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URBAN AND RURAL STUDENTS’ ACADEMIC ACHIEVEMENT AND INTEREST IN GEOMETRY: A CASE-STUDY WITH GAMES AND SIMULATIONS METHOD

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Abstract

Games and simulations method was used to address the rural-urban difference in achievement and interest of students in Benue State of Nigeria. Two research questions and hypotheses guided the study. The sample is made up of 70 urban and 59 rural students. One group pre-test post-test design was used on intact classes. Data were generated using GAT and GII. GAT is multiple-choice 20 items with four options while GII is a 20 item Likert-rating scale with five options. Internal consistency reliability index of 0.80 for GAT was established using Kuder-Richardson (KR-20), while Cronbach Alpha was used to estimate the GII internal coefficient reliability of 0.90. Mean and standard deviations were used to answer all the research questions while t-test was used to test the hypotheses at .05 level of significance. The study revealed that rural students achieved significantly better in mean achievement and interest scores than those in urban schools post treatment. These findings showed that rural students suffer disadvantage not as a result of their attendance at rural schools but non-usage of effective methods of teaching. The study’s findings show that games and simulations in teaching mathematics concepts can be used to facilitate meaningful learning in rural schools.
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URBAN AND RURAL STUDENTS’ ACADEMIC ACHIEVEMENT AND INTEREST IN GEOMETRY: A
CASE-STUDY WITH GAMES AND SIMULATIONS METHOD
By
Ajai, John T1* and Imoko, Benjamin I.2
1. Dept. of Science education, Faculty of Education, Taraba State University, Jalingo. Nigeria
2. Dept. Of Curriculum and Teaching, Faculty of Education, Benue State University, Makurdi. Nigeria
* jtajai@gmail.com
Abstract
Games and simulations method was used to address the rural-urban difference in achievement and interest of
students in Benue State of Nigeria. Two research questions and hypotheses guided the study. The sample is made up
of 70 urban and 59 rural students. One group pre-test post-test design was used on intact classes. Data were
generated using GAT and GII. GAT is multiple-choice 20 items with four options while GII is a 20 item Likert-rating
scale with five options. Internal consistency reliability index of 0.80 for GAT was established using Kuder-
Richardson (KR-20), while Cronbach Alpha was used to estimate the GII internal coefficient reliability of 0.90.
Mean and standard deviations were used to answer all the research questions while t-test was used to test the
hypotheses at .05 level of significance. The study revealed that rural students achieved significantly better in mean
achievement and interest scores than those in urban schools post treatment. These findings showed that rural
students suffer disadvantage not as a result of their attendance at rural schools but non-usage of effective methods
of teaching. The study’s findings show that games and simulations in teaching mathematics concepts can be used to
facilitate meaningful learning in rural schools.
Key words: Games and simulations, student’s achievement, student’s interest, geometry, rural, urban.
Introduction
There is a general perception that
rural schools are inferior to the urban schools.
This perception goes further to imply that
there are rural-urban differences in students’
achievement levels. These rural-urban
differences in academic achievement extend
to many other socially desirable outcomes
such as aptitude, intelligence, interest and
aspiration. This concern about potential rural-
urban differences in academic achievement
appears to be global and has become a topic
of debate among researchers
The conjecture that students in rural
areas receive an inferior education compared
to their urban counterparts can be described
as ‘deficit model’ of rural community and life
style (Fan & Chen, 1999). A few factors may
be considered as potential reasons for the
reported rural-urban differences in students’
academic achievement. These include family
characteristics (Ramos, Duque & Nieto,
2012), the availability of resources and
technology, differences in socio-economic
status, and quality of teachers (Gaviria &
Barrientos, 2001; Brown and Swanson, 2001;
Rangel & Lleras, 2010). Suzanne and Lauren
(2012) are of the view that rural schools do
not always have access to the same level of
federal funding as urban and suburban
schools and this can limit the opportunity
students have for learning mathematics.
Although rural areas differ from urban areas
in many ways, it is not easy to define the
differences so that they fit every case.
