Gender differences in learning outcome

M O N I T O R I N G L E A R N I N G A C H I E V E M E N T ( M L A ) I N R I V E R S
S T A T E P U B L I C S C H O O L S :
V O L U M E I I
Gender Differences in Learning Outcome
(Report)
Prepared for:
Rivers State Ministry of Education
BY
ARBITRAGE CONSULT LIMITED
arbitrageconsult@gmail.com

1 Table of Content | Arbitrage Consult Limited
T h e A r b i t r a g e C o n s u l t T e a m
J o y O b a l l u m
N a n c y E n e
A d e y e m i O n a f u y e
A m i n a A r o - l a m b o
B e r n a r d B a s a s o n
O l a t a y o B a b a l o l a
D a n i e l O g h o j a f o r
P r o f . O l a s e n i A k i n t o l a - B e l l o ( L e a d C o n s u l t a n t )

2 Table of Content | Arbitrage Consult Limited
Table of Content
Table of Content.........................................................................................................................................2
Executive Summary....................................................................................................................................3
CHAPTER 1..................................................................................................................................................5
1.0 INTRODUCTION: CONTEXT AND DESIGN .........................................................................................5
1.1 Preamble ......................................................................................................................................5
1.3 Objectives of the Study: ...............................................................................................................7
1.4 Significance of the Study..............................................................................................................7
1.6 Methodology and Analysis...............................................................................................7
1.6.1 Scope.........................................................................................................................................7
1.6.2 Data Source......................................................................................................................8
1.6.3 Data analysis ....................................................................................................................8
1.7 Organization of this work.............................................................................................................9
Chapter Two.............................................................................................................................................10
2.0 GENDER DIMENSIONS IN RIVERS STATE ........................................................................................10
2.1 Introduction....................................................................................................................................10
2.1.1. PART 1: SOCIO-ECONOMIC BCKGROUND ..............................................................................10
2.1.2.Gender Disparity .....................................................................................................................13
Where does the Problem Lie?..........................................................................................................13
A Look at the Numbers: Gender Gap in Learning Performance ......................................................13
Chapter three...........................................................................................................................................51
Factors Associated with Gender Differences.......................................................................................51
Chapter Four ............................................................................................................................................55
Strategies for Addressing Gender Differences.....................................................................................55
Strategies in Relation To Learning, Teaching and Assessment ........................................................55
Classroom Organization ...................................................................................................................56
Chapter Five .............................................................................................................................................58
Conclusion and Recommendations......................................................................................................58
Recommendations: ..........................................................................................................................59
Reference .................................................................................................................................................60
Appendix ..................................................................................................................................................63

3 Executive Summary | Arbitrage Consult Limited
Executive Summary
In this study, we focused on gender and learning performance in Rivers state public schools;
its aim was to examine the gender dimension of learning achievement in public primary and
secondary schools in Rivers state: A Monitoring Learning Achievement (MLA) Survey. Using
Article 26 of the Universal Declaration of Human Rights (1948), the Jomtien Declaration (1990)
and the Millennium Development Goals (2000) as conceptual framework, the paper discussed
the imperatives of gender equity in primary and secondary education against the background
of the challenges and prospects. Data on 2013 Monitoring Learning Achievement (MLA)
assessment, school enrolment, dropout rate and teaching manpower were obtained from
primary and secondary sources for the analysis. The result of the analysis show:
 There are gender gaps in Rivers state public schools; these gaps exist in enrollments,
learning performance, repeaters rate, dropout rate and teaching manpower.
 The Rivers state government does not discriminate against females in the field of
education. However, factors that can be directly responsible for gender disparity in
public schools include: socio-economic influences, gender themes in current
educational practices, motivational and psychological issues, school environment, and
teacher attitude.
 From current enrollment statistics, Gender Parity Index (GPI) is 1.07 in 2011/2012
academic year against GPI of 0.97 in 2005. Since a GPI of 1 indicates parity between
the sexes; a GPI that varies between 0 and 1 typically means a disparity in favour of
males; whereas a GPI greater than 1 indicates a disparity in favour of females. This
implies that Rivers state has achieved the MDG’s goals of attaining gender parity Index
(GPI) of between 0.97 and 1.03.
 More boys than girls are perceived to be underperforming from the results of 2013
Monitoring Learning Achievement (MLA) survey, showing that, except in numeracy
and mathematics in secondary schools; girls generally performed better than boys
across the various Local Government Areas. Also this is validated by the fact that there
are more boys than girls who are repeating a class (or more) of schooling.
Dropout rate is higher for girls in both primary and secondary schools.
There are more female to male teachers across public schools in the state.
On the basis of these findings, it was recommended that: Government should organise
workshops for capacity building and policy advocacy on gender issues, which will create the
needed awareness in this regards. Also due to the high dropout rate for female; it is important
for the state government to setup, implement, monitor an intervention programs to address
the females drop out problem. For example, a program to eradicate child labour and promote
Gender and Family values. This catch-up program should be linked to a special curriculum that
facilitates those girls and boys to finish their primary and secondary school. The use of

4 | Arbitrage Consult Limited
gender-sensitive textbooks and curriculum for girls will help bridge the gender-gap. Finally,
Rivers state government should implement a state performance assessment monitoring
system that can disaggregate performance by gender and the interaction between gender and
the assessment process itself.

5 CHAPTER 1 | Arbitrage Consult Limited
CHAPTER 1
1.0 Introduction: Context and Design
1.1 Preamble
Education is vital to advancing human capital by enabling individuals to develop their
knowledge and skills throughout their lives. Relatively high levels of education are often
related to higher earnings and productivity, better career development, health, life
satisfaction as well as to better investments in education and health of future generations.
There has been a strong case for greater gender equality across the world; this implies greater
educational and economic opportunities for both men and women. However, achieving
greater gender parity remains a big challenge despite the many gains in women’s educational
and employment outcomes.
Gender parity in learning performance has been a subject of focus in the international
community, since the Beijing conference of 1989. The main objective of the Beijing
Conference was to allocate sufficient resources for and monitor the implementation of
educational reforms; while achieving gender equality is seen fundamentally as enhancing
education performance. However, many factors impede gender equality, such as limited
access to schooling, low female enrolment and high school dropout.
Article 26 of the Universal Declaration of Human Rights (1948); the Jomtien Declaration of
1990, both stipulate that basic education should be provided for all children, youth and adults
and the Millennium Development Goals (MDGs) specifically Goal 3, also stress that the goal is
to promote gender equality and eliminate gender disparity in primary and secondary
education, preferably by 2005, and in all levels of education no later than 2015 (United
Nations Millennium Project, 2005).
Similarly, the United Nations in 1975, proclaimed the International Decade for Women in
recognition of the fact that women who constitute about 50 percent of the World’s
population have not been given a fair share of the resources and opportunities in all spheres
of life. The critical importance of gender equity in education is also underscored by the
Millennium Development Goals (MDG’s), and the Dakar goals set for Education for All (EFA)
among other platforms. Gender equity became not only a development imperative but an
essential human rights issue. The assumption is that if education is a basic human right, then
quality education should promote equality of opportunities between sexes.
The aforementioned declarations were reinforced by the Convention on the Right of the Child
(CRC) in 1989, which states that children may not be excluded from any right, including
education, based on race, sex, disability, and economic status.

The 1997 Human Development Report (HDR) of the United Nation’s Development Programme
observes that “no society treats its women as well as its men”. The report further elaborates
that gender inequality is strongly associated with Human Poverty Index (HPI), and substantial
progress in gender equality has been made in only few societies, as women suffer the double
deprivation of gender disparity and low achievement in education.
Further, the United Nations Decade of Education for Sustainable Development (DESD) is a
complex and far-reaching undertaking. The overall goal of the DESD is to integrate the
principles of sustainable development into teaching and learning. This educational effort will
encourage changes in behavior that will create a more sustainable future in terms of
environmental integrity, economic viability, and a just society for present and future
generations.
In spite of these declarations, gender equity has remained a daunting challenge. The Forum
for African Women Educationalists (FAWE) observed many years ago that many more girls
than boys are left out in the education system, and emphasized the need for countries in the
Region to identify effective strategic intervention that will enhance the improvement of girls’
participation (FAWE, 2001). The forum also identified factors such as low socio-economic
status, long walking distances, poor policies that are not gender specific, socio-cultural
attitudes and other school related factors as responsible for the disparity.
Data collected in 1999 from 154 countries showed that 115.4 million school age children were
not in school and of these, 56% were girls, (UNESCO 2002); and 94% of these came from the
LCDS while one - third were from sub-Sahara Africa. Indeed gender inequity holds back the
growth of individuals, the development of countries and the evolution of societies, to the
disadvantage of both men and women, (UNESCO; 2004).
Analysis done in 128 countries showed that despite the pursuit of the MDG’s goals for
attaining gender parity Index GPI of between 0.97 and 1.03, 56% of the countries mostly in
sub-Saharan Africa and South West Asia were far behind. As at 2000, sub-Saharan Africa stood
at the average GPI of 0.89; an indication that Gender Equity Recurrent for girls has become
increasingly significant. In countries where enrolment is high, the trend has been that the
participation and performance of girls lagged behind that of boys and thus been aggravated
by high repetition rate; high dropout rate and low retention rate.
In response to the global commitment to bridge the gender gap in education, the Rivers state
government, through the Ministry of Education, developed the Blueprint on Education and
have been implementing strategic initiatives designed to enhance the educational sector. One
of such initiatives led to the conduct of the monitoring learning achievement (MLA) survey; in
other to assess if student are actually learning; and also focusing on bridging the knowledge
and capacity gaps between males and females and sought to provide the necessary leverages
to bring about gender equity in certain critical domains of education.

1.3 Objectives of the Study:
The general objective of this study is to assess if there are gender disparities in learning
performance and to highlight which of the gender has a better learning performance based on
the 2013 MLA assessment. This paper therefore assessed the gender difference in learning
performance of Primary four pupils and JSS two students of public schools in Rivers state. The
following questions were addressed in this study: Does enrolment gap exists and is there a
gendered achievement in favor of female pupils and students? If so, is the difference
significant? This research was carried out to give a description of learning performance of
females as compared to males in the 2013 MLA assessment in Rivers state.
The following hypotheses were formulated for the study.
Ho1: There is no significant difference in the academic performance of female and male
pupils/students in the 2013 MLA assessment in Rivers state.
Ho2: There is significant difference in the academic performance of female and male
pupils/students in the 2013 MLA assessment in Rivers state.
1.4 Significance of the Study
The significance of the study is to evaluate gender equity in performance, and determine the
extent to which River state has closed the gaps. This study will help to present an evidence-
based framework that can assist stakeholders in the education policy formulation process as it
affects gender. Essentially, it should be useful to the Rivers state government, the federal
government, civil society, International communities, public and private organization and the
entire citizens of the nation. More importantly, the findings from this study will be useful for
educational policy makers as it will reveal the state of education as regards to enrolment and
gendered result of learning performance. This will in turn enable the government to direct her
effort towards sustaining of students interest, and eventually the growth and the
development of education in Nigeria and Rivers state in particular.
Methodology and Analysis
1.6.1 Scope
The research work covers the entire public primary and secondary schools in Rivers state.
Questionnaires were administered in 6 learning domains across 916 public primary schools
and 276 junior secondary schools. The study covered not only the gender aspects of
performance, but the influence of residential location (rural versus urban) as well as other
socio-economic factors that may influence pupils’ and students learning outcome. The
population of the study is a total population of 15560 pupils and students comprising of
11,862 primary four pupils and 3,698 JSS2 students across the state. The proportion of male to
female was randomly selected for the 2013 MLA assessment, and all the students were tested
in various learning domains and their sub-components; as follows:
Learning Domain for Primary Four Pupils.

