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Performance Determinants of Kenya Certificate of Secondary

Education (KCSE) in Mathematics of Secondary Schools

in Nyamaiya Division, Kenya

Philias Olatunde Yara PhD

Kampala International University

Kampala, Uganda

Tel: 256-783-104-687 E-mail: yaraphilias@yahoo.com

Wanjohi. W. Catherine

E-mail: wangari.catherine@ymail.com

Abstract

The study found the performance determinants of students’ performance in mathematics Kenya certificate of

secondary education (KCSE) in Nyamaiya division of Kenya. The study employed descriptive survey design of

the ex-post facto type with a total student population of 151 and 12 teachers. Four validated research instrument

developed for the study were Mathematics Achievement Test (MAT) (r = 0.67), Students Questionnaire (SQ) (r

= 0.75), Teachers Questionnaire (TQ) (r = 0.60 and Head teachers Questionnaire (HQ) (r = 0.70). Three research

questions were answered. The data was analyzed using multiple regression analysis. There was a positive

correlation among the six independent variables and the dependent measure – mathematics performance(R=

0.238; F(6,151)=1.53843; p<0.05). The six variables accounted for 45.6% of the total variance in the independent

measure (R2 = 0.564). Teachers’ experience (B=0.972, t=2.080; p<0.05), teachers’ qualification (B=0.182,

t=2.390; p<0.05), teachers/students’ attitude (B=0.215, t= 2.821; p<0.05) and school category (B=0.064, t=0.352;

p<0.05) could be used to predict students’ academic performance in mathematics. It is therefore recommended

that adequate attention should paid to these variables that can predict students’ performance by the government

and other stakeholders of education in Kenya.

Keywords: Performance determinants, Students performance, Secondary school mathematics, Kenya certificate

of education

1. Introduction

Schools are social organizations with defined rules and procedures that determine the degree of activities and

behavior of each member (Mbithi, 1974). Schools are in a sense factories in which raw children are to shaped

and finished to meet the various demands of life. The specifications for manufacturing come from the policies

laid down by the government. The system of education in Kenya is highly selective even in primary level, while

access to schooling is limited. Advancement is solely based on students’ performance in examinations.

Examinations are used above all to identify and define those adjudged suitable to proceed to the next stage of

education.

Success in educational institution is measured by the performance of students in external examinations.

Examinations are used among others to measure the level of candidates’ achievements and clarify the

candidates’ level of education, training and employment. They also provide the basis for evaluating the

curriculum both at local and national level. Examinations can when used properly, improve the quality of

teaching and learning and because of this reason when Kenya Certificate of Secondary Education (KCSE) results

are released the feedback is sent to schools through a report indicating not only how students have performed but

also what teachers and students should do to improve on future examinations.

The government through its policy documents has outlined several strategies to be adopted in order to enhance

the field of science and technology. Key among them is the strengthening of technical capabilities through

training of personnel and provision of equipments. Through partnership with development partners, the

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government has embraced several initiatives such as the strengthening of mathematics and science subjects in

secondary schools (SMASSE) project. This is a joint venture between Japanese government through the

Japanese international Development Agency (JICA). It was established in 1998 to improve the capacity of young

Kenyans in science and mathematics through In-training (INSET) centre for mathematics and Technology

Education in Africa (CEMASTEA News letter, 2008).

In South Africa, Mji and Makgato (2006) observed that many schools do not offer mathematics and those that

offer do not have adequate facilities for effective teaching and learning. Pupil-textbook ratio has been high

especially in rural and urban slums where students do not perform to expectations. A survey conducted by

Education Insight (2005) in Kenya revealed that inadequate learning facilities are a common feature in many

schools. Yeya (2002) agreed with the above studies that schools with adequate facilities perform better in

National Examination especially in core subject such as mathematics. He however asserted that facilities alone

cannot count while other factors should be taken into consideration.