Sometimes problems associated with rural-
urban students’ achievement are partly
pedagogical and partly due to the
environment in which the teaching and
learning take place. More so, there are times
when these problems interact, hence the need
to investigate them simultaneously
Researchers have compared rural
students with students from urban schools on
several major areas of academic achievement,
including geography (Obasi, 2011), television
technology (Umunadi, 2009), mathematics,
science and reading (Ramos, Duque & Nieto,
2012; Ijenkeli, Paul, & Vershima, 2012;
Reeves, n.d; Chianson, 2012), level of stress
(Tajularipin, Aminuddin, Vizata & Saifuddin,
2009) and senior school certificate
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examination (Adepoju & Oluchukwu, 2011).
Although studies examining school’s location
(urban or rural) on students’ educational
outcomes started in the mid 80s in the United
States, there appears to be no consensus on
the significance of this characteristic (Ramos,
Duque & Nieto 2012). Some studies debunk
the urban-rural influence on students’
performance (Yusuf & Adigun, 2010; Jahun
& Momoh, 2001; Uka, 2006). Other studies
have found that students in urban areas
exhibit better performance than their rural
counterparts in mathematics, reading, and
science (Owoeye & Yara, 2011; O’kwu 2008;
Chianson, 2012; Ijenkeli, Paul & Vershima,
2012). In other studies, however, students
from rural schools were found to have
performed better than those from urban areas
(Alspaugh, 1992; Alspaugh & Harting, 1995;
Haller, Monk, & Tien, 1993).
Hu (2003) argues that the rural
schools face challenges that can lead to
unfavourable educational outcomes for their
students. One of such challenges as pointed
out by Ertl and Plante (2004) is in terms of
information and communication technology
(ICT) usage, which is usually lacking in rural
areas. Nielson (2004) observes that rural
students are advantaged by small class size
and enjoy more individual attention from
teachers than their urban counterparts.
Differences between rural and urban schools
in terms of the availability of resources
(books, computers, art and science supplies,
course offerings, and adequately heated or
cooled buildings) have been considered by
many researchers as one potential contributor
to the perceived or in some cases, observed
rural-urban differences in education
outcomes (Caplan, 1995; DeYoung &
Lawrence, 1995; Haller et al., 1993; Herzog
Pittman, 1995; Howley, 1996; McLean &
Ross, 1994; Owens & Waxman, 1996). The
availability of fewer resources in many rural
schools than those in urban areas have been
found to contribute to a more limited
curricula being made available at these rural
schools (DeYoung & Lawrence, 1995; Hall
& Barker, 1995; Haller et al., 1993).
Owoeye and Yara (2011) attributed
the urban-rural difference in achievement of
students to factors such as an uneven
distribution of resources, and lack of
functional facilities. Some qualified teachers
are not willing to take up teaching
appointment; those already in the system are
not willing to put in their best in rural
schools, for lack of motivation for teachers.
There are some indications that socio-
economic status is another factor in any rural-
urban difference in educational outcomes.
Socio-economic status has been shown to be
positively related to students' academic
achievement (Coladarci & Cobb, 1996), and
there is a perceived difference between rural
students and their urban counterparts in this
aspect, with rural students usually having
lower socio-economic status. However, the
role socio-economic status plays in
differences in students' academic
achievement may be less important in rural
schools than in urban schools.
Alspaugh (1992) observed that a
large proportion of the between-school
variance in school achievement among urban
schools was associated with the students'
socio-economic status, while a smaller
proportion of the between-school variance in
school achievement among rural schools is
associated with the students' socio-economic
status. Community and parental involvement
have also been shown to be positively related
to student school achievement and
subsequent career choices (Alspaugh &
Harting, 1995; Ramos & Sanchez, 1995;
Rutherford & Billig, 1995). As viewed by
some researchers, rural students may be at
some disadvantage compared with their urban
counterparts in these respects, because small,
isolated, and low-socio-economic rural
communities often have less community
involvement in education (DeYoung &
Lawrence, 1995).
Students are usually attentive toward
certain subjects or topics that appeal to them
but show apathy towards other ones that they
do not fancy. This degree of attentiveness or
apathy goes a long way in shaping their level
of participation and achievement in the
subject matter (Nnaka & Anaekwe, 2005).
Students’ performance in school subjects is
influenced by intellective and non-intellective
variables. One of such non-intellective
variables is interest. Interest is a
psychological factor that has the tendency to
make or mar students’ participation and
achievement in mathematics. Describing
interest as a person’s likes or dislikes, Nnaka
and Anaekwe (2005) stressed that when a
learner has extreme likeness for an activity,
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object and event, he interacts with it more
frequently; otherwise, he withdraws from it
or has as little interaction with it as possible.