 Literacy
 Comprehension
 Grammar
 Vocabulary
 Numeracy
 Numbers
 Measurement
 Quantitative
 Life Skills
 Health
 Civic
 Technology
JSS2 Student Assessments were based on the following domains:
 English Language
 Comprehension
 Grammar
 Vocabulary
 Mathematics
 Numbers
 Algebra
 Geometry
 General Science
Data Source
This research study is limited to data collected from the Monitoring of Learning Achievement
(MLA) survey conducted in Rivers State between July–August 2013, and the Rivers State
Schools Census Report, for 2011/2012. It is a combination of primary and secondary sources
of data.
Data Analysis
Test scores of student from different learning were analyzed using descriptive statistics
employing frequencies, percentages, means, standard deviations, bar charts, line graphs and
t-tests and z-statistics. We then estimate the factors associated with gender differences using

single-equation regression model (ANOVA model) and dummy variables to point out
differences, if they exist, between boy’s and girl’s performance in the learning domain.
1.7 Organization of this Work
This work is organized into five chapters. Chapter one (context and design) comprises of
general introduction such as: Background to the study, statement of the problem, study
objective, significance and scope of the study, and research methodology.
Chapter two, (Gender Dimensions in Rivers state) has two parts, Part 1: Socio-Economic
Background of Rivers state; giving a brief description of socio-economic issues with emphasis
on gender status in the state, part 2: Gender disparities: Where does the problem lie?; this
part delved into the average gender gap in learning performance, asking the question: where
the problem really resides? It revealed a complex picture of gender differences and other
inequalities in education, all of which need to be taken into account.
In Chapter three (Factors associated with gender differences) we highlight the factors
associated with gender differences based on extant research analysis, by estimating the
factors associated with gender differences using single-equation regression model(ANOVA
model) and dummy variables to point out differences, if they exist, between boys’ and girls’
performance in each of the learning domains.
In Chapter four (Strategies for addressing gender differences) we look at some strategies
aimed at addressing underperformance. Strategies perceived to be successful include; study
support, mentoring, and building confidence, self-esteem and developing systems of target-
setting for individual pupils, and their implications for gender differences because more boys
than girls were perceived to be under-performing.
Chapter five concludes and makes recommendations based on the findings.

10 Chapter Two | Arbitrage Consult Limited
Chapter Two
2.0 Gender Dimensions in Rivers State
2.1 Introduction
This chapter has three parts. Part 1: Socio-Economic Background of Rivers state; is a brief
description of socio-economic issues with emphasis on gender status in the state; Part 2:
Gender disparities: Where does the problem lie? Part 3: Gender Analysis of Other Indicators of
Learning Performance.
This part is an in-depth analysis of the average gender gap in learning performance, asking the
question: where the problem lay? It revealed a complex picture of gender differences and
other inequalities in education, all of which need to be taken into account.
2.1.1. Part 1: Socio-Economic Bckground
Rivers state was created in 1967 with the split of the eastern region of Nigeria. Its capital is
Port Harcourt. It is bounded on the south by the Atlantic Ocean, to the north by Imo, Abia and
Anambra states, to the east by Akwa Ibom state, to the west by Bayelsa and Delta states.
Rivers state is home to a variety of ethnic groups, including Abua, Andoni, Ekpeye, Engenni
,Etche, Ibani, Ikwerre, Kalabari, Ogba/Egbema/Ndoni, Okrika and Ogoni. The State is the 5th
most populous State in Nigeria. The 2006 National Population Census places the population
of the state at 5,133,400, and of this population; 2,809,840 and 2,474,735 constitute the male
and female population respectively. It also has a school age children population of 1,866744.
Rivers state is currently made up of 23 local government areas. These are Abua/ Odual,
Ahoada East, Ahoada West, Andoni, Asari /Toru, Akuku/Toru, Bonny, Degema, Eleme,
Emohua, Etche, Gokana, Ikwerre, Khana, Obio /Akpor, Ogba/Egbema/Ndoni, Ogu/Bolo,
Okrika, Omuma, Opobo/Nkoro, Oyigbo, Port Harcourt, and Tai. Port Harcourt, the state’s
capital is Nigeria’s second largest commercial and agricultural centre and has the second
busiest seaport in Nigeria. Agriculture is the main occupation of the people of River State. The
strategic importance of Rivers State in the economic equation of Nigeria earned her the name
“treasure base of the nation”; due to the fact that Rivers state accounts for more than 48% of
Nigeria crude oil production and also one of the largest economies in Nigeria, mainly because
of its crude oil and heavy presence of oil and gas related industries.
Accordingly, there is an increase in the influx of people into the State from other States,
including expatriates. With the increase in population, this leads to an increase in demand for
basic education. Paradoxically, poverty is still prevalent in the state. The poverty profile of the
state is shown in a recent study by MDG (2008); Table1 below illustrates that in Rivers state,
the poverty line is N 12,500 and the poverty incidence is 31.5% this means that 31.5% of the
household in Rivers state were poor or the income of 31% of population is below the poverty
line of N 12,500. The poverty gap of 23.4% is the equivalent amount that needs to be
transferred to the poor in other to bring them to the poverty line. The estimated poverty gap

for the LGAs ranges from 0.3 percent in Eleme to as high as 41.2 percent in Degema. Other
LGAs with high Poverty Gap ratio were Ogu/Bolo and Bonny. The Gini Coefficient which gives
a more precise and quantifiable measure of inequality and also provides a solid understanding
of the direction of concentration and distribution of income among groups. The Gini
Coefficient is 0.58 in the state; this implies that the income inequality between the rich and
poor is high. However, LGAs experienced different levels of income or expenditure disparity.
While, Tai (0.19), Gokana (0.28), Eleme (0.37), Emuoha (0.40), Ikwerre (0.41) and
Abua/Oduah (0.42) have lesser income/expenditure spread than the state average; Degema
(0.82), Bonny (0.68), Ahoada East (0.62), Onelga (0.61) and Okrika (0.59) have higher
income/expenditure spread than the state average; Ogu/Bolo (0.58) has the same
income/expenditure spread as the state average. It is curious to observe that LGA, with high
poverty incidence(headcount ratio) also have high poverty gap ratio and higher than average
Gini.
Table 1: Poverty Indicators by LGAs
Source: MDG, 2008
The prevalence of poverty in the state affects the learning performance of pupils and students
in the state, as it is still classified as one of the educationally disadvantaged States in Nigeria.
In spite of the existence of 2292 public primary and secondary schools and 1244 private
primary and secondary schools (Rivers State School Census Report, 2012) – an indication that
State
Poverty Line Headcount Ratio % Poverty gap ratio Gini Coefficient
N 12,500 31.5 23.4 0.58
Local Government Area (LGA) Headcount Ratio % Poverty gap ratio Gini Coefficient
Abua/Odua 21.7 8.91 0.42
Ahoada East 33.3 20.02 0.62
Ikwerre 16.7 8.78 0.41
Emuoha 28.3 20.17 0.4
Onelga 12.5 12.5 0.61
Eleme 0.83 0.3 0.37
Tai 1.67 1.67 0.19
Bonny 30.8 30.83 0.68
Degema 41.7 41.67 0.82
Okrika 15.7 15.15 0.59
Ogu/Bolo 40.0 40.0 0.58
Gokana 5.8 0.96 0.28

the existing private and secondary schools cannot accommodate the increasing demand for
primary and secondary education.
Also from the baseline survey as illustrated in Volume One of this survey, we observed a high
influence of poverty on the pupils and students, which led to the deduction that poverty
keeps many children away from schools; The survey shows that 61% of school age children
don’t have breakfast before going to school, while Only about 39% of respondents were well
fed. Since hungry children are less likely to concentrate in class, this could result in poor
performance and can even result to absenteeism. Again, poverty contributes to high drop-
outs and low performance due to irregular school attendance, a study by Arbitrage Consult
(2013) on out of school children in Rivers state showed that 82.44% of dropout is due to
poverty. This further reinforces our earlier reference that “gender inequality is strongly
associated with Human Poverty Index (HPI), and substantial progress in gender equality has
been made in only few societies, as women suffer the double deprivation of gender disparity
and low achievement in education”, {Human Development Report (HDR) 1997}. This is proven
by the fact that most parents in African communities largely have preferences for educating
male children, hence family development efforts are invested in boys while girls are treated as
second fiddles. Thus, this attitude generally contributes to low enrolment, low participation
and poor performance of girls in schools. The above factors are rendered more complex by
drop outs due to teenage pregnancy and early marriages. Our baseline survey also reveals
that there is a disparity in the view of parent as regards to the education of their children,
19% of the parents believe that male child should be educated more than girl child, 38%
responded “Yes” to the question “if male child should be educated more than the female”,
while 43% were indifferent. Consequently, this leads to gender disparity (gaps) in enrollment
and eventually, performance of pupils and students.
Eliminating gender disparity is understood to mean overcoming the barriers to equal access to
and achievement in schooling for girls and boys. Thus, gender parity is measured by the
enrolment strength of boys and girls and the completion rate. That is why the Task Force on
the MDGs (2004) states “that working to achieve the stipulated targets in the MDGs should
look beyond access to forging collaboration among the various actors to adopt best practices
to improve curriculum and pedagogy”. This also involves eliminating factors that impede the
education of the girl child; these include:
 Poverty and economic issues especially with girls being sent to engage in income
generating ventures.
 Early marriage and teenage pregnancy, as about 30% of school age girls drop out of
school.
 Inadequate school infrastructure such as classroom space, furniture, equipment and
other educational inputs.
 Cultural and religious biases especially in communities predominantly Islamic