Recent studies carried out to determine the relationship between teacher experience and students’ performance in

mathematics found that teacher experience and competence were the prime predictors of students’ performance

in all subject in secondary schools in Ondo state Nigeria (Adeyemi, 2008). Jones (1997) observed that teachers

are a key input and a force to reckon with in school. Sweeney (1998) made similar observation about schools in

Mississippi, USA that scored better in mathematics when taught by teacher with more years of teaching,

considering the common saying that experience is the best teacher.

Studies carried out to determine the relationship between teacher quality and student performance in

mathematics revealed that there exists a strong relationship. For instance, Kaur (2004) asserts that in Singapore

the problem of teaching mathematics needed qualified teachers/educators and recommended that the Ministry of

Education equip mathematics teachers with the necessary skills through in-service courses. Teacher’s shortage in

South Africa has been a stumbling block to performance of mathematics (Mji and Makgato, 2006). Schools are

characterized by huge enrollments leading to overcrowding. Odhiambo (2006) pointed out that there is a

shortage of mathematics teachers in Kenya but in urban schools the problem is not as pronounced. He further

revealed that the student/teacher ratio in many secondary schools in Kenya is 40:1 and proposed that for

effective teaching of mathematics, it should be 25:1 hence the need to employ more mathematics teachers.

Mji and Makgato (2006) observed that non-completion of the syllabus is a major determinant to students’

performance in mathematics. The principal of a leading school in Kenya noted that students fail in mathematics

because they do not cover their syllabus and are therefore unprepared for examinations (Education Insight, 2005).

In the same vein, Yeya (2002) observed that students in boarding schools cover the syllabus in time and are

exposed to more remedial exercises because they are ever in school as compared to day schools which are

characterized by absenteeism of both teachers and students which lead to non-completion of the syllabus in a

given year. This view was also supported by Maundu (1986).

In Jamaica, poor attitude to mathematics as a subject is evident among many students and some view the subjects

as being of little or no use to them outside schools as noted by the Ministry of Education Youth and Culture

(2003). In South Africa, Mji and Makgato (2006) pointed out that few students take mathematics and those who

do so do not perform well because they are not motivated which ultimately may lead to mass failures. Yeya

(2002) had similar views that many teachers, students and parents have a negative attitude towards the teaching

and learning of mathematics. Chiriswa (2003) agreed with the above view and recommended that mathematics

teachers and students be given incentives to raise their morale for better grades in mathematics.

A number of studies have approached the question of mathematics performance from the school category status,

type and location view. Many studies dating as far back as the eighties have maintained that single-sex schools

are solution to poor female performance (KNEC, 2006). Survey carried out by USAID education program

strategy revision (2003) revealed that rural schools in South Africa have poor facilities and teachers lack

rudimentary training in key subjects for example mathematics. Odhiambo (2006) observed that urban schools are

not badly hit by teacher shortages as many prefer teaching in urban areas. Yeya (2002) noted that students with

impressive marks avoid day schools in favor of boarding schools. He further observed that students in boarding

schools perform better in national examinations.

Available literature in Nigeria has not been able to identify a single direction of difference in performance in

mathematics between male and female students (Kadiri, 2004). Although most studies have found boys

performing better (Fennema and Sherman, 1978) a few others saw girls out-performing boys while others

established no significant difference. Fennema and Sherman (1978) asserted that mathematics is a subject of

male domain. This view was supported by Alao and Adeleke (2000) that girls recorded low performance than

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boys in mathematical activities in Nigeria secondary schools. Girls were found to exhibit more mathrophobies

than boys. Manger (1996) in Norway had the same view, but observed that the difference was small. In New

Zealand, Blith, Forbes, Clerk and Robinson (1994) reported a consistent difference in performance in favor of

boys while Armstrong (1981) noted that sex difference existed at high level and not at the junior level in

mathematics achievements.

