







































Education, Society and Human Studies 
ISSN 2690-3679 (Print) ISSN 2690-3687 (Online) 

Vol. 1, No. 2, 2020 
www.scholink.org/ojs/index.php/eshs 

154 

Original Paper 

Gender Difference in the Relationship between Self-Efficacy 

and Performance in Science among Secondary School Students 

in Migori County, Kenya 
Polycarp O. Gor1*, Lucas O. A. Othuon1 & Quinter A. Migunde1 

1 Department of Educational Psychology, Maseno University, P.O. Box 333-40105, Maseno, Kenya 
* Polycarp O. Gor, Department of Educational Psychology, Maseno University, P.O. Box 333-40105, 

Maseno, Kenya 

 

Received: October 21, 2020     Accepted: October 31, 2020     Online Published: November 9, 2020 

doi:10.22158/eshs.v1n2p154                     URL: http://dx.doi.org/10.22158/eshs.v1n2p154 

 

Abstract 

The purpose of this study was to investigate the gender difference in the relationship between 

self-efficacy and performance in science. A sample of 327 Form Four students in Migori County was 

used. Questionnaires, focus group discussion guide and interview schedules were used for data 

collection. Quantitative data were analyzed using descriptive statistics and correlation. Qualitative 

data were organized into themes and interpreted. Overall, boys had higher levels of performance in 

science (Mean=39.21) than girls (Mean=30.80) and the mean difference was statistically significant 

(t=3.89, p=.00). Boys had higher levels of self-efficacy (Mean=2.89) than girls (Mean=2.81) and the 

mean difference was not statistically significant (t=1.56, p=.12). Further, the overall correlation 

between self-efficacy and performance was statistically significant with r=.236 (p=.002, n=327). The 

correlation between self-efficacy and performance for boys was significant with r=.250 (p=.005, 

n=200) and non-significant for girls with r=.085 (p=.558, n=127). It is concluded that boys 

outperform girls in science and record higher scores in self-efficacy than girls. In addition, the 

variance shared in common between self-efficacy and performance is higher for boys than girls. To 

improve performance and also reduce the gender gap in science performance, self-efficacy should be 

enhanced for students but more particularly so for girls. 

Keywords 

self-efficacy, performance in science, gender, Kenya 

 

 



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1. Introduction 

Gender bias in Mathematics and science classrooms has been and still continues to be a problem (Diane, 

2003). Despite improvements in the past two decades, girls are still less likely than boys to take physics 

and higher-level Mathematics and science courses in high school. As a consequence, fewer female 

students may study Mathematics and science at the college level. The types of courses taken in high 

school and how students perform in these courses can impact acceptance into college, choice of college 

major, and subsequent career choice (Diane, 2003). 

A growing demand for professionals in Science, Technology, Mathematics and Engineering (STEM) is 

met with a significant labor shortage in these fields as women account for just 28 percent of global 

researchers but the figure masks wide variations between countries and regions (UNESCO, 2016). 

Women are often underrepresented in STEM, and their low levels of participation can be traced back 

all the way to their school years, where a number of influences from society and culture, education and 

the labor market are all at play (UNESCO, 2016). 

Statistics Canada (2007) reports that as of 2006 only 22% of professionals in natural sciences, 

Engineering, and Mathematics are women, an increase of a mere 2% from 1987; and since women 

continue to account for a small sector of the student population in these fields, there will probably be 

little change to this statistic in the near future (Fried & MacCleave, 2009; Statistics Canada, 2007). 

Similar situations are visible in the United States and Europe. In the United States, women comprise 

50% of the workforce but only 15% of scientists are female (Weinburgh, 2000). While more women are 

receiving PhDs in science nowadays there still is no equity as far as their career is concerned (Burrelli, 

2008; UNESCO, 2007).  

At the end of the 20th century, still fewer than 6% of the highest attainable academic positions were 

held by women (Fox, 2001). This trend is also seen in other parts of the world. In Europe, although 

women represent more than half of the student population, only 11% of top academic science positions 

are held by women (Dewandre, 2002; European Commission, 2009). While women represent 52% of 

professionals and technicians, only 32% of scientists and engineers are women. Efforts in Europe to 

encourage more women to obtain their PhDs have been successful in life sciences and humanities, 

where women are 41% of PhD holders; however, in Engineering and physical science, only 25% of 

PhD holders are women (European Commission, 2009). Women scientists attribute this skewed 

representation to small discriminations along the way that finally end up creating this gender gap 

(Baker & Leary, 2003; Ceci & Williams, 2007).  