 Gender bias in curricula and pedagogy, and
 Inadequate and poorly trained teachers.
2.1.2. Part 2: Gender Disparity
Where does the Problem Lie?
Dissimilarities between girls and boys learning performance outcomes have been issues of
concern generally, and not only in Rivers state. There has been awareness that some aspects
of school education may contribute to the disadvantaged position of women in society and in
employment. More recently, there has been concern that the levels of attainment of boys in
school examinations have been lower than those of girls. Important issues of equality of
opportunity for females and males need to be addressed by the education system. Gender
differences in learning performance have become an increasing focus of policy over the past
few years because of their implications for strategies to raise standards of performance in
schools in Rivers state. If the State targets for raising learning performance are to be met it is
important that pupils of both genders should achieve all they can.
A peak at the Numbers: Gender Gap in Learning Performance
Primary and Secondary Education
With an enabling environment and policies in place, Rivers state has made strong progress in
improving access to education at all levels, and improving gender parity at the primary and
secondary levels, with respect to achieving the MDG goals. Despite these efforts, gender
disparities still exist in public primary and secondary schools in the state: This part went
beneath the average gender gap in learning performance, to ask the question: where the
problem really is? It revealed a complex picture of gender differences and other inequalities in
education, all of which need to be taken into account
Enrollment
The tables below show a summary of enrollment in Rivers state school. Tables 1A and 1B
provide data of the enrolment of pupils and students in primary and secondary schools in the
2011/2012 academic session. Rivers State has 23 Local Government Areas with a total of 3536
public and private schools; i.e. 2292 public schools, and 1,244 private primaries and secondary
at the time of executing this study. Table 1B further shows that in the 2011/2012 academic
session, a total of 532,000 students enrolled, and of this number, 257,505 or 48.4%
represented male enrolment, while 274495 or 51.6% represented female enrolment. The
population of the state is 5,185,400 (2006 census figure), of this figure 33% of this population
is supposed to be in schools (population of school –age-children), which translates to
1,712,591 pupils and students, ironically only 1,459,813 are in school and 252,778 children are
out of school(NPC,2009). Furthermore, figure 1 illustrates that: enrollment is highest at
Obio/Akpor and lowest at Ogu Bolo, while girls enrollment is lowest in Abua/Odua at 48.94%
and highest in Port Harcount LGA (PHALGA) with 51.9%; Similarly, for the junior secondary

school; girls enrollment is lowest in Ahoada west and highest in PHALGA, and in the senior
secondary school category, girls enrollment is lowest at Degema and highest at PHALGA.
Table 1A: Number of public schools, enrolment and teachers
Level Number of
schools
Number of pupils Number of teachers
Male Female Total Male Female Total
Pre-primary
and primary
912 21143 23494 44637 2756 4805 7573
Primary
only
895 123440 127378 250818 2756 4805 7573
Junior
secondary *
260 44175 49405 93580 2305 2670 4975
Senior
secondary *
225 47627 53589 101216 1904 1064 2968
Grand total 2292 236385 253866 490251 9721 13344 23089
Source: State School Census Report 2011-2012

Table 1b: Summary Analysis of by gender and LGEA (2011-2012)
LGEA Primary School Total Junior secondary school Total total of Senior secondary school Total Total Gender grand total Gender ratio
Pupils Girls % girls Students Girls % girls Students Girls %% girls % girls Boys Girls Total
ABUA/ODUAL 7371 3607 48.94% 1770 979 55.31% 4288 2237 52.17% 6606 6823 13429
1.03
AHOADA EAST 12028 6035 50.17% 5377 2537 47.18% 5148 2693 52.31% 11288 11265 22553
0.99
AHOADA WEST 12634 6283 49.73% 2763 1235 44.70% 2152 1005 46.70% 9026 8523 17549
0.95
AKUKU-TORU 4364 2244 51.42% 1302 682 52.38% 1044 593 56.80% 3191 3519 6710
1.10
ANDONI 12237 6053 49.46% 3453 1550 44.89% 3919 1902 48.53% 10104 9505 19609
0.94
ASALGA 2746 1363 49.64% 1546 697 45.08% 1743 849 48.71% 3126 2909 6035
0.93
BONNY 8111 4065 50.12% 3142 1680 53.47% 2188 1190 54.39% 6506 6935 13441
1.07
DEGEMA 3953 1979 50.06% 1163 576 49.53% 1075 496 46.14% 3140 3051 6191
0.97
ELEME 10968 5751 52.43% 4485 2555 56.97% 3288 1750 53.22% 8685 10056 18741
1.16
EMOHUA 13535 6649 49.12% 5445 2607 47.88% 6648 3201 48.15% 13171 12457 25628
0.95
ETCHE 17263 8820 51.09% 6391 3292 51.51% 7928 3911 49.33% 15559 16023 31582
1.03
GOKANA 16298 8290 50.87% 5199 2607 50.14% 7077 3676 51.94% 14001 14573 28574
1.04
IKWERRE 11719 5935 50.64% 4669 2308 49.43% 4951 2481 50.11% 10615 10724 21339
1.01
KHANA 23348 11785 50.48% 10159 5281 51.98% 9401 4735 50.37% 21107 21801 42908
1.03
OBIO/AKPOR 37952 19751 52.04% 27077 15358 56.72% 17313 10422 60.20% 36811 45531 82342
1.24
OGU/BOLO 2476 1236 49.92% 374 191 51.07% 384 212 55.21% 1595 1639 3234
1.03
OKRIKA 9730 4929 50.66% 2473 1170 47.31% 2132 1087 50.98% 7149 7186 14335
1.01
OMUMA 5522 2855 51.70% 1703 834 48.97% 1916 924 48.23% 4528 4613 9141
1.02
ONELGA 25172 12577 49.96% 12169 6031 49.56% 11533 5743 49.80% 24523 24351 48874
0.99
OPOBO/NKORO 3614 1838 50.86% 1036 487 47.01% 333 150 45.05% 2508 2475 4983
0.99
OYIGBO 15544 7919 50.95% 8512 4456 52.35% 7766 3998 51.48% 15449 16373 31822
1.06
PHALGA 28957 15025 51.89% 4933 3170 64.26% 12995 7879 60.63% 20811 26074 46885
1.25
TAI 10649 5412 50.82% 2119 1147 54.13% 3327 1530 45.99% 8006 8089 16095
1.01
Grand Total 296191 150401 50.78% 117260 61430 52.39% 118549 62664 52.86% 257505 274495 532000
1.07

Figure 1: Graphical Illustration of School Enrollment by Gender and LGA
Gender Ratio
Gender ratio is an indicator for measuring gender balance in enrolment between boys and
girls. It is the ratio of female gross enrolment to male gross enrolment. If the ratio is 1 there is
a perfect balance in male and female participation. If the gender ratio is less than 1, there is
gender imbalance in favour of males. On the other hand, if the gender ratio is greater than 1,
the imbalance is in favour of girls. Across the state, (Figure 2), there is gender imbalance in
favour of females. Gender ratio for the 2011-2012 sessions was 1.07. This means that girls had
greater access to primary and secondary education than boys.
The picture is different when participation in the primary school is disaggregated by LGAs.
Fifteen LGAs namely: Abua Odua, Akuku-Toru, Bonny, Eleme, Etche, Obio Akpor, Gokana,
Ikwerre, Khanna, Ogu/ Bolo, Okrika, Omuma, Oyigbo, PHALGA, and Tai had average gender
ratios which are greater that one; suggesting imbalance in access to primary education in
these LGA in favour of females. On the contrary, the remaining 8 LGAs had gender ratios of
less than one.

Figure 2: School enrollment gender ratio by LGA
Performance
The Analysis of Gender gaps in learning performance is a very important component of the
2013 Monitoring Learning achievement (MLA) survey. The contention is that gender-equity in
public schools in Rivers State can be achieved by understanding the dimension of gender
disparity in learning performance in the schools. From the result of pupils and students
assessment in the 2013 MLA survey in Rivers state as illustrated in Tables 2 and 3, we found
that there are differences in performance of pupils and students in the different learning
domains across LGAs with respect to gender: on the average, boys perform marginally better
in Numeracy and mathematics and general science whereas girls perform better in Literacy
and English language. However, the advantage of girls in Literacy and English Language is
larger than the advantage of boys in Mathematics.

Table 2: Analysis of Primary School Performances by LGAs in Literacy, Numeracy and Life
Skills test by Gender
LGA LITERACY NUMERACY LIFE SKILL
MALE FEMALE MALE FEMALE MALE FEMALE
Abua/ODUA 67.40 67.50 58.50 57.72 79.72 80.95
Ahoada East 56.80 58.40 40.30 41.52 72.02 73.96
Ahoada West 45.70 50.60 47.80 46.00 55.36 53.91
Andoni 54.60 56.00 47.70 48.64 69.50 90.35
Akuku Toru 66.90 70.10 46.20 49.59 70.64 72.67
Asari Toru 62.50 68.50 39.70 40.09 68.52 71.95
Bonny 61.20 63.00 64.80 68.67 70.35 70.85
Degema 64.80 64.60 40.18 37.92 71.44 73.38
Eleme 56.50 56.50 57.12 57.09 71.51 71.54
Emohua 53.00 53.90 46.23 46.24 68.67 69.69
Etche 66.30 66.90 44.50 46.80 72.40 74.70
Gokana 55.40 53.70 46.67 40.59 61.66 59.02
Ikwere 65.90 61.60 60.57 54.17 74.82 78.54
Khana 56.80 59.50 48.43 48.67 69.94 69.73
Obio/Akpor 61.80 64.90 44.79 43.43 83.77 82.34
Onelga 61.90 59.90 73.26 74.29 48.58 49.82
Ogu/Bolo 67.20 69.30 62.66 60.05 72.36 70.95
Okrika 64.70 57.30 55.06 53.35 79.86 74.93
Omuma 71.70 71.80 49.55 52.84 64.52 65.39
Opobo/Nkoro 62.20 63.00 59.03 48.64 52.44 90.35
Oyigbo 68.70 70.00 50.91 50.76 69.36 69.06

Port Harcourt 60.50 61.60 48.97 50.33 72.56 73.04
Tai 64.00 65.50 41.96 42.54 70.28 46.94
Grand Mean Score 59.80 60.50 49.86 49.15 67.19 69.03
Source: MLA assessment, 2013
Table 3: Analysis of Secondary School Performances by LGAs in English, Mathematics and
General Science test by Gender
LGAs English Mathematics General science
Male Female Male Female Male Female
Abua/ODUA 55.27 59.38 37.88 46.30 70.37 78.28
Ahoada East 37.18 41.41 57.79 46.12 68.08 66.63
Ahoada West 55.22 57.34 61.87 58.84 44.46 45.87
Andoni 31.77 33.06 48.63 49.56 47.07 46.42
Akuku Toru 41.76 41.69 54.65 52.87 48.35 45.77
Asari Toru 57.59 58.09 42.25 43.72 65.59 65.81
Bonny 52.45 54.73 64.48 63.56 32.88 32.88
Degema 37.34 38.91 37.81 24.36 67.20 60.89
Eleme 51.09 51.92 48.38 48.60 72.00 69.94
Emohua 50.51 54.55 47.99 46.98 68.01 59.88
Etche 51.02 45.11 44.86 41.78 63.00 67.24
Gokana 43.85 53.82 33.55 50.17 45.89 48.98
Ikwere 49.50 43.22 53.42 45.44 37.25 35.04
Khana 36.75 36.82 43.70 41.96 51.69 53.69
Obio/Akpor 53.96 54.50 33.81 39.99 75.61 69.94
Onelga 52.64 52.02 50.02 45.70 65.33 69.29
Ogu/Bolo 54.00 63.35 53.71 59.10 68.13 73.50

Okrika 53.82 55.28 57.15 53.35 77.33 70.74
Omuma 46.40 48.13 40.92 31.43 41.70 35.50
Opobo/Nkoro 39.23 40.26 38.40 37.40 44.97 46.21
Oyigbo 60.48 58.14 33.65 35.97 70.39 63.10
Port Harcourt 72.41 71.46 38.76 38.11 72.46 71.03
Tai 47.79 48.51 41.75 46.12 57.98 59.58
Grand Mean Score 49.22 50.51 46.32 45.54 58.94 58.10
Primary School
From the performance of the pupils across the Local Government Areas in the three learning
domains: Literacy, Numeracy and Life Skills, we observed that in:
Literacy
Figures 1 and 2 below show that, Female pupils scored an average of 60.53 marks or 50.3%
against 59.79 marks or 49.7% scored by males. Apparently from the charts, females
performed slightly better than their male counterpart in Literacy domain test across the LGAs.
From table 2 we can claim that, although there is a difference between the two group means
and substantial difference in performance in some LGAs, the difference is not statistically
significant. This implies that the difference may be caused by chance since the t- test value of
0.8 at 95% confidence limit is lesser than t-critical value of 1.68. So, the null hypothesis which
states that there is no significant difference in the academic performance of female and male
in the 2013 MLA assessment was accepted.

