506 comprising of 614
rural students, 340 urban and 552 peri-urban were randomly
Secondary data were used for the study, and were retrieved
with permission from Botswana Examination Council (BEC)
academic records. To ensure validity of the scores used in the
grading, BEC carries out intensive panel-based content analysis
and face validation; the scores were therefore deemed valid.
Data were coded and entered into the computer and analysis
was done by carrying out regression analyses. All the analyses
were done using SPSS version 16 for Windows. The prediction
model for CA scores was determined by fitting the values of the
relevant parameters in the linear regression model. The predic-
tor variable in the regression analysis was the students’ CA and
JC examination as the criterion variable. The significance level
for testing the hypotheses set at .05 for all statistical tests.
Data Analysis an d Interpretation of Results
In testing the hypothesis, data on scores obtained by students
(from Urban, peri-urban and rural) for CA and JCE collected
from Botswana Examination Council were used in a regression
and the res ults were as pr esented in Table 1. The Zβ values of the
analysis for urban, peri-urban and rural were 0.830, 0.829 and
0.820 respectively. When comparing the three pairs, the results
show that the pairs of beta weights for (urban and peri-urban),
(urban and rural) and (peri-urban and rural) when transformed
into z-values are not significant. The Zβ values for rural, pe-
ri-urban and urban are 1 stand ard deviation above the mean score
and are less than the critical values (1.96) given alpha level of
0.05. Hence it was concluded that location of school has no
significant influence on the ability of CA scores to predict per-
formance in agriculture education JC examinations and the null
hypothesis was re t ai ned.
Discussion of Findings and Conclusions
Table 1 showed that school location has no significant in-
fluence on the ability of CA scores to predict academic perfor-
mance of students in agriculture junior school certificate ex-
aminations in Botswana.
The above results support earlier findings by Gana (1997),
Location influence on the ability of CA scores to predict JCE perfor-
mance in agriculture education.
Location of Scho ol β-weight Zβ z-value
Urban 0.681 0.830 0.014
Per-urban 0.680 0.830 0.001
Rural 0.673 0.820 0.001
Considine and Zappal (2002), and Alokan (2010), among oth-
ers that academic performance of students in rural community
does not differ from those in urban locations. In his own con-
tribution on rural/urban differences, Gana (1997) in his study
on the effect of using designed visual teaching models on the
learning of mathematics at Junior Secondary level of Niger
State, Nigeria, found out that there was no significant differ-
ence in mathematics achievement scores of students in urban
and rural locations.
The findings of current study could be explained by the fact
that in Botswana, majority of students in urban schools are ori-
ginally from rural areas, but happen to be in town because their
parents are working in urban centres. This means that they are
able to carry their agricultural experience from ru ral areas where
farming is mostly practiced in their daily teachings hence the
balance in performance.
It is recommended that the government of Botswana should
maintain the status quo between the rural and urban locations
by providing the rural dwellers the social amenities which will
enhance academic performance and reduce transfers of students
from rural to urban schools as this could result in class conges-
tions as well as lack of land for agricultural projects in town
There is also a need to conduct studies using continuous as-
sessment and other variables in predicting JC examination
grades in different subjects.
Ajayi, I. A. (1 9 9 8 ). Unit cost of secondary education and students’ aca-
demic 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 be-
tween student problems and academic performance. The Social Sci-
ences (TSS), 5, 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 eco-
nomic disadvantage in the academic performance of school students
in Australia. Journal Sociology, 38, 127-148.
Costello, A. B., & Osb orne, J. W. (2005). Best p ractices in exploratory
factor analysis. Practical Assessment Research & Evaluation, 10.
Friedman, M. (1962). Capitalism and freedom. Chicago: University of
Gana, E, S. (199 7). Effects of using visual designed training models on
the learning of mathematics at J.S.S. Ph.D. Thesis, Ibadan: Univer-
sity of Ibadan.
Kostman, S. (1977). The EDC school partnership project and the school
self-renewal project. A collaborativ e model between New York City
high schools and t he business communi ty. High Points, 12, 55-57.
Lawin, S. D. (1973). Environmental background and student learning
behaviours. M.Ed. Thesis. Uyo: University of Uyo .
Masole, T. M ., & Utlwan g, A. (2 005). The reliab ility of forecas t grad es
in predicting students’ performance in the final Botswana General
Certificate of Secondary Education Examinations.
McGaw, B. (2006). Assessment fit for purpose. Singapore: The Inter-
national Association for E ducational Assessment.
T. Bulala ET AL.
Mkpughe, M. L. (1998). The interaction of gender, location, and so-
cio-economic status on students’ academic performance in home eco-
nomics at the junior second ary school level. M. Ed Dissertation, A bra -
Morgan, R. (1989). Analysis of the predictive of the SAT and high school
grades from 1976 to 1985.
Obot, C. S. (1991). Influ ence of schoo l factors and quality of education
in Nigeria: A study of AkwaIbom State. M.Ed. Thesis. Uyo: Univer-
sity of Uyo. http://www.informaworld.com
Okunrofia, P. O. (1973). Social class differences an d Nigerian children
entry behaviour in geography. M.Ed. Thesis. Ibadan: University of
Ogunlade, J. O. (1973). Environmental effects and students perform-
ance—Urban cities in Western Nigeria. Senior Research Project,
Ibadan: University of Ibadan.
Ramatlala, M. S. (2009). The validity of coursework scores in predict-
ing performance in Botswana Genera l Certificate of Se condary Edu-
cation Physical Education examinations among senior secondary
school students in Botswana. Master’s Thesis, Gaborone: University
Ramatlala, M. S., & Nenty, H. J. (2012). Gender as a factor in the pre-
diction of performance in Botswana general certificate of secondary
education physical education examinations by coursework and fore-
cast grades among senior secondary school students. Scientific Re-
search Journal, 3, 32-37.
Republic of Botswana. (2008) . Botswana examination council.
Thobega, M., & Masole, T. M. (2008). Relationship between forecast
grades and component scores of the Botswana General Certificate of
Secondary Education Agricultur e.
Shield, B., & Dockrell, J. (2008). The Effects of classroom and envi-
ronmental noise on children’s academic performance. 9th Interna-
tional Congress on Noise as a Public Health Problem (ICBEN), Fox-
Simmelkjaer, R. T. (1979). Evolution of urban educational reform. The
Educational Forum. The Psychology of Human Differences. New York:
Young, J. W. (2001). Differential validity, differential prediction, and
college ad mission testing: A comprehensive review and analysis. Col-
lege Board Research Report No. 2001-6. New York: College En-
trance Examination Board.