In Kenya, mathematics is a core subject in school curriculum for both primary and secondary schools and yet the

performance is very dismal. While poor performance is applicable to most parts of the country, some areas have

a record of perennial mass failures in mathematics. This is especially so in Nyamira District. Even though studies

have been carried out to determine the reasons for general poor performance in the district, mathematics has not

been singled out as a subject that students do perform poorly. This study therefore seeks to find the determinants

of poor performance in mathematics in Nyamira District of Kenya.

2. Research Questions

The following research questions are answered in this study.

1. What is the composite effect of performance determinants (teachers’ experience, teachers’ qualification,

teachers’ and students’ attitude, school category, school facilities and students’ gender) on students’

performance in mathematics?

2. What are the relative effects of performance determinants (teachers’ experience, teachers’ qualification,

teachers’ and students’ attitude, school category, school facilities and students’ gender) on students’

performance in mathematics?

3. Which of the performance determinants (teachers’ experience, teachers’ qualification, teachers’ and

students’ attitude, school category, school facilities and students’ gender) will predict students’

performance in mathematics?

3. Methodology

3.1 Research Design

The research design used for this study is the descriptive survey design of the ex-post facto type. This is because

the researchers had no direct control over the independent variables as they have manifested already.

3.2 Population and Sample procedure

The target population for this study consists of head-teachers, mathematics teachers and the students of all the

thirteen public secondary schools in Nyamaiya Division. For this study, a sample of six secondary schools was

purposively selected from the thirteen schools. Schools were selected across categories and type which includes

day, boarding, single-sex and co-educational. Intact classes were selected. Simple random sampling technique

was used to select the teachers and students for the study. A total of 12 teachers and 151 students were selected

for the study.

3.3 Instruments

The research instruments designed by the researchers include Mathematics Achievement Test (MAT) Students

Questionnaire (SQ), Teachers Questionnaire (TQ) and Head teachers Questionnaire (HQ). Each of the

questionnaires will have three parts. Part A contains profile of the respondents and Part B contains respondents’

views on the determinants of poor performance in mathematics. The MAT was validated using Kuder

Richardson 20 (KR-20) with the calculated value of 0.067 while the other instruments (SQ, TQ and HQ) were

validated using Cronbach alpha coefficients. The calculated values were 0.75, 0.60 and 0.70 respectively.

3.4 Data Analysis

Multiple regression analysis was used to analyze the data.

4. Results

4.1 Research question one

What is the composite effect of performance determinants (teachers’ experience, teachers’ qualification,

teachers’ and students’ attitude, school category, school facilities and students’ gender) on students’ performance

in mathematics? This question is answered using tables 1 and 2.

From Table 1, it could be observed that there is positive multiple correlation (R =0.238) among the six

independent variables and the dependent measure. These variables are teachers’ experience, teachers’

qualification, teachers’ and students’ attitude, school category, school facilities and students’ gender and

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students’ performance in mathematics, which is the dependent variable. This implies that the factors are relevant

towards the determination of the dependent measure. Also the adjusted R2 value of 0.456 revealed that the six

variables accounted for 45.6% of the total variance in the dependent measure (academic performance in

mathematics). The remaining 54.4% could be due to errors and factors that are not considered in this study.

The result in the analysis of variance in Table 2 showed that the F-ratio of the regression analysis is significant

(F(6,151) = 4.478; p<0.05). This shows that the R value is not due to chance.

4.2 Research Question Two

What are the relative effects of performance determinants (teachers’ experience, teachers’ qualification,

teachers’ and students’ attitude, school category, school facilities and students’ gender) on students’ performance

in mathematics? Table 3 was used in answering this question.