Figure 1: Mean scores of pupils by sex and LGA in the Literacy domain
0
10
20
30
40
50
60
70
80
Literacy
Male
Female

Figure 2: Group Mean scores of pupils by sex in the Literacy domain
Table 2: Mean rating, standard deviation and t-analysis of Literacy mean score
Literacy Gender N Mean Std df t-cal t-crit Ho
Female 23 60.53 9.65 44 0.7958 1.68 Accept
Male 23 59.79 9.7
Numeracy
In figures 3 and 4, we observed that female scored an average of 49.2 marks or 49.6% against
the 49.9 marks or 50.4% scored by the males in Numeracy domain test. As expected, males
performed marginally better than their Female counterpart in Numeracy domain test across
the LGAs. From table 3 we can claim that, although there is a difference between the two
group means and substantial difference in performance in some LGAs, the difference is not
statistically significant. This implies that the difference may be caused by chance since the t-
test value of 0.6343 at 95% confidence limit is lesser than t-critical value of 1.68. So, the null
hypothesis which states that there is no significant difference in the academic performance of
female and male in the 2013 MLA assessment was accepted.

Figure 3: Mean scores of pupils by sex and LGA in the Numeracy domain
0
10
20
30
40
50
60
70
80
Numeracy
Male
Female

Figure 4: Group Mean scores of pupils by sex in the Numeracy domain
Table 3: Mean rating, standard deviation and t-analysis of numeracy mean score
Female 23 49.14 14.001 44 0.6343 1.68 Accept
Male 23 49.90 11.946
Life Skill
In the Life skill domain, figures 5 and 6 show that averagely, female scored 69.02706 marks or
50.7% against 67.18843 marks or 49.3% scored by the males, thus females performed
marginally better than their male’s counterpart in Life skill domain test across the LGAs. As
observed from Figure 5; females performed far better than their male counterpart in Adoni
and Opobo/Nkoro LGAs.From table 3 we can claim that, although there is a difference
between the two group means and substantial difference in performance in some LGAs, the
difference is not statistically significant, thus implying that the difference may be caused by
chance since the t- test value of 0.51 at 95% confidence limit is lesser than t-critical value of
1.68. So, the null hypothesis which states that there is no significant difference in the
academic performance of female and male in the 2013 MLA assessment was accepted.

Figure 5: Mean scores of pupils by sex and LGA in the Life Skill domain
0
10
20
30
40
50
60
70
80
90
100
Life Skill
Male
Female

Figure 6: Group Mean scores of pupils by sex in the life skill domain
Table 4: Mean rating, standard deviation and t-analysis of Life skill mean score
Female 23 69.03 14.001 44 0.6343 1.68 Accept
Male 23 67.19 11.946
Secondary School
The MLA tests for secondary schools were administered in 3 learning domains and their sub-
components. These domains are: English Language, Mathematics and General science. From
the performance of students from schools across the Local Government Areas in the three
learning domains, we observed as follows:
English Language
Figures 7 and 8 below show that, Female students scored an average of 50.51 marks or
50.65% against 49.22 marks or 49.36 scored by males. Apparently, the females performed
slightly better than their male counterpart in this domain test. A t- test value of 0.638991 at
95% confidence limit is lower than the t-critical value of 1.6, thus we can conclude that
although there is a difference between the two group means, the difference is not statistically
significant; this difference can be attributed to chance. Hence the null hypothesis that there is
no significant difference in the academic performance of female and male in the 2013 MLA
assessment English Languageis accepted.

Figure 7: Mean scores of students by sex and LGA in the English Language domain
0
10
20
30
40
50
60
70
80
English Language
Male
Female

Figure 8: Group Mean scores of students by sex in the Literacy domain
Table 5: Mean rating, standard deviation and t-analysis of numeracy mean score
English
Language
Gender N Mean Std df t-cal t-crit Ho
Female 23 50.51 9.16 44 0.63899 1.68 Accept
Male 23 49.22 9.33
Mathematics
The performances of students across the Local Government Areas in the Mathematics
domains are shown in Figures 9 and 10 below. It can be observed that Female students scored
an average of 45.55 marks or 49.6% as against 46.3 marks or 50.4 % by male students,
showing females performed slightly lower than their male’s counterpart in this domain test. A
t- test value of 0.83 at 95% confidence limit is lower than the t-critical value of 1.68. We can
conclude that although there is a difference between the two group means, the difference is
not statistically significant; and can be attributed to chance. Therefore, we accept the null
hypothesis that there is no significant difference in the academic performance of female and
male in the 2013 MLA Mathematics assessment. However the average performance in
mathematics as compared to other learning domains average at the secondary school level is
low.

Figure 9: Mean scores of students by sex and LGA in the Mathematics domain
0
10
20
30
40
50
60
70
Mathematics
Male
Female

Figure 10: Group Mean scores of studentss by sex in the Mathematics domain
Table 6: Mean rating, standard deviation and t-analysis of Mathematics mean
score
Mathematics Gender N Mean Std df t-cal t-crit Ho
Female 23 45.54 8.98 44 0.7711 1.68 Accept
Male 23 46.32 9.15
General Science
The performance of students across the Local Government Areas in General Science domains
is illustrated in Figures 11 and 12 below; Female students scored an average of 58.01 marks or
49.6 % against males’ 58.94424 marks or 50.4 %. Apparently the females performed slightly
lower than their male counterpart in this domain test. A t- test value of 0.83 at 95%
confidence limit is lower than the t-critical value of 1.68. We can conclude that although there
is a difference between the two group means, the difference is not statistically significant; and
can be attributed to chance. Therefore we accept the null hypothesis that there is no
significant difference in the academic performance of female and male in the 2013 MLA
assessment in General Science as a learning domain.

Figure 11: Mean scores of students by sex and LGA in the General Science domain
0
10
20
30
40
50
60
70
80
Abua/ODUA
AhoadaEast
AhoadaWest
Andoni
AkukuToru
AsariToru
Bonny
Degema
Eleme
Emohua
Etche
Gokana
Ikwere
Khana
Obio/Akpor
Onelga
Ogu/Bolo
Okrika
Omuma
Opobo/Nkoro
Oyigbo
PortHarcourt
Tai
General Science
Male
Female
S
ource: MLA assessment, 2013

Figure 12: Group Mean scores of students by sex in the General Science domain
57.6
57.8
58
58.2
58.4
58.6
58.8
59
Male
Female
General Science
Table 7: Mean rating, standard deviation and t-analysis of General Science mean
score
General
Science
Gender N Mean Std df t-cal t-crit Ho
Female 23 58.10 13.47 44 0.8317 1.68 Accept
Male 23 58.94 13.45

Gender Analysis using Criterion-Referenced Assessment
Criterion-referenced assessment is an assessment where an individual’s performance is
assessed based on a specific learning objective or performance standard and not compared to
the performance of other students or test takers as in norm-referenced assessment. It tends
to evaluate how well students are performing on specific goals or standards rather than how
their performance compares to a norm group of test takers. In criterion-referenced
assessment, each person is their own unique individual and is only compared to them. It
involves teaching students based on their needs with respect to a set standard/objective; and
assessing them based on their knowledge of such target standards. Thus, measuring against
such fixed goals can be used to examine the success of an educational reform program which
seeks to raise the achievement of all students unto new and improved standards. Hence,
instead of comparing them to their peers of same age or class; they are simply compared to
their prior performance.
For this study, the Minimum Mastery Level (MML) and Desired Mastery Level (DML) will be
used as a performance standard; the MML and DML were derived using statistical methods.
The MML and DML in each learning domain are specified as the sum (in the case of DML) or
difference (in the case of MML) of the mean score and thrice the standard deviation divided
by the square root of the number of LGAs. This corresponds to the upper control limit of the
control charts in each learning domain. Therefore, scoring above the upper limit signifies a
mastery of the subject whereas scoring below the lower limit signals a failure in learning but
though within the control limits but indicating a possibility to attain the minimum mastery
level. We will assess the performances of boys and girls against a set performance standard
MML and DML.
Table 7: Mean rating, MML, DML, and State Mean Score
Pupils Students
S/N Learning
Domain
Boys
Mean
Score
Girls
Mean
Score
MML DML State
Mean
Score
Learning
Domain
Boys
Mean
Score
Girls
Mean
Score
MML DML State
Mean
Score
1 Literacy 59.78 60.53 57.2 64.9 57.7 English 49.22 50.51 43.7 55.7 49.7
2 Numeracy 49.90 49.14 44.9 54.4 49.6 Mathematics 46.32 45.54 40.0 50.0 45.2
3 Life Skill 69.03 67.17 66.9 74.8 70.9 General
Science
58.10 58.94 50.5 66.3 58.4

Figure 13: Mean Scores of Pupils Disaggregated by Sex and Locational Residence Relative to Mastery Levels: Literacy domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
AxisTitle
Literacy
Male
Female
STATE MEAN SCORE
MML
DML

Fig.13, Shows that in the Literacy learning domain, about 65 percent of the Boys from 15 LGAs
achieved the Desired Mastery Level, while 60 percent of Girls from 14 L.G.As achieved the
Desired Mastery Level. Although, 5 and 8 LGAs have their boys and Girls fall respectively
within the control limits; this implies that they have achieved the Minimum Mastery Level in
literacy learning domain. But, 2 LGAs each have their boys and Girls achieved below the lower
control limit1
.
1 See Appendix for Table 14: LGAs Mean rating, MML, DML, and State Mean Score in Literacy learning
domain

Figure 12: Mean Scores of Pupils Disaggregated by Sex and Locational Residence Relative to Mastery Levels: Numeracy domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
NUMERACY
Male
Female

Fig.13, Shows that in 35 percent of the LGAs, Boys achieved the Desired Mastery Level in
Numeracy domain while Girls from only 22 percent of the LGA performed at the Desired
Mastery Level.
The Minimum Mastery Levels in numeracy were achieved by Boys from 9 and Girls from 12 LGAs. And
in 26 percent of the LGAs, both boys and Girls performed below the Minimum Mastery Level2
.
2 See Appendix for Table 15: LGAs Mean rating, MML, DML, and State Mean Score in Numeracy learning domain