Studies in Nigeria have however revealed mixed reports on gender difference in science performance. 

Some researchers have provided reports that there are no longer distinguishing differences in the 

cognitive, affective and psychomotor skill achievements of students in respect of gender (Abayomi & 

Mji, 2004; Bilesanmi- Awoderu, 2006 & Din, Ming & Ho, 2004). Girls are being encouraged and 

sensitized into developing positive attitudes towards science. Other researchers have reported 



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differently on this issue. For example, in one study carried out by Eriba and Sesugh (2006), they found 

that boys outperformed girls in integrated science and mathematics achievements. Some other research 

studies reported that males are becoming the disadvantaged gender in schools, and that fewer males are 

interested in science (Omoniyi, 2006). 

In Uganda, a study conducted by the Female Education in Mathematics and Science in Africa project, 

found that women’s performance in science subjects (which is the gateway to computer science studies) 

in the Uganda Certificate Examinations is very low compared to that of men (Ochwa-Echel, 2011). He 

further reckons that although there has been some rhetoric from politicians and educators in Uganda 

about improving the teaching of mathematics and science courses to women, not much has been done 

and the deficiencies and inadequacies continue. The concern this raises is that women are not 

participating fully in the sector, meaning that their potential is not being fully realized and their 

capabilities to participate in the development of the country in particular, and in the human 

development process in general, are being curtailed. 

The situation in Kenya is no better as recent literature show that there has been a big problem of poor 

performance in STEM subjects in the country as a whole as girls perform even worse in comparison 

with the boys (Forum for African Women Educationalists, 2008). The same position is held by Wambua 

(2007) in his study as he found that boys performed better than girls in STEM subjects. This has caused 

a lot of public outcry as poor performance by girls in STEM would automatically translate to fewer 

women enrolling for STEM courses (FAWE, 2008). Wambua (2007) reckons that as a result of the 

gender gaps in performance in STEM subjects, fewer girls as compared to boys qualify to join Science, 

Mathematics, Engineering and Technology related courses. 

The situation in the Kenyan universities is no better. The figures from the Commission for University 

Education indicate that a third (33 percent) of university students enrolled in STEM courses are women. 

The presence of women varies according to the field of study. In 2015, at the undergraduate level, the 

share of female students’ enrolment was particularly low in the clusters of Manufacturing (16 percent), 

Engineering (17 percent) and Computing (22 percent). But there was near gender parity in the health 

and welfare cluster (49 percent) (CUE, 2018). Consequently, at the Kenya Certificate of Secondary 

Education (KCSE) examination, boys continue to outshine girls in Science and Mathematics. In 2019 

boys defeated girls in all the science subjects. In the 2018 Kenya Certificate of Secondary Education 

examination girls defeated boys in Metal Work only and lagged behind the boys in the other science 

and technology-related courses, including Biology, Chemistry, Computer Studies, Electricity, General 

Science, Mathematics, Physics, Power Mechanics, Agriculture, and Aviation. A similar result was 

evident in the 2017 KCSE results where, of the examinable subjects, boys scored better than girls in 23 

subjects, defeating girls in all Sciences, whereas girls only defeated boys in 6 subjects of; English, 

Kiswahili, CRE, Home Science, Art, and Design and Electricity (KNEC, 2019). 

Migori County in Kenya has not done any better in terms of science performance as well. The County 



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had girls scoring an average of 22.63% for all sciences against the boys’ 26.65%, giving a gender 

disparity of 4.02%. Consequently, just 10.22% of girls in comparison with 20.46% of boys who sat for 

2019 KCSE examination did all the 4 Science subjects (Migori County Education Office Records, 

2019). Migori county risks lagging behind in contributing to scientific development, since STEM 

subjects contribute towards industrialization, environmental conservation, medical research, food 

management and improved agricultural production. As a result, Kenya risks losing out on her 

aspirations to achieve the Sustainable Development Goals and attainment of Vision 2030. Subsequent 

to the conflicting research findings regarding gender differences in sciences, it is important to conduct 

more studies in this area, particularly at secondary school level. 

One possible explanation of gender disparity in performance in sciences is that girls tend to have lower 

levels of self-efficacy in science than boys. Titilayo, Oloyede and Adekunie (2016) explained that 

self-efficacy reflects the extent to which students believe that they can successfully perform in school. 