Interest in mathematics could be evident from
the way a student is punctual in class, paying
attention in class work, assignments and tests,
as well as taking delight in observing and
exploring the environment.
Research findings have shown that
students poor performances results from lack
of interest (Amoo, 2002; Uhumuavbi &
Umoru, 2005; Imoko & Agwagah, 2006).
Interest plays a crucial role in the teaching
and learning process. At the classroom level
and beyond, learning can be meaningfully
achieved within the context of optimal
disposition of the learner to the tasks in
question. This means that the learner needs to
be psychologically present and attentive
towards the learning tasks. Situations often
arise in which the learner may be physically
present but psychologically absent in the
classroom. Students that are interested in
mathematics are likely to perform better in
mathematics.
It is interesting to note from the
literature review that, like many other issues
in education, research comparing rural
students with their urban counterparts in
academic achievement has yielded
inconsistent findings. These divergent views
are always supported with seemingly
convincing reasons. Because rural-urban
differences in cultural, economic and political
conditions may differ from one country to
another, findings from a study, on rural-urban
difference, may not necessarily be
generalizable from one country context to
another. There is no doubt that disparity in
mathematics achievement, in terms of school
location exists, but Etukudo (2002)
emphasized that differences in mathematics
achievement can exist only in the face of
weak instructional methods. The need for a
good instructional delivery process that could
balance the urban-rural inequality in
mathematics achievement and interest cannot,
therefore, be over-emphasized. The present
study used games and simulations as a
pedagogical variable to investigate rural-
urban disparity in the achievement and
interest of students. The specific aims of this
study are:
1. To ascertain whether there is
difference in the mean achievement
scores of urban and rural students in
geometry when taught using games
and simulations.
2. To ascertain if there is difference in
the mean interest scores of urban
and rural students in geometry when
taught using games and simulations.
Research questions and hypotheses
The following research questions and
hypotheses guided the study:
RQ1: What is the difference in the mean
achievement scores of urban and rural
students taught geometry using games and
simulations?
RQ2: What is the difference in the mean
interest scores of urban and rural students
when taught geometry using games and
simulations?
Ho1: There is no significant difference in the
mean achievement scores of students in urban
and rural schools when taught geometry
using games and simulations.
Ho2: There is no significant difference in the
mean interest scores of urban and rural
students taught geometry using games and
simulations.
Materials and Method
The study adopted one group pre-test
post-test design. The sample is made up of 70
students from two urban schools and 59
students from two rural schools of Makurdi
and Gwer-West local government areas
(respectively) of Benue State, Nigeria. Two
researcher-made instruments (Geometry
Achievement Test (GAT) and Geometry
Interest Inventory (GII) were used to collect
data for this study. GAT is a 20 item
multiple-choice with four options (A, B, C,
and D) while GII is a 20 item Likert-rating
scale with five options (SA, A, U, D and SD).
GAT was validated and Kuder-Richardson
(KR-20) was used to estimate the internal
consistency reliability index of 0.80, while
Cronbach Alpha was used to estimate the GII
internal coefficient reliability of 0.90.
The students were taught by
research assistants trained by the researcher
for one week before the commencement of
the experiment. The research assistants
played a supervisory role, while the games
and simulations lasted. To ensure uniformity
in implementation, a researcher-prepared
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methodology instructional package (MIP)
which was given to the research assistants.
The researcher also monitored
implementation by going round the schools
in the course of the treatment so as to ensure
that research assistants did not deviate from
the instructional materials provided and the
participating students’ exercise books were
checked at regular intervals.
The game itself, Mathematical
Palace Game, was adapted from the resource
materials of the National Mathematical
Centre (2008).The rules and mechanics of the
game were developed by modifying the
format of National Mathematical Centre
Abuja, Nigeria (2008) and Agwagah (2001).
The rules and mechanics were explained to
the students after which they competed
against one another. The decision to develop
different rules and techniques for the games
was informed by the need to reflect the
peculiarities of geometry and level of
students involved.
The pretest was used to ascertain the
level of students’ interest and achievement in
geometry prior to the treatment. The posttest
was used to determine the extent of students’
achievement and interest in geometry after
the experiment. Pre-test and post-test items
were the same in content but different in
organization, that is, the numbering shuffled.