From Table 3 out of the six variables, school category made the greatest contribution (? = 0.192). This implied that

the type of school (whether single sex or mixed or public or private) has effect on the academic performance of

students in mathematics. The type of school students go to greatly influenced their performance in mathematics

because some of the schools have qualified mathematics teachers and good learning environment. Next was

teachers/students’ attitude (? = 0.181). The implication of this contribution to the academic performance of

students in mathematics is that the attitudes of the teachers and students to mathematics have a very great influence

in the academic performance of the students in mathematics. The positive attitude of the students to mathematics

and their teachers has an impact on the good performance of the students in mathematics. Teachers’ qualification

(? = 0.152) also made contribution to the academic performance of the students in mathematics. This means that

the qualification of the teachers affect the performance of the students in mathematics. The more qualified the

teachers are the better the students’ performance in mathematics. The fourth in the rank of contribution is teachers’

experience (? = 0.118). This implies that the more experienced the teachers are, the better they are able to teach

better and hence the students perform better. The fifth and sixth contributions in order of decreasing magnitude are

students’ gender (? = 0.031) and school facilities (? = 0.024). This means that gender and school facilities even

though made contributions to the academic performance of students in mathematics, they do not have much

influence neither do they affect in greater measure the performance of students in mathematics.

4.3 Research Question Three

Which of the performance determinants (teachers’ experience, teachers’ qualification, teachers’ and students’

attitude, school category, school facilities and students’ gender) will predict students’ performance in

mathematics? This question was answered using table 3.

From the results in Table 3, teachers’ experience (B=0.972, t=2.080; p<0.05), teachers’ qualification

(B=0.182,t=2.390; p<0.05), teachers/students’ attitude (B=0.215,t= 2.821; p<0.05) and school category

(B=0.064, t=0.352; p<0.05) could be used to predict students’ academic performance in mathematics. The other

variables like students’ gender and school facilities cannot be used to predict students’ academic performance in

mathematics.

5. Discussions

The results of the findings showed that teachers’ experience is significant and can be used to predict students’

performance in mathematics. This result is in accordance with the findings of Adeyemi (2008) who said that

teacher experience and competence were the prime predictors of students’ performance in all subject in

secondary schools in Ondo state, Nigeria. It also agrees with the findings of Sweeney (1998) and Jones (1997)

who opined that the teacher is a prime factor in the performance of students. Teachers’ qualification was found

to be significant and can also used to predict students’ performance in mathematics. This is in agreement with

the findings of Kaur (2004) who opined that in Singapore the problem of teaching mathematics needed qualified

teachers/educators and recommended that the Ministry of Education equip mathematics teachers with the

necessary skills through in-service courses.

Teachers’ attitude towards the teaching of mathematics plays a significant role in shaping the attitude of students

towards the learning of mathematics. The result of this study confirmed this because teachers’ and students’

attitude was significant and can be used to predict students’ performance in mathematics. The results also agrees

with that of Onocha (1985) who reported in one of his findings that teachers’ attitude towards science is a

significant predictor of pupils’ science achievement as well as their attitude towards science. The result also

agrees with that of Miji and Makgato (2006), Yeya (2002) and Chiriswa (2003). Also, Ogunniyi (1985) found

that students’ positive attitude towards science could be enhanced by teachers’ enthusiasms, resourcefulness and

helpful behaviour, teachers’ thorough knowledge of the subject matter and their making science quite interesting.

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All these factors could also be applicable to mathematics learning since mathematics is regarded as the language

of science.

School category was found to be significant and can be used to predict students’ performance in mathematics.

This result was in accordance with that of Odhiambo (2006) and Yeya (2002) who observed that urban schools

are not badly hit by teacher shortages as many prefer teaching in urban areas and also noted that students with

impressive marks avoid day schools in favor of boarding schools. They further observed that students in

boarding schools perform better in national examinations. The implication of this result is that the government

should endeavour to build more schools in the rural areas with adequate facilities and qualified teachers. This

will improve the students’ performance in mathematics as all benefits that urban teachers enjoy will be made

available to teachers in rural schools.

6. Conclusion

The study found out performance determinants of academic performance of students in mathematics. Out of the

six variables, four of them (teachers’ experience, teachers’ qualification, teachers’ and students’ attitude, school

category) could be used to predict academic performance in mathematics. It is therefore important that these

variables should be taken into consideration by the government and stakeholders in formulating policies in our

Ministries of Education. For good academic performance in mathematics, great attention should be placed on

qualified and experienced teachers, enhanced remuneration to boost their attitude towards the teaching of the

subject in order to retain them in the classroom. Moreover, adequate attention should be placed on students’

attitude towards their teachers and the subject in order for them to perform well in mathematics.