Thus, science self-efficacy may be defined as the extent to which students believe that they can 

successfully perform in science. Consequently, if a girl believes she is unable to succeed in science, her 

perception may be subsequently altered and this is likely to manifest in lower scores or in avoiding 

science subjects altogether (Corbett, Hill, & Rose, 2008). Previous research has established that science 

self-efficacy is associated with science achievement and science-related choices across grade levels 

(Kiperman, 2002; Lau & Reeser, 2002). 

Titilayo, Oloyede and Adekunie (2016) sought to establish whether the dismal performance of Nigerian 

candidates in School Certificate Chemistry could be traced to their self-efficacy. The study however 

found no significant relationship between self-efficacy and chemistry students’ academic achievement 

in chemistry. 

The above notwithstanding, the relationship between self-efficacy and performance in science subjects 

cannot be underscored (Ochieng, 2015). In his study on the relationship between self-efficacy and 

Mathematics achievement, the results showed that male students had higher levels of self-efficacy than 

their female counterparts. His study further observed that there was a significant gender difference in 

self-efficacy. 

While some studies showed a relationship between self-efficacy and performance in science (Corbett, 

Hill, & Rose, 2008; Kiperman, 2002; Lau & Reeser, 2002; Ochieng, 2015); others have shown no 

significant relationship between these two constructs (Titilayo, Oloyede, & Adekunie, 2016). Based on 

these conflicting findings, one cannot conclusively posit whether or not self-efficacy is related to 

performance in science, and if it does, it would be worthwhile to explore the nature of such a 

relationship across gender. 

Whereas Corbett, Hill and Rose (2008), Kiperman (2002) and Lau and Reeser (2002) assessed the 

relationship between self-efficacy and performance in physics, Titilayo, Oloyede and Adekunie (2016) 

assessed the relationship between self-efficacy and performance in chemistry while Ochieng (2015) 



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investigated the relationship between self-efficacy and performance in mathematics. These studies did 

not examine the relationship between self-efficacy and science as a combined subject area. Rather, they 

each focused on one science subject only. This may lead to inaccurate findings as each science subject 

is unique in its own way. This study therefore sought to fill this gap by looking at gender difference in 

the relationship between self-efficacy and performance in science among secondary school students in 

Migori County, Kenya. 

1.1 Objectives of the Study 

The study was guided by the following objectives: 

i. To establish secondary school students’ level of performance in science subjects across gender. 

ii. To establish secondary school students’ level of self-efficacy in science across gender. 

iii. To determine the overall relationship between self-efficacy and performance in science among 

secondary school students. 

iv. To examine gender difference in the relationship between self-efficacy and performance in science 

among secondary school students. 

1.2 Limitation of the Study 

One limitation is that the Science Achievement Test used in the present study tested the theoretical 

aspects of the science subjects only, leaving out the practical segment. Therefore the results of the test 

could be slightly inaccurate as the practical segment makes a reasonable part of science as a subject. To 

remedy this, qualitative data were collected from the Heads of Science Department on the level of 

performance in science with respect to both theoretical and practical aspects to complement the results 

obtained from the Science Achievement Test. 

1.3 Significance of the Study 

The findings of this study may be useful to both teachers and students as it would seek to provide ways 

of improving confidence in science, particularly for female students who have consistently shown a 

weaker performance in science. The findings would also be useful to curriculum developers and teacher 

trainers in the development of content and methodology that may improve learners’ confidence in 

science. 

 

2. Method 

2.1 Research Design 

A mixed methods research design which includes both quantitative and qualitative paradigms was 

adopted in the study. More specifically, the convergent parallel mixed methods approach was used.  

2.2 Study Population 

The population for this study consisted of Form Four students of the year 2020 and the Heads of 

Science Department in Migori County, Kenya. The Form Four students in this target population were 

those who took all the four science subjects, namely, Mathematics, Biology, Chemistry and Physics. 



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They were approximately 2,200 in total, i.e., 1550 boys and 650 girls spread out in the 240 public 

secondary schools in the County (Migori County Director of Education Office Records, 2019). The 

population of the Heads of Science Department (HOSD) was approximately 240 since the 240 public 

secondary schools were believed to have one HOSD each. The HOSD were selected because they were 

perceived to have a better understanding of student performance in the four science subjects. 

2.3 Sample Size and Sampling Technique 

Fisher et al. (1991) formula was used to arrive at a sample size of 327 students. The study used 

stratified sampling method and simple random sampling technique