Both the teaching units and test
administration took place simultaneously.
Data analysis was done by using the
descriptive statistics of mean and standard
deviations to answer research questions while
t-test was used to test the hypotheses.
Results
Table 1: Levene’s test for equality of variances between urban and rural schools
Type of test df F Sig Decision
at p <.05
Pre-GAT 127 1.482 .226 NS
Pre-GII 127 2.049 .155 NS
Results on Table 1 show that the pre-test
mean scores of the urban students were not
significantly different statistically from those of the
rural students. This confirms that the groups were on
equal strength before the treatment
Table 2: Mean achievement and standard deviation scores of students in urban and rural schools
Location N Pre-GAT Post-GAT Mean
𝑿
̅ δ 𝑿
̅ δ Difference
Urban 70 29.91 7.97 46.03 12.84 16.12
Rural 59 29.39 9.55 52.12 10.98 22.73
Mean Difference 0.52 6.09
Legend: N=Number of Students; 𝑿
̅ = Mean Scores; δ = Standard Deviation Scores
Results on Table 2 reveal that urban students had
post-test mean achievement (GAT) scores of 46.03
and standard deviations of 12.84; while the rural
students had post-test mean achievement (GAT)
scores of 52.12 with standard deviations of 10.98.
The pre-test post-test difference for the urban
students is 16.12 while that of the rural students is
22.73. The post-test mean difference between the
urban and rural students is 6.09.
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Table 3: Mean Interest and standard deviation scores of students in urban and rural schools
Location N Pre-GII Post-GII Mean
𝑿
̅ δ 𝑿
̅ δ Difference
Urban 70 48.20 12.76 57.57 13.56 11.01
Rural 59 46.56 11.23 64.02 10.54 15.82
Mean Difference 1.64 6.45 4.81
Results on Table 3 reveal that the urban students had
post-test mean interest (GII) scores of 57.57 and
standard deviations of 13.56; while the rural students
had post-test mean interest (GII) scores of 64.02 with
standard deviations of 10.54. The pre-test and post-
test mean interest difference for the urban students is
11.01 and 15.82 for rural students. The post-test
mean interest score difference between the urban and
rural students is 6.45 in favour of the rural students.
Table 4: Independent t-test analysis of significance between the achievement of rural and urban students
Location N 𝑿
̅ 𝜹 df tcal tcrit Eta Squared
Urban 70 46.03 12.84
127 2.9038 1.960 .062
Rural 59 52.12 10.98
Results on Table 4 reveal that there is significant
difference between the mean achievement scores of
rural and urban students taught geometry using
games and simulations. This is because the calculated
t-value of 2.9038 is greater than the critical t-value of
1.96 at .05 level of significance with 127 degrees of
freedom. The effect size (eta squared =.062) shows
that 6.2% of the difference is explained by the
location of students. Thus, hypothesis 1 of no
significant difference in the rural and urban students’
achievement is not retained.
Table 5: Independent t-test analysis of significance between the interest of rural and urban students
Location N 𝑿
̅ 𝜹 df tcal tcrit Eta Squared
Urban 70 57.57 13.56 127 3.037 1.960 .067
Rural 59 64.02 10.54
From Table 5 it can be seen that there is
significant difference between the interest scores of
urban and rural students in mathematics, at .05 level
of significance with 127 degrees of freedom. The
calculated t-value of 3.037 is greater than the critical
t-value. The effect size indicates that 6.7% of the
difference is because of the location. This means that
hypothesis 2 of no significant difference between the
interest of urban and rural students is not retained.
Discussion of Results
The finding that the urban and rural groups
were on equal strength before the treatment is
necessary to show that the differences noticed after
the treatment would not be attributed to chance but to
the effect of method of instruction. The posttest mean
achievement and interest scores of the rural and
urban students were found to be significantly
different. This finding revealed that the rural students
gained both in achievement interest scores more than
their urban counterparts. This contradicts earlier
studies (Owoeye & Yara 2011; O’kwu 2008;
Chianson, 2012; Ijenkeli, Paul & Vershima, 2012)
which assert that urban students achieve more than
rural students. The findings corroborate earlier
findings (Alspaugh, 1992; Alspaugh & Harting,
1995; Haller et al., 1993) that rural students’
achievement in mathematics is higher than their
urban counterparts.