Figure 12: Mean Scores of pupils Disaggregated by Sex and Locational Residence Relative to Mastery Levels: Life Skill domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Life Skill
Male
Female
STATE MEAN SCORE
MML
DML

Fig.13, Shows that performance below the Minimum Mastery Level was recorded for both Boys and Girls from 22 percent or 5 LGAs.
Girls from 6 LGAs achieved the Desired Mastery Level in Life Skills whereas performance at the Desired Mastery Level was recorded for Boys
from only 4 LGAs. A greater number of Boys from 14 LGAs achieved the minimum mastery Level while Girls from 11 LGAs achieved the
Minimum Mastery Level3.
3 See Appendix for Table 15: LGAs Mean rating, MML, DML, and State Mean Score in Life Skills learning domain

Figure 12: Mean Scores of Students Disaggregated by Sex and Locational Residence Relative to Mastery Levels: English Language domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0 ENGLISH
Male
Female

Fig.13, Shows that the Desired Mastery Level was achieved by Boys from only 3 LGAs in
contrast to Girls from 6 LGAs achieving the Desired Mastery Level in English Language.
Boys from 14 LGAs achieved the Minimum Mastery Level compared to Girls from 10 LGAs
achieving the MML in English Language. Girls from 7 LGAs performed poorly below the MML
while boys from 6 LGAs performed similarly4.
4 See Appendix for Table 16: LGAs Mean rating, MML, DML, and State Mean Score in English Language learning
domain

Figure 12: Mean Scores of Students Disaggregated by Sex and Locational Residence Relative to Mastery Levels: Mathematics domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
MATHEMATICS
Male
Female

Fig.13, Shows that Boys from 7 LGAs achieved the desired mastery level in Mathematics while
girls form 5 LGAs performed at the Desired Mastery Level. Girls from 12 LGAs achieved the
Minimum Mastery Levels compared to Boys from 9 LGAs achieving the MML. Again, Boys
from 7 LGAs achieved below the MML whereas Girls from 6 LGA similarly performed so
poorly-below the MML5
.
5 See Appendix for Table 17: LGAs Mean rating, MML, DML, and State Mean Score in Mathematics learning
domain

Figure 12: Mean Scores of Students Disaggregated by Sex and Locational Residence Relative to Mastery Levels: General Science domain
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
GENERAL SCIENCE
Male
Female
STATE MEAN SCORE
MML
DML

Fig.13, Shows that 44% or 9 LGAs have their Boys and Girls achieved the Desire Mastery Level.
5 LGAs have their boys fall within the control limits and 6 LGAs have their girls fall within the
control limits, this implies that they have achieved the minimum mastery level in literacy
learning domain corresponding to 22% and 26% respectively. But, 8 LGAs have their boys
achieved below the lower control limit, and 8 LGAs have their girls achieved below the lower
control limit corresponding to 34% and 34% respectively6.
2.1.1. Part 3: Gender Analysis of Other Indicators of Learning Performance
In addition to the learning outcome from the administered test questions, other indication of
performance can be inferred from the School Census Report (administrative data) when
disaggregated along gender lines. A peek into the data on repeaters and dropout pattern or
trend can reveal more information about gender difference in learning performance.
REPEATERS
In Figure 13; we observed that there are more boys than girls who are repeating a class (or
more) of schooling. The repetition of a school class can be considered as a form of support for
low performance as it seeks to adapt the curriculum to pupil/student performance. The
requirement for a particular student to repeat a class usually follows a formal assessment or
informal decision by the teachers when pupil/student has poor results in crucial subjects.
Statistics from the State school census report 2011-2012 shows that the number of repeaters
is higher for both males and females in the primary than in the secondary schools, figure 13
shows that the number of repeater gradually decline as students’ progress from one
education level to another or from a lower class (year) to a higher class (year).
6 See Appendix for Table 18: LGAs Mean rating, MML, DML, and State Mean Score in General Science learning
domain

Figure 13: Public Repeaters by Gender and Grade
Source: State school census report 2011-2012
Dropout Rate
There are few noteworthy gender distinctions with respect to participation rates in primary
and secondary education. The differences emerge at the end of compulsory education.
Despite significant progress in increasing primary and secondary enrolment; completion rates
continue to be low. In Figure 14, it can be observed that more girls than boys are dropping
out, with the exception of primary 1. As observed from the figure more girls dropout from
primary 2, 4, 5 and 6. Similarly, in the same pattern occurred at the secondary school levels
except SS1 and 2. A 2013 study by Arbitrage consult on Out-Of-School children in Rivers state
reveals that poverty and teenage pregnancy are the most significant reasons responsible for
pupils and students dropping out of school; accounting for 91 percent of school dropout7
.
Some interesting pilot programs to address the drop out problem should be introduced for
possible replication.
7 See Out-Of School Children in Rivers State by Arbitrage Consult Limited

Figure 14: Dropout rate by class and gender
0
100
200
300
400
500
600
700
PRY1 PRY 2 PRY 3 PRY 4
PRY 5
PRY 6
JSS 1
JSS 2
JSS 3
SSS 1
SSS 2
Drop out rate by Class and Gender
Boys
Gils
Source: State school census report 2011-2012

Teachers
The staff strength of the public schools in Rivers state as captured in the State School Census
Report 2011-2012 stood at 7,573 teachers for primary school, 4,975 teachers for junior
secondary and 2,968 teachers for senior secondary, giving a total of 15516 teachers. 8This
Statistics also show that there more female teacher than male, with a total of 8540
accounting for 55% as against 6976 or 45% for males’. However, table 8 and Figure 15 showed
that there are more male teachers in 8 LGAs at the primary school level, 15 out of 23 LGA
have more male teachers at the Junior Secondary Level, and 18 out of 23 LGA have more male
teachers at the Senior Secondary School levels. This disparity in the number of teachers
employed with emphasis on their gender can significantly affect the performance of students.
Basu and Chakroborty (1996) reported that student taught by male teacher achieve higher
than those taught by female teachers.
Table 8: Distribution of Public School Teachers by LGAs and Gender
8 See Appendix, TABLE 12A and 12B: Staff Strength of Public Schools by LGAs and Gender
Primary School
teachers
Junior secondary
school teacher
Senior Secondary
School teachers
LGEA M
Male
Fem
Female
ale
Total
Totl
Male
Male
Fem
Female
ale
Total
Total
Male Fem
Female
ale
Total
Total
ABUA/ODUAL 194 175 369 93 26 119 62 16 78
AHOADA EAST 100 246 346 126 105 231 64 15 79
AHOADA WEST 140 126 266 113 60 173 100 22 122
AKUKU-TORU 38 82 120 86 64 150 24 11 35
ANDONI 135 95 230 101 24 125 38 8 46
ASALGA 51 124 175 33 25 58 31 30 61
BONNY 33 106 139 14 13 27 39 32 71
DEGEMA 85 150 235 76 67 143 27 17 44
ELEME 57 149 206 58 135 193 67 44 111
EMOHUA 196 166 362 191 164 355 122 32 154
ETCHE 298 205 503 181 76 257 149 42 191
GOKANA 201 131 332 132 52 184 143 27 170
IKWERRE 102 358 460 159 177 336 105 25 130
KHANA 413 177 590 190 63 253 178 38 216
OBIO/AKPOR 50 644 694 139 682 821 239 301 540
OGU/BOLO 49 53 102 14 21 35 9 3 12
OKRIKA 52 191 243 65 115 180 42 17 59
OMUMA 75 119 194 45 14 59 24 5 29

ONELGA 180 255 435 228 209 437 170 78 248
OPOBO/NKORO 59 68 127 39 17 56 2 1 3
OYIGBO 38 247 285 39 65 104 38 32 70
PHALGA 80 856 936 121 475 596 155 253 408
TAI 128 80 208 62 21 83 76 15 91
Grand Total 2756 4805 7573 2305 2670 4975 1904 1064 2968

Figure 15: Distribution of Public School Teachers by LGAs and Gender
0
200
400
600
800
1000
1200
1400
1600
1800
Distribution of Public School Teachers by LGAs and Gender
Male
Female

51 Chapter three | Arbitrage Consult Limited
Chapter three
Factors Associated with Gender Differences
Rivers state has demonstrated commitment in promoting Gender equity, as demonstrated in
her educational policy. However, a number of social and institutional barriers remain, which
impair boys’ and girls’ performance in school, and combine to prevent girls from completing
primary and secondary school in equal numbers to boys or young women from reaching
university in equal numbers to young men. Girls continue to lag behind their male classmates
in terms of completion rates and in some cases; their overall performance in school.
Attendance rates for girl students are generally lower, which could translate into lower scores
on national examinations. We will examine the causes of this disparity, which persist despite
the strong policy framework in place, by estimating the factors associated with gender
differences using single-equation regression model (ANOVA model) and dummy variables to
point out differences, if they exist, between boys and girls performance in the learning
domain.
Table 9: Some Selected Factors that Influence Learning Performance by Gender
S/N Factors Categories Total Score of
Boys with
respect to a
factor
Total Score of
Girls with
respect to a
factor
1 REGION Urban=1
Rural=0
180.5
(0)
184.8
(1)
194.2
(0)
186.6
(1)
2 Home Learning
Environment
Going to library
or attending
extra-mural
lession=1
Others=0
182.7
(0)
182.1
(1)
188.5
(0)
193.2
(1)
3 Feeding Yes=1
No=0
177.5
(0)
188.4
(1)
191.1
(0)
189.1
(1)
4 Teacher Attitude Liking
teacher=1
Other=0
182.2
(0)
187.4
(1)
185.9
(0)
244.5
(1)