References

Adeyemi T.O. (2008). Teachers teaching experience and students learning outcomes in secondary schools in

Ondo state, Nigeria. MED Project, Department of Educational Foundation and Management, University of

Ado-Ekiti-Nigeria.

Alao, K.A and Adeleke. A. (2000). A study of interference and factors influencing Phobia for mathematics

among Nigeria secondary school students. Ife journal of psychology, 10 (1), 9-18.

Armstrong, J.M. (1981). Achievement and participating of women in mathematics. Results of Tow National

Survey. Journal for research in Mathematics education, 12(5), 356-372.

Blith, T., Forbes, S, Clark, M. and Robinson, E. (1994). Gender difference in New Zealand mathematics

performance of secondary–tertiary interface. International Journal of Educational Research, 21(4), 427-228.

CEMASTEA News letter, March 2008 page 2.

Chariswa, P. (2002). An investigation into the probable factor responsible for poor performance of mathematics

in K.C.S.E in Vihiga district of western Kenya. A project for the degree of Master of Education, Kenyatta

University.

Education insight. (2005). For quality information, education and communication issues, volume 8, page 21.

Eshiwani, G.S. (1983). Factors influencing performance in primary and secondary school in western province.

Policy study (Kenyatta University college) bureau of Educational Research.

Fennema and Sherman. (1978). Sex-related difference in mathematics achievements and related factors, a further

study. Journal for Research in Mathematics Education.

Jonnes, M. (1993). Trained and untrained secondary school teachers in Barbados, “is there a difference in

classroom performance?” Educational research, 39(2), 182.

Kadiri.S.A. (2004). The effectiveness of the personalized system of instruction among secondary school students

in Osun State. Unpublished PhD Thesis, Obafemi Awolowo University, Ile-life, Nigeria.

KNEC. (2003, 2006, 2007, 2008). Examination Report, government Printers Nairobi.

Manger. (1996). Gender differences in mathematical achievement at the Norwegian Elementary school level.

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and Harambee secondary school in Kenya. Unpublished PhD Dissertation of secondary education, MC Gill

University, Montreal.

Mbithi, D.M. (1974). Foundation of School Administration. Oxford University press, Nairobi.

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Mji, A. and Makgato. (2006). Factors that associate with high school learners’ poor performance. Spotlight on

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Njuguna, B. (2005). A report of Educational standard of Nyanza province. “Students can excel in mathematics.

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Odhiambo, J. W. (2006). Teaching of statistics in Kenya. University of Nairobi.

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Table 1. Summary of Regression Analysis on Performance determinants

Multiple R R2 Adjusted R2 Standard Error

.238 .561 .456

1.53843

Table 2. Analysis of Variance on Performance determinants

Source of

Variance Squares

Regression 33.709 6

Residual 563.287 145

Total 596.996 151

*sig. at p<0.05

Table 3. Estimate of the Relative Contribution of Performance determinants and Students’ academic

performance in Mathematics

Independent

Variables

(Predictors)

B

Standard

error

Teachers’

Experience

Teachers’

Qualification

Teachers/Stude

nts’ Attitude

Students’

gender

School

category

School

Facilities

F ratio

4.748

Sum of Df Mean

Square

11.236

2.367

F Sig.

4.748

.003

Unstandardized

Coefficients

Standardized

Coefficients

Beta Rank T Sig.

0.972 0.331 0.118 4th 2.080 0.009*

0.182 0.078 0.152 3rd 2.390 0.018*

0.215 0.076 0.181 2nd 2.821 0.005*

0.083 0.176 0.031 5th 0.472 0.637

0.229 0.078 0.192 1st 2.917 0.004*

0.064 0.182 0.024 6th 0.352 0.725