In the course of the experiment, it was
observed that the rural students paid more attention to
instructions and the game than did the urban students.
The better achievement recorded by the rural students
may not be unconnected with this fact. The rural
students may have seen the mathematical palace
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game as a novelty, more compelling because they are
less exposed to such resources, in mathematics
classroom and paid more attention. The urban
students may not have seen anything so spectacular
with the games and simulations. It is also possible
that the urban students may have thought that the
games and simulations was mere play, without any
educational implications. The finding of the study on
interest revealed that there is significant difference
between the interest of urban and rural students in
geometry in favour of the rural students.
Conclusion and Recommendations
The results in this study provide empirical
evidence that achievement and interest in geometry
depend on the method of instruction adopted and are
not influenced by location or social environment.
These results do not support the conjecture that
students in rural schools are at general disadvantage
in terms of quality of their education, as reflected in
their achievement and interest tests. The implications
of these findings for future teaching and learning of
mathematics is that, it is one thing to teach
mathematics using a facilitative method; it is another
to teach with a method that is interesting,
participatory and concretized with rules and
procedures that are well documented. It may be
appropriate to say that the use of games and
simulations appeal to students who are concrete
operators and those who shy away from participating
actively in mathematics lessons for whatever reason.
This study therefore recommends that adequate
incentives should be provided to rural school teachers
to encourage them put in their best and use methods
of teaching that would promote students’ interest in
the learning of mathematics. In addition, mathematics
teacher should endeavour to constantly expose
students to various games and simulations situations
that are related to mathematics concepts taught in the
classroom. School administrators should provide
local games such as lido, playing cards, whot, etc to
facilitate meaningful learning in their schools. This
will enable the teachers to have access to them for
better delivery of their lessons. Teacher training
institutions (colleges of education, faculties/schools
of education in the universities) should provide
would-be teachers with enough opportunities to
master the principles behind the use of games and
simulations and how to develop them. This will
ensure the training of pre-service mathematics
teachers to use games and simulations technique.
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... According to Ge and Wang (2019), there is a wide gap between rural and urban schools regarding education performance. Some factors that are considered the potential reasons urban students differ from rural students include family, availability of resources and technology, socioeconomic status, and teachers' quality (Ajai & Imoko, 2013). Young (1998) states that urban schools are equipped with better facilities and resources than schools in rural areas. ...
... It has become common to think that rural schools are considered inferior to urban schools (Fan & Chen, 1999;Ajai & Imoko, 2013;Alokan & Arijesuyo, 2013;Faisal et al., 2016). Rural schools typically lack facilities compared to urban schools, and rural students rarely go to private courses. ...
... The results of this study also confirm the previous findings by Haller et al. (1993), Fan and Chen (1999), and Ajai and Imoko (2013), who also highlight the excellence of the rural schools' performance compared to the urban schools' performance. This study has shown that rural students have no disadvantages in terms of the quality of education, confirming the research findings by Fan and Chen (1999) and Alokan and Arijesuyo (2013). ...
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This study aimed to investigate the difference in rural-urban students’ critical thinking in biology class across gender. It involved 289 students from public and private senior high schools. Essay tests were administered to measure the students‘ critical thinking skills. The study results showed that female students outperformed male students in critical thinking. Meanwhile, the rural students demonstrated better critical thinking skills than urban students.
... Some studies have found that rural students have a tendency to have a lower achievement (Jayarani, 2019;Pangeni, 2014;Khanal, 2015& Nepal, 2016 Anjana, 2018). Similarly, the socio-economic condition, educationalbackground and language backgrounds have a direct effect on the students' learning achievements, however, there was no significant difference in the achievements of girls and boys in the urban and rural school students in mathematics (Rijal et al., 2018) while others have found that rural students perform better in mathematics at secondary level than the urban students (Ajai & Imoko, 2013;Kayla, 2018;& Pokherel, 2018). Likewise, Ajai & Imoko (2013) found that rural students had a more positive family environment than urban students and that those from positive family environments performed better academically based on examination scores. ...
... Similarly, the socio-economic condition, educationalbackground and language backgrounds have a direct effect on the students' learning achievements, however, there was no significant difference in the achievements of girls and boys in the urban and rural school students in mathematics (Rijal et al., 2018) while others have found that rural students perform better in mathematics at secondary level than the urban students (Ajai & Imoko, 2013;Kayla, 2018;& Pokherel, 2018). Likewise, Ajai & Imoko (2013) found that rural students had a more positive family environment than urban students and that those from positive family environments performed better academically based on examination scores. Again, Ntibi and Edoho (2017) statistically the same. ...