Table 9 above illustrates gender disparity in learning with respect to some selected factors;
the factors are regressed against total score of students, the essence is to show clearly the
disparity in boy and girls performance (total score) relative to these factors using dummy
variables. For example, from the table we can observe that urban boys and girls perform
relatively better than their rural counterpart, and girls perform better than boys with respect
to this factor in the respective urban and rural categories; implying that these factors impact
on the performance of boys and girls differently, and could be a reason for the disparity in
performance.
Social and Economic Factors which Influence Girls’ and Boys’ Performance and Behavior
In Rivers state, socio-economic factors affect pupil and students performance in schools.
Studies have shown that students with better parental background tend to perform better
than students from poor homes. Other factors such as ethnic origin and language intersect
with gender to influence educational performance and indeed, these factors become more
significant as students grow older.9
Obstacles to high academic performance include poverty,
family size and parents in unskilled or low skilled employment, while enhancements include
higher social class level, being a girl and having educated parents (Sammons, 1995). In table 9
above and Volume IV (Determinants of learning performance), we highlighted that
pupilsstudent parental background such as parental education can influence the
performance of pupils. Interestingly, membership of a minority ethnic group can be
advantageous or disadvantageous, depending on its specific cultural disposition towards
education. Nevertheless, other factors or combination of factors are also significant; such as
the influence of both gender and home learning environment (HLE) on the pupil performance,
where HLE involves frequency of reading to the child, visiting the library, teaching songs,
nursery rhymes, playing with letters and numbers, drawing/painting etc.
Gender Themes in Current Educational Practice
Gender equity can only be achieved through in-depth curriculum adjustment that gives
credence to Gender equality. The official curriculum concerns the subjects that are taught in
schools and their contexts. Rivers state adopted the National curriculum based on the concept
of basic education for all. Curriculum theorist Paechter (2000) points out that though official
curricula tend rarely to address gender equality, they tend to imply certain gender
assumptions; for example, that ‘power’ subjects (e.g. Numeracy, mathematics and science)
will attract males and others (e.g. languages, literature) females. This means that the content
of different subjects attracts boys and girls on the basis that’ ‘this is what proper girls or boys
do’. A number of recurrent themes or topics are to be found in the literature on gender and
education which deal primarily with educational practice (or what goes on in schools). These
are the curriculum (official and hidden). The Official curriculum is concern with school reading
materials, subject preference and choice, motivational and psychological factors of students,
school organization and management, teacher attitudes, assessment, teaching as a
9 See Determinants of Learning Performance

profession, co-education and single-sex settings, and the problem of boys. On the Hidden
curriculum, on the other hand, concerns everything that happens in the school that is not
‘official’, for example, social relations in the classroom or playground, friendships,
relationships between teachers and pupils, levels of bullying and harassment and so on.
Motivational and Psychological Issues
Self-esteem and confidence is a major determinant of gender parity in pupils and student
performance, although this issue is psychological, it has played a major role in determining
how pupils performed thus, studies of gender difference in student ‘self-concept’ have been
of much interest. However, research evidence is inconclusive with findings ranging from little
evidence of difference to males having far higher self-images. Student ‘motivation’ to do well
at school is also an important factor. For example, a study suggests that (some) boys’
underperformance is associated with their generally negative attitudes towards schools, in
particular their less positive relationships with teachers, lack of feeling of well-being while in
school, and their poor attitude towards schoolwork. In Table 9 and Volume IV of this study,
we established that pupils attitude toward their teachers could affect their performance, it
was stated that, “when a pupil likes his or her teacher, it enhances his/her performance by 2
percent and this is statistically significant at 5 percent”.
School Environment
Evidence has emerged that students’ achievement levels are much influenced by the school
environment and, in particular, the daily management and organisational procedures of
schools which are frequently reliant on gender as a management tool. Girls and boys may be
separated for classroom registers, classroom activities and team sports, for example. Dress
codes may be different for boys and girls (trousers for boys, skirts for girls) and also for
members of staff (Scott, 2007). Such practices are criticized because they have little
educational benefit save as deliberate ‘marker’ of gender difference. On the other hand,
investment of time in the development of institutional policies and associated staff
development (SEED, 2006) which address, for example, bullying and sexual harassment have
been shown to be effective in raising pupil and staff consciousness that such behaviour is
demeaning and unacceptable.
Teacher Attitudes
Teachers as basic tool in curriculum implementation remain a very crucial factor that
influence students’ experience and achievement, and continuing educational development.
These is no longer achievable since teachers are accorded with little or no respect in the
society due to insufficient fund, lack of motivation incentives, delay in salary payment among
others all these affects teachers activities, causing psychological and emotional trauma which
in turn affect his output. Simpsom and Troost (1982) found out that teacher attitude is an
important factor that determines achievement and enrolment of students. The argument is
that teacher attitudes influence student attitudes (Aiken 1970, Larson 1983), and that student
attitudes have a powerful influence on learning (Evans 1965, Khan and Weiss 1973). Indeed a

number of researchers have found a significant correlation between teacher attitude and
student achievement (Begle 1979, Bishop and Nickson 1983, Schofield 1981). The influence
exacted by the teachers on their students as a gender implication, since when teachers
believe that they treat their students equally, they are more likely to chastise male students
and pay them more attention, while at the same time creating greater dependency in their
female students. Hence, a variety of countries studies or studies in other countries have
shown that both male and female teachers tend to encourage passivity and conformity in
their female pupils and students while at the same time valuing independence and
individuality in their male students (Golombok & Fivush, 1994). They thus allow boys to be
naughtier because they think it natural and, for the same reasons, expect girls to take up
‘domestic’ related activities such as caring for others or cleaning-up in the classroom. Girls are
generally perceived to be more cooperative and malleable, and boys more confident and able.

55 Chapter Four | Arbitrage Consult Limited
Chapter Four
Strategies for Addressing Gender Differences
In other to achieve the MDG’s goals of attaining gender parity Index GPI of between 0.97 and
1.03, Rivers state in recent times has put in place, policies aimed at reducing Gender
inequality and enhancing learning performance in public schools in the state. In spite of these
policies, gender equity has remained a daunting challenge, as it is rare to find public schools
with written policies on gender equality. In this chapter, we highlight some strategic
interventions aimed at addressing underachievement. Some of these strategies include:
 Study support
 Mentoring
 Building confidence
 Self-esteem
 Developing systems of target-setting for individual pupils
And their implications for gender differences because more boys than girls are perceived to
be under-achieving.
Strategies in Relation To Learning, Teaching and Assessment
Teaching and Learning Processes
The reason why boys and girls learn in different ways may be due to their physiological
pattern; which may explain the variations in males and females performance in various
learning domains. Understanding some broad patterns that are evident in the way in which
girls and boys prepare themselves for learning and engage in learning in the classroom is
fundamental to addressing gender issues. (Noble and Bradford 2000) state that “a range of
approaches can be used to confront issues such as boys’ work habits, their need to be fully
engaged in the classroom and their reported limited concentration”. Such approaches include:
using activity-based and experiential tasks in learning; the understanding and development of
specific skills such as reading and literacy and revision and study skills; focused, clear and
time-bound; less written and more oral work, also using competitive dimension such as:
quizzes and games, ICT and audio visual support and a variety of formats, e.g. diagrams and
images to supplement text. It is essential to use cooperative/interactive methods of learning
to support girls in their learning. In addition, understanding female pupils and students
response mechanism is paramount, due to the fact that girls respond better to feedback that
is exciting but gives precise guidance for improvement as well as praise.

Assessment Practices
To effectively reduce gender inequality in learning performance in Rivers state, the
government may need to implement a state performance assessment monitoring systems
disaggregated by gender and the interaction between gender as well as the assessment
process itself.
Interaction Patterns in the Classroom
To effectively address gender difference in learning performance, it is essential to study and
understand the interaction pattern of gender and classroom. The key issues that will emerge
include: how the relative Silence of boys and girls affects classroom dynamics; differences in
the nature and quality of interaction, with teachers tending to have more negative
interactions with boys; and teachers’ reinforcement of gender stereotypes, both through the
formal curriculum and informal interactions.
Pupils and Students Attitude and Motivation
In the classroom pupilsstudents have gender-differentials patterns of interaction and
motivation styles and how this pattern affects the students and pupils has to be recognised. It
is important to note that this gender-differential pattern of interaction has implications for
school behavior and overall policies; (Galloway, 1998) states that “girls tend to have higher
levels of task orientation (where the focus is on the achievement itself), particularly in English,
than boys. While boys have higher levels of ego orientation (the concern is their standing with
other people) in both English and mathematics. Identifying these gender-differential patterns
will enable policy makers to implement policy that will reduce gender inequality in learning
performance in public schools.
Classroom Organization
Two complementary classroom organizational strategies have been developed to manage
gender inequality in various learning domains: the use of mixed gender groupings and single
gender groupings/classes.
Mixed-Gender Groups
Seating arrangement in the classroom is a critical factor in addressing gender inequality in
learning performance; some classroom seating arrangement prevents boys and girls from
interacting; particularly detaching the boys from the girls. (Raphael Reed, 1999) reveals that
the function of girls is to exercise their ‘civilizing’ influence in ‘supporting’ boys’. Also, (Epstein
1998) states that “it is important to use girls to police, teach, control and civilise boys”. The
expectation that at least some girls should play this role raises questions about their own
opportunities. The use of girls as a tactic in the control of boys is implicit in a range of
strategies such as seating policies, mixed gender pairs and groups.
Single Gender Groups and Classes
To tackle boy’s low performance in public schools, the use of single gender and classes’
strategy has been suggested. According to Woodhead, (1996), “during adolescence boys are

distracted by the presence of girls and they can engage in behaviours that detract them, from
their learning”. However, in some instances, this strategy has been adopted to support the
learning of both boys and girls. The importance of adopting single gender teaching includes:
improving girls’ opportunities, addressing boys’ low performance and behavior problems. The
impact of this strategy depends to a certain extent on whether the focus is on pupils in the
‘top’ or ‘bottom’ sets.
Role Models
Statistics from the Monitoring Learning Achievement 2013 survey reveals that there are more
female than are male teachers in public schools in Rivers state. Interestingly, women have
always been in the majority in primary schools. This has raised the issue of a lack of male role
models for boys at various levels in the education system. Efforts have to be made to attract
more men into teaching, especially into primary Schools.
Mentoring
Mentoring is an effective approach in managing boys’ low performance in primary and
secondary schools. This is because at that stage pupils and student are easily influenced by
what they see, feel and sense. Mentoring comes in different forms, such as the practice of
peer counselling (Ryder, 1998), using reading partners and groups (Noble, 1998) and subject-
specific support (Penny, 1998). Studies have shown that mentoring can be adopted for a
range of reasons, such as: focusing on specific pupils, frequently low performance by boys, to
tackle motivation and confidence and to support pupils on specific exams and expected
results. Applying mentoring approach can result in a number of positive outcomes for both
boys and girls. Nonetheless, there were limitations, most especially relating to structure, time,
lack of sufficient numbers of trained mentors and high mobility rate of teachers from one job
to another.
School Culture and Participation in Development
School culture is an important factor in promoting gender equity in learning performance.
Government and policy makers must be active in promoting the development of whole school
strategies to establish and enhance a positive culture. In the Monitoring Learning Survey of
2013, most pupils complain of bullying and harsh academic environment. Approaches to
promote positive behaviour and to create greater pupil participation in the School community
life style and decision-making processes of school should be encouraged. Pupils and their
parents need to be involved in both the decision making process and in discussing policy
formulation for the school. Parents should also be seen as having an important role to play
both in supporting pupil learning and in contributing to public activities to raise the awareness
of gender equity.