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The study entitled "Exploring Students’ Attitudes, Learning Behaviors, and their effects on Mathematics Achievements" basically aims to analyse the grade X students' attitudes levels; attitudes influence on the creation of learning behaviors, and ultimate effects on students' achievements in mathematics and establish their relationships as one of the achievement models. Bandura's social cognitive theory and Bem's self-perception theory are major theoretical referents for this study. The study has employed a concurrent embedded mixed-method research design with a sample of 540 grade X students from 12 community schools in Nepal. The quantitative data were collected using attitude towards mathematics inventory, classroom learning behavior self-assessment inventory, and mathematics achievement test, and analyzed using statistical tools such as mean, standard deviation, correlation, and regression. The qualitative data related to learning behavior was collected through class observation and semi-structured interviews. The qualitative information was analysed thematically for drawing the categories and embedded with the results of the quantitative data iv while analyzing and interpreting. As results of the study, most of the students' levels of attitudes and learning behaviors were positive whereas the achievement level of the students was found medium and differed between ecological regions and rural-urban backgrounds. The result refutes that rural student lagged behind their urban counterparts in achievement, and genderwise achievement difference was statistically insignificant. The majority of the students preferred learning mathematics by using more behaviorist attributes and credited the teacher for their success. Overall, the effects of the students'attitudes and learning behaviors on achievements were found positive and statistically significant. The positive correlations between attitudes, learning behaviors, and achievements, suggest that a positive attitude towards mathematics causes positive learning behaviors leading to higher achievements and vice-versa.
... Likewise, Umar (2017) reported significant difference between rural and urban students' academic performances in biology in West African Examination Council with students in urban schools performing better than those in rural schools. In contrast to the result of the present study, Ajai and Imoko (2013) in their study on urban and rural differences in academic achievement and interest in geometry among students in Benue State, Nigeria reported that rural students achieved significantly better in mean achievement and interest scores than those in urban schools. ...
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The study determined the level of scientific literacy among basic science students in junior secondary schools in Nsukka L.G.A. A descriptive survey research design was adopted for the study. A total of 244 JSS 11 students (129 males & 115 females; 160 urban & 84 rural) were sampled for the study. Scientific Literacy Test (SLS) was used with a reliability coefficient of 0.60 using Kuder-Richardson (K-R-20). Data were analyzed using frequency and percentages and chi-square. The results of the study showed significant differences in the level of scientific literacy possessed among junior secondary school students in the area with majority (76.8%) of the students possessing nominal level of scientific literacy among others. It is recommended among others that there is need to develop a curriculum of basic science that will be geared towards the inculcation of the higher levels of scientific literacy.
... Likewise, Umar (2017) reported significant difference between rural and urban students' academic performances in biology in West African Examination Council with students in urban schools performing better than those in rural schools. In contrast to the result of the present study, Ajai and Imoko (2013) in their study on urban and rural differences in academic achievement and interest in geometry among students in Benue State, Nigeria reported that rural students achieved significantly better in mean achievement and interest scores than those in urban schools. ...
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The study determined the level of scientific literacy among basic science students in junior secondary schools in Nsukka L.G.A. A descriptive survey research design was adopted for the study. A total of 244 JSS 11 students (129 males & 115 females; 160 urban & 84 rural) were sampled for the study. Scientific Literacy Test (SLS) was used with a reliability coefficient of 0.60 using Kuder-Richardson (K-R-20). Data were analyzed using frequency and percentages and chi-square. The results of the study showed significant differences in the level of scientific literacy possessed among junior secondary school students in the area with majority (76.8%) of the students possessing nominal level of scientific literacy among others. It is recommended among others that there is need to develop a curriculum of basic science that will be geared towards the inculcation of the higher levels of scientific literacy.
... The previous educational experience contributes to self-regulation in learning (Phan, 2011) and to carrying out activities (Irdianto & Putra, 2016). Seventh, the place of students' origin is also one of the factors to be measured, it is supported by the results of research that showed students from rural areas do not experience lagging in academic performance (Alokan & Arijesuyo, 2013), they are even significantly better at achieving average and interest scores than in the urban schools (Ajai, 2013). ...
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This study aims at identifying the factors affecting students' self-regulation. It is seen from Gender, Age, and Duration of study in the Islamic Boarding Schools, Formal Education, Parental Education, Previous Education, and Students Place of Origin. It is to find a new format for the self-regulation of students in traditional Muslim schools. This study applies a quantitative approach to identify factors or variables that influence learning based on student self-regulation. The research design uses an instrument to explore and identify variables. This research is sample research with the Proportional Random Sampling Cluster technique. This technique is used because the population has elements that are not homogeneous and structured proportionally This study uses a sample of 108 students with different backgrounds. From the results of the analysis, it was found that out of the seven factors proposed as variables, five factors influence the self-regulation of students. The findings of this study provide evidence that the student's background dimensions can significantly influence students' self-regulation, so the better the student's background, the better the student's self-regulation. We recommend further studies for deeper examination and analysis of these factors.
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There is a belief that rural students lag behind their urban counterparts and that their academic performance shows greater difference with their social environments (Manley, 2018). The researchers conducted the study in order to testify for the said belief. The objective of this study was to determine if there are differences that occurs in the social adaptation and academic performance between rural-based students and urban-based students.
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As a result of its practical value in routine tasks and transactions, mathematics is regarded as a subject of utmost importance in people's lives. The researcher used a non-experimental quantitative research design focusing on a survey design in this study. The primary objective of the investigation is to investigate the school location to indicate the rural-urban prediction regarding academic achievement in Mathematics of senior secondary level schools in Bodoland Territorial Region (BTR) of Assam State, India. The overall population of the study amounted to four thousand twenty eight (4028) Grades tenth and two thousand six hundred thirty three (2633) Grade twelfth learners sampled from upper secondary schools of BTR in Assam. Research hypotheses were developed and put to the test. The data was analysed by using the statistical technique like descriptive statistics and t-test to determine the nature of achievement. The findings of the result revealed that there is a substantial mean difference in academic achievement in mathematics between boys and girls, students from rural and urban schools, rural school boys and urban school boys, and rural school girls and urban school girls in the tenth grade as well as twelfth grade.
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There is an ongoing concern of rural versus township versus uptown learners achieving different levels of academic success. For this regard, a quasi-experiment was carried out on three Senior Secondary School classes in the Buffalo City Metro education district. A quantitative approach, pretest~post-test on mathematical achievement design was adopted. To analyse data, a two-way ANOVA was run on a sample of 297 participants to examine the effects of teaching strategy (traditional versus cooperative) and the location of schools (uptown vs township vs rural) on learner performance (scores). The findings revealed that there was significant interaction between the effects of teaching strategy and the location of school on learners' performance, F(2,291) = 5.31, p = .0054. cooperative teaching strategy learners perform significantly better (average 20.98; t = 38.20; p =.000) than traditionally taught ones (mean = 11.05; t = 22.65; p = .002). It was also observed that uptown learners under cooperative teaching strategy perform the best, while rural learners taught in the traditional way perform the worst. Sample main effects analysis showed that learners from uptown performance is highest, followed by township and lastly, rural learners. For a close comparison of the three locations, a Scheffe Post-Hoc mean comparison technique was used and the differences were statistically significant, at least at 5%, except for township vs uptown, which is significant at 10%. The greatest difference is between rural vs uptown.
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The study looked at the location of schools as it relates to academic performance of students in Ekiti state of Nigeria between 1990 and 1997. The study population was results of the West African School Certificate Examinations (WASCE) conducted between 1990 and 1997 in 50 secondary schools in both rural and urban areas of the state. One validated instrument “Student Location Questionnaire (SLQ)” was used for data collection. One hypothesis was formulated and answered. Data were analysed using mean and t – test. The results showed that there was a significant differences between students’ academic achievement of rural and urban secondary schools in senior school certificate examinations (t=2.73, p<0.05). The study has proven that students in urban areas had better academic achievement than their rural counterpart. It is recommended that Government should bridge the gap between the rural and urban locations by providing the rural dwellers the social amenities which will enhance better academic performance of students in their final examinations like the SSCE. The community should assist the government by providing taxis and buses to facilitate movement of teachers and students to their school. Adequate incentives should be provided to rural area teachers to encourage them to put in their best to remain in their duty stations.