58 Chapter Five | Arbitrage Consult Limited
Chapter Five
Conclusion and Recommendations
 Developing gender policies and practices requires that gender issues are considered in
all aspects of school development, as well as being a specific focus pursued through
discrete strategies. Such eclectic or all-embracing approaches might be mirrored in
local Government and the state education initiatives. The invisibility of gender in many
policy documents has been surprising, given the highly gendered patterns of pupil
experience. Our findings showed that:
 There are gender gaps in Rivers state public schools; these gaps exist in enrollments,
learning performance, repeaters rate, dropout rate and teaching manpower.
 The Rivers state government does not discriminate against females in the field of
education. However, factors that can be directly responsible for gender disparity in
public schools include: socio-economic influences, gender themes in current
educational practices, motivational and psychological issues, school environment, and
teacher attitude.
 From current enrollment statistics, Gender Parity Index (GPI) is 1.07 in the 2011/2012
academic against GPI of 0.97 in 2005. Since a GPI of 1 indicates parity between the
sexes; a GPI that varies between 0 and 1 typically means a disparity in favour of males;
whereas a GPI greater than 1 indicates a disparity in favour of females. This implies
that Rivers state has achieved the MDG’s goals of attaining gender parity Index GPI of
between 0.97 and 1.03
 More boys than girls are perceived to be underperforming; this is evidenced by the
fact that there are more boys than girls who are repeating a class (or more) of
schooling. Also results from the 2013 Monitoring Learning Achievement (MLA) showed
that except in numeracy and mathematics; generally, girls performed better than boys
across the various Local Government Areas.
 Dropout rate is higher for girls in both primary and secondary schools

Recommendations:
 Government should organise workshops for capacity building and policy advocacy on
gender issues, which will create the needed awareness in this regards. Also due to the
high dropout rate for female; it is important for the state government to setup,
implement, monitor an intervention programs to address the females drop out
problem. For example, a program to eradicate child labour and promote Gender and
Family values. This catch-up program should be linked to a special curriculum that
facilitates those girls and boys to finish their primary and secondary school.
 Use of gender-sensitive textbooks and curriculum for girls will help bridge the gender-
gap.
 River state government schools implement a state performance assessment
monitoring system that can monitor performance by gender and the interaction
between gender and the assessment process itself.

60 Reference | Arbitrage Consult Limited
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63 Appendix | Arbitrage Consult Limited
Appendix
Table 9: TABLE SUMMARY FOR PRIMARY SCHOOLS
L.G.A SAMPLE
D
LITERAC
Y
NUMERAC
Y
LIFE
SKILLS
TOTAL AVERAG
E
TOTAL AVERAG
E
TOTAL AVERAG
E
1 ABUA UDUA 685 45,959 67.09343 39,368 57.47153 51,487 75.1635
2 AHOADA EAST 538 29,976 55.7175 21,070 39.164 35,708 66.372
3 AHOADA WEST 712 33,718 47.3567 33,683 47.3076 39,132 54.9607
4 AKUKU-TORU 256 15,561 60.7852 11,938 46.6328 18,071 70.5898
5 ANDONI 719 35,038 48.7316 34,540 48.0389 56,275 78.2684
6 ASA 319 20,391 63.9216 12,248 38.395 22,426 70.3009
7 BONNY 317 19,922 62.8454 21,332 67.2934 22,253 70.1987
8 DEGEMA 284 16,724 20.2262 10,054 35.4014 20,454 72.0211
9 ELEME 300 16,837 56.1223 17,340 57.8 21,387 71.29
10 EMOHUA 713 38,143 53.4965 32,960 46.2272 49,392 69.2735
11 ETCHE 593 14,827 25 10,021 17 16,504 28
12 GOKANA 617 36,182 58.6418 30,284 49.0827 33,813 54.8023
13 IKWERE 653 41,410 63.415 37,271 57.0766 49,913 76.4364
14 KHANA 1168 67,092 57.4418 56,538 48.4058 81,230 69.5462
15 OBIO/AKPOR 709 44,770 63.1453 31,077 43.8322 58,532 82.5557
16 OGU/BOLO 114 7,785 68.2895 6,989 61.307 8,166 71.6316
17 OKRIKA 509 31,068 61.0373 27,637 54.2967 39,447 77.499
18 OMUMA 208 14,928 71.7692 10,744 51.6539 13,536 65.0769
19 ONE 688 41,684 60.5872 33,835 49.1788 50,671 73.6497
20 OPOBO NKORO 209 13,131 62.8278 11,692 55.9426 14,817 70.8947
21 OYIGBO 341 24,228 71.0499 17,224 50.5103 24,778 72.6628
22 PORT-HARCOURT 763 46,692 61.195 37,509 49.16 55,033 72.127
23 TAI 447 28,727 63.3848 18,792 42.179 32,646 73.1876
STATE TOTAL 11862 684793 564146 815671
STATE MEAN
SCORE
57.72998 47.559096 68.7633
6

Table 10: TABLE SUMMARY FOR SECONDARY SCHOOLS
L.G.A ENGLIS
H
MATHEMATIC
S
GENERA
L
SCIENCE
SAMPLE
D
TOTAL AVERAG
E
TOTAL AVERAG
E
TOTAL AVERAG
E
1 ABUA UDUA 8,884 55.8742 6,361 40.006 11,469 72.132 159
2 AHOADA EAST 7,450 38.8021 7,977 41.547 12,569 65.464 192
3 AHOADA WEST 14,170 66.8396 8,268 39 11,770 55.519 212
4 AKUKU-TORU 3,969 44.5955 5,446 61.191 4,389 49.315 89
5 ANDONI 4,868 29.503 8,107 49.133 7,708 46.715 165
6 ASA 7,578 56.9774 5,719 43 8,738 65.699 133
7 BONNY 4,935 53.6413 5,880 64 2,809 30.533 92
8 DEGEMA 4,326 38.2832 3,719 32.856 7,383 65.336 113
9 ELEME 3,658 51.5211 3,443 48.493 5,138 72.366 71
10 EMOHUA 16,307 52.266 14,841 47.567 20,200 64.744 312
11 ETCHE 9,085 47.8158 8,206 43.19 12,407 65.3 190
12 GOKANA 8,152 49.1084 7,065 42.56 7,856 47.325 166
13 IKWERE 5,513 45.4594 5,868 48.9 4,320 36 120
14 KHANA 11,255 36.7811 13,110 42.843 16,014 52.333 306
15 OBIO/AKPOR 15,349 54.0458 10,492 36.944 20,657 72.736 284
16 OGU/BOLO 2,563 58.25 2,471 56.159 3,105 70.562 44
17 OKRIKA 4,927 55.3596 5,287 59.405 6,584 73.978 89
18 OMUMA 3,625 47.0779 2,866 37.221 3,025 39.286 77
19 ONE 13,918 52.3233 12,723 47.831 17,713 66.59 266
20 OPOBO NKORO 2,707 39.8088 2,573 37.838 3,105 45.662 68
21 OYIGBO 10,524 50.596 8,511 40.918 12,629 60.716 208
22 PORT-
HARCOURT
14,135 72.8608 6,958 35.866 12,999 67.005 194
23 TAI 6,953 46.9797 6,436 43.487 8,617 58.223 148
STATE TOTAL 184,851 162,327 221,204 3,698
STATE MEAN
SCORE
54.836 48.154 65.62

Table 11: MEAN PERFORMANCE BY GENDER AND LGAs
Abua/OD
UA
Ahoada
East
Ahoada
West
Andoni Akuku
Toru
Asari Toru Bonny Degema
Literacy Male 67.39655
2
56.8112
45
45.7027
03
54.616863
91
66.896551
72
62.5 61.1666
67
64.75
Female 67.53571
4
58.3855
42
50.5818
97
56.005068 70.081818
18
68.496855
35
62.9527
56
64.6346
15
Numera
cy
Male 58.44985
7
40.3035
02
47.8419
96
47.651163 46.226562
5
39.682432
43
64.8412
7
40.1764
71
Female 57.71940
3
41.52 45.9956
9
48.637462 49.591666
67
40.094339
62
68.6692
91
37.9230
77
Life skill Male 79.72254
3
72.0164
61
55.3575
88
69.502618 70.643410
85
68.516339
87
70.3492
06
71.4411
76
Female 80.95223
9
73.9618
64
53.9137
93
90.349544 72.672131
15
71.945783
13
70.8503
94
73.3846
15
Eleme Emohua Etche Gokana Ikwere Khana Obio/Akp
or
Onelga Ogu/Bol
o
Okrika
56.51655
6
52.94884
9
24.93664 55.41907
5
65.91147
5
56.84051 61.782738 61.90113 67.23636
4
64.732
56.53409
1
53.93769
5
25.11587 53.66051
7
61.58092
5
59.46748 64.894595 59.91212
1
69.27118
6
57.25
57.11920
5
46.22762
1
16.50519
8
46.67052 60.57049
2
48.43381
2
44.791667 73.25633
8
62.65454
5
55.064
57.09090
9
46.23676 17.27859
1
40.59409
6
54.17002
9
48.67073
2
43.433604 74.29216
9
60.05084
7
53.35
71.51049 68.66496
2
27.45246
5
61.65606
9
74.82026
1
69.94258
4
83.770833 48.58427 72.36363
6
79.856
71.54487
2
69.69158
9
28.23309
7
59.02214 78.54069
8
69.73374 82.344173 49.81626
5
70.94915
3
74.93461
5
Omuma Opobo/Nkoro Oyigbo Port
Harcourt
Tai
71.706667 62.22549 68.715976 60.481268 63.955556
71.804511 63.037736 69.97093 61.639423 65.479821
49.546667 59.029412 50.911243 48.971182 41.955157
52.842105 48.637462 50.755814 50.329327 42.541667
64.52 52.443396 69.35503 72.56196 70.282511
65.390977 90.349544 69.05814 73.038462 46.944444

Table 12A: Academic Staff Strength of Public Schools by LGAs and Gender
Primary School
teachers
Junior secondary
school teacher
Senior Secondary
School teachers
LGEA Male Female Total Male Female Total Male Female Total
ABUA/ODUAL 194 175 369 93 26 119 62 16 78
AHOADA EAST 100 246 346 126 105 231 64 15 79
AHOADA WEST 140 126 266 113 60 173 100 22 122
AKUKU-TORU 38 82 120 86 64 150 24 11 35
ANDONI 135 95 230 101 24 125 38 8 46
ASALGA 51 124 175 33 25 58 31 30 61
BONNY 33 106 139 14 13 27 39 32 71
DEGEMA 85 150 235 76 67 143 27 17 44
ELEME 57 149 206 58 135 193 67 44 111
EMOHUA 196 166 362 191 164 355 122 32 154
ETCHE 298 205 503 181 76 257 149 42 191
GOKANA 201 131 332 132 52 184 143 27 170
IKWERRE 102 358 460 159 177 336 105 25 130
KHANA 413 177 590 190 63 253 178 38 216
OBIO/AKPOR 50 644 694 139 682 821 239 301 540
OGU/BOLO 49 53 102 14 21 35 9 3 12
OKRIKA 52 191 243 65 115 180 42 17 59
OMUMA 75 119 194 45 14 59 24 5 29
ONELGA 180 255 435 228 209 437 170 78 248
OPOBO/NKORO 59 68 127 39 17 56 2 1 3
OYIGBO 38 247 285 39 65 104 38 32 70
PHALGA 80 856 936 121 475 596 155 253 408
TAI 128 80 208 62 21 83 76 15 91
Grand Total 2756 4805 7573 2305 2670 4975 1904 1064 2968

Table 12B: Summary of Academic Staff Strength of Public Schools by LGAs and Gender
LGA Male Female
ABUA/ODUAL 349 217
AHOADA EAST 290 366
AHOADA WEST 353 208
AKUKU-TORU 148 157
ANDONI 274 127
ASALGA 115 179
BONNY 86 151
DEGEMA 188 234
ELEME 182 328
EMOHUA 509 362
ETCHE 628 323
GOKANA 476 210
IKWERRE 366 560
KHANA 781 278
OBIO/AKPOR 428 1627
OGU/BOLO 72 77
OKRIKA 159 323
OMUMA 144 138
ONELGA 578 542
OPOBO/NKORO 100 86
OYIGBO 115 344
PHALGA 356 1584
TAI 266 116
Grand Total 6963 8537