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This study assessed and investigated the academic performance of secondary school students in two principal subjects (English Language andMathematics) at the Senior School Certificate Examinations (SSCE) in tensecondary schools typical of urban and rural locations in five randomisedLocal Government Areas of Oyo State, Nigeria between 2005 and 2007. The study employed a descriptive survey research design. An instrument titled: Students’ Academic Performance in English Language and Mathematics Questionnaire (SAPEMQ) was used to collect relevant data for the study. The ten secondary schools involved were selected based on simple randomsampling technique and the statistical tools employed to analyse the data collected were percentages, means scores and multiple regression (backward procedure). Four research questions and one null hypotheses were formulated to guide the study. The result among other things revealed that, there was a marked difference in the performance of students in urban and rural schools at the SSCE with impressive means scores obtained in urban schools (Urban = 69.8, 54.4 and 60.2 in 2005, 2006 and 2007 respectively;Rural = 36.4, 24.9 and 23.8 in 2005, 2006 and 2007 respectively). The implications of the findings for educational planning and policy in Nigeria were discussed.
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The paper examined the influence of school type, sex and location on students’ academic performance in Ekiti state secondary schools. The sample of the study consisted of forty (40) secondary schools. Four (4) Government colleges (State Unity colleges) were purposively selected for the study while thirty-six (36) public secondary schools were randomly selected for the study. The school sampled had presented candidates for both West Africa Examination Council (WAEC) and National Examination Council (NECO) respectively. An instrument, school type, sex, location and students’ academic performance inventory was used to collect data for the study. Data collected were analyzed using percentage scores and t-test statistics. Three null hypotheses were generated and tested at 0.05 level of significance. Findings from the study showed that the level of students’ academic performance was low. It was also revealed that school type, sex and location had no significant influence on students’ academic performance. Based on the finding it was recommended that educational planners, administrators and evaluators should appreciate the fact that the Parent Teacher Association; Guidance and Counselors, philanthropists, students and society at large have crucial role to play in improving students’ academic performance and solicit their supports in this regard.
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Using data from the National Education Longitudinal Study of 1988 (NELS: 88), this study examines educational aspirations and postsecondary access and choice by students in urban, suburban, and rural schools. In addition, this study raises issues with the methods in postsecondary educational research by using students in different grades (8th, 10th, and 12th grades) as baseline populations to compare educational outcomes. The results indicated that students in urban schools were comparatively disadvantaged in the early years in schooling in terms of postsecondary access but appeared to be enrolled in postsecondary institutions at similar percentages as their suburban counterparts, if they made it to later years in K-12 schooling. For those students in urban schools who went to college, higher percentages were enrolled in private institutions and four-year colleges. Students in rural schools were consistently disadvantaged in postsecondary aspirations and enrollment, compared to students in other schools.
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One of the potential dangers related to technology occurs when technology access and use are not equitably distributed. This study examined the access and use of technology in urban, suburban, and rural schools by using teacher survey data from the eighth-grade cohort of the National Educational Longitudinal Survey of 1988 (NELS:88). The subjects were 3,825 eighth-grade mathematics teachers who answered questions on the extent to which students had access to technology and how they were using it in their mathematics class. The results indicated that there were several significant differences on technology use by type of school setting. Teachers from suburban schools were more likely to report using calculators than teachers from urban and rural schools. Teachers from rural schools reported that they were less likely to use calculators and computers than teachers from suburban and urban schools. Finally, teachers from rural and suburban schools were more likely to report that their students used computers for enrichment purposes, while urban teachers were more likely to report that computers in their schools were used for remediation.
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In this brief, authors Suzanne Graham and Lauren Provost examine whether attending a school in a rural, urban, or suburban community is related to children’s mathematics achievement in kindergarten, and whether increases in mathematics achievement between kindergarten and eighth grade differ for children in rural, urban, and suburban schools. They also consider whether achievement differs by region of the country and for children of different racial and ethnic groups. Finally, they discuss the impact of a family’s socioeconomic status, and the ways in which place and socioeconomic status together affect both early mathematics achievement levels and change over time. They report that rural and urban kindergarten students have slightly lower average mathematics achievement levels than their suburban peers. In addition, the average increase in mathematics achievement from kindergarten to eighth grade for rural and urban children is smaller than the increase for suburban children, resulting in a widening achievement gap over time.