Table 13: Gender parity ratio across LGA
LGA Gender
ratio
Parity
ABUA/ODUAL 1.032849 1
AHOADA EAST 0.997962 1
AHOADA WEST 0.944272 1
AKUKU-TORU 1.102789 1
ANDONI 0.940717 1
ASALGA 0.930582 1
BONNY 1.065939 1
DEGEMA 0.971656 1
ELEME 1.157858 1
EMOHUA 0.94579 1
ETCHE 1.029822 1
GOKANA 1.040854 1
IKWERRE 1.010268 1
KHANA 1.03288 1
OBIO/AKPOR 1.236886 1
OGU/BOLO 1.027586 1
OKRIKA 1.005176 1
OMUMA 1.018772 1
ONELGA 0.992986 1
OPOBO/NKORO 0.986842 1
OYIGBO 1.05981 1
PHALGA 1.252895 1
TAI 1.010367 1
GPI 1.06598 1
Table 14: LGAs Mean rating, MML, DML, and State Mean Score in Literacy
learning domain
LITERACY
Male Female STATE MEAN SCORE MML DML
Abua/ODUA 67.4 67.5 57.7 53.9 61.5
Ahoada East 56.8 58.4 57.7 53.9 61.5
Ahoada West 45.7 50.6 57.7 53.9 61.5
Andoni 54.6 56.0 57.7 53.9 61.5
Akuku Toru 66.9 70.1 57.7 53.9 61.5
Asari Toru 62.5 68.5 57.7 53.9 61.5
Bonny 61.2 63.0 57.7 53.9 61.5
Degema 64.8 64.6 57.7 53.9 61.5
Eleme 56.5 56.5 57.7 53.9 61.5

Emohua 52.9 53.9 57.7 53.9 61.5
Etche 66.3 66.9 57.7 53.9 61.5
Gokana 55.4 53.7 57.7 53.9 61.5
Ikwere 65.9 61.6 57.7 53.9 61.5
Khana 56.8 59.5 57.7 53.9 61.5
Obio/Akpor 61.8 64.9 57.7 53.9 61.5
Onelga 61.9 59.9 57.7 53.9 61.5
Ogu/Bolo 67.2 69.3 57.7 53.9 61.5
Okrika 64.7 57.3 57.7 53.9 61.5
Omuma 71.7 71.8 57.7 53.9 61.5
Opobo/Nkoro 62.2 63.0 57.7 53.9 61.5
Oyigbo 68.7 70.0 57.7 53.9 61.5
Port Harcourt 60.5 61.6 57.7 53.9 61.5
Tai 64.0 65.5 57.7 53.9 61.5
Table 15: LGAs Mean rating, MML, DML, and State Mean Score in Numeracy
learning domain
NUMERACY
LGA Male Female STATE MEAN SCORE MML DML
Abua/ODUA 58.4 57.7 49.6 44.9 54.3
Ahoada East 40.3 41.5 49.6 44.9 54.3
Ahoada West 47.8 46.0 49.6 44.9 54.3
Andoni 47.7 48.6 49.6 44.9 54.3
Akuku Toru 46.2 49.6 49.6 44.9 54.3
Asari Toru 39.7 40.1 49.6 44.9 54.3
Bonny 64.8 68.7 49.6 44.9 54.3
Degema 40.2 37.9 49.6 44.9 54.3
Eleme 57.1 57.1 49.6 44.9 54.3
Emohua 46.2 46.2 49.6 44.9 54.3
Etche 44.5 46.8 49.6 44.9 54.3
Gokana 46.7 40.6 49.6 44.9 54.3
Ikwere 60.6 54.2 49.6 44.9 54.3
Khana 48.4 48.7 49.6 44.9 54.3
Obio/Akpor 44.8 43.4 49.6 44.9 54.3
Onelga 73.3 74.3 49.6 44.9 54.3
Ogu/Bolo 62.7 60.1 49.6 44.9 54.3
Okrika 55.1 53.4 49.6 44.9 54.3
Omuma 49.5 52.8 49.6 44.9 54.3
Opobo/Nkoro 59.0 48.6 49.6 44.9 54.3
Oyigbo 50.9 50.8 49.6 44.9 54.3

Port Harcourt 49.0 50.3 49.6 44.9 54.3
Tai 42.0 42.5 49.6 44.9 54.3
Table 16: LGAs Mean rating, MML, DML, and State Mean Score in Life Skill
learning domain
LIFE SKILL
Abua/ODUA 79.7 81.0 70.9 67.0 74.8
Ahoada East 72.0 74.0 70.9 67.0 74.8
Ahoada West 55.4 53.9 70.9 67.0 74.8
Andoni 69.5 90.3 70.9 67.0 74.8
Akuku Toru 70.6 72.7 70.9 67.0 74.8
Asari Toru 68.5 71.9 70.9 67.0 74.8
Bonny 70.3 70.9 70.9 67.0 74.8
Degema 71.4 73.4 70.9 67.0 74.8
Eleme 71.5 71.5 70.9 67.0 74.8
Emohua 68.7 69.7 70.9 67.0 74.8
Etche 72.4 74.7 70.9 67.0 74.8
Gokana 61.7 59.0 70.9 67.0 74.8
Ikwere 74.8 78.5 70.9 67.0 74.8
Khana 69.9 69.7 70.9 67.0 74.8
Obio/Akpor 83.8 82.3 70.9 67.0 74.8
Onelga 48.6 49.8 70.9 67.0 74.8
Ogu/Bolo 72.4 70.9 70.9 67.0 74.8
Okrika 79.9 74.9 70.9 67.0 74.8
Omuma 64.5 65.4 70.9 67.0 74.8
Opobo/Nkoro 52.4 90.3 70.9 67.0 74.8
Oyigbo 69.4 69.1 70.9 67.0 74.8
Port Harcourt 72.6 73.0 70.9 67.0 74.8
Tai 70.3 46.9 70.9 67.0 74.8

Table 17: LGAs Mean rating, MML, DML, and State Mean Score in English
Language learning domain
ENGLISH LANGUAGE
Abua/ODUA 55.3 59.4 49.7 43.7 55.7
Ahoada East 37.2 41.4 49.7 43.7 55.7
Ahoada West 55.2 57.3 49.7 43.7 55.7
Andoni 31.8 33.1 49.7 43.7 55.7
Akuku Toru 41.8 41.7 49.7 43.7 55.7
Asari Toru 57.6 58.1 49.7 43.7 55.7
Bonny 52.5 54.7 49.7 43.7 55.7
Degema 37.3 38.9 49.7 43.7 55.7
Eleme 51.1 51.9 49.7 43.7 55.7
Emohua 50.5 54.6 49.7 43.7 55.7
Etche 51.0 45.1 49.7 43.7 55.7
Gokana 43.8 53.8 49.7 43.7 55.7
Ikwere 49.5 43.2 49.7 43.7 55.7
Khana 36.7 36.8 49.7 43.7 55.7
Obio/Akpor 54.0 54.5 49.7 43.7 55.7
Onelga 52.6 52.0 49.7 43.7 55.7
Ogu/Bolo 54.0 63.4 49.7 43.7 55.7
Okrika 53.8 55.3 49.7 43.7 55.7
Omuma 46.4 48.1 49.7 43.7 55.7
Opobo/Nkoro 39.2 40.3 49.7 43.7 55.7
Oyigbo 60.5 58.1 49.7 43.7 55.7
Port Harcourt 72.4 71.5 49.7 43.7 55.7
Tai 47.8 48.5 49.7 43.7 55.7
Table 18: LGAs Mean rating, MML, DML, and State Mean Score in Mathematics
learning domain
MATHEMATICS
Abua/ODUA 37.9 46.3 45.2 40.0 50.4
Ahoada East 57.8 46.1 45.2 40.0 50.4
Ahoada West 61.9 58.8 45.2 40.0 50.4
Andoni 48.6 49.6 45.2 40.0 50.4
Akuku Toru 54.6 52.9 45.2 40.0 50.4
Asari Toru 42.2 43.7 45.2 40.0 50.4

Bonny 64.5 63.6 45.2 40.0 50.4
Degema 37.8 24.4 45.2 40.0 50.4
Eleme 48.4 48.6 45.2 40.0 50.4
Emohua 48.0 47.0 45.2 40.0 50.4
Etche 44.9 41.8 45.2 40.0 50.4
Gokana 33.5 50.2 45.2 40.0 50.4
Ikwere 53.4 45.4 45.2 40.0 50.4
Khana 43.7 42.0 45.2 40.0 50.4
Obio/Akpor 33.8 40.0 45.2 40.0 50.4
Onelga 50.0 45.7 45.2 40.0 50.4
Ogu/Bolo 53.7 59.1 45.2 40.0 50.4
Okrika 57.2 53.4 45.2 40.0 50.4
Omuma 40.9 31.4 45.2 40.0 50.4
Opobo/Nkoro 38.4 37.4 45.2 40.0 50.4
Oyigbo 33.6 36.0 45.2 40.0 50.4
Port Harcourt 38.8 38.1 45.2 40.0 50.4
Tai 41.8 46.1 45.2 40.0 50.4
Table 19: LGAs Mean rating, MML, DML, and State Mean Score in General
Science learning domain
GENERAL SCIENCE
Male Female STATE MEAN SCORE MML DML
Abua/ODUA 70.4 78.3 58.4 50.5 66.3
Ahoada East 68.1 66.6 58.4 50.5 66.3
Ahoada West 44.5 45.9 58.4 50.5 66.3
Andoni 47.1 46.4 58.4 50.5 66.3
Akuku Toru 48.4 45.8 58.4 50.5 66.3
Asari Toru 65.6 65.8 58.4 50.5 66.3
Bonny 32.9 32.9 58.4 50.5 66.3
Degema 67.2 60.9 58.4 50.5 66.3
Eleme 72.0 69.9 58.4 50.5 66.3
Emohua 68.0 59.9 58.4 50.5 66.3
Etche 63.0 67.2 58.4 50.5 66.3
Gokana 45.9 49.0 58.4 50.5 66.3
Ikwere 37.3 35.0 58.4 50.5 66.3
Khana 51.7 53.7 58.4 50.5 66.3
Obio/Akpor 75.6 69.9 58.4 50.5 66.3
Onelga 65.3 69.3 58.4 50.5 66.3
Ogu/Bolo 68.1 73.5 58.4 50.5 66.3
Okrika 77.3 70.7 58.4 50.5 66.3

Omuma 41.7 35.5 58.4 50.5 66.3
Opobo/Nkoro 45.0 46.2 58.4 50.5 66.3
Oyigbo 70.4 63.1 58.4 50.5 66.3
Port Harcourt 72.5 71.0 58.4 50.5 66.3
Tai 58.0 59.6 58.4 50.5 66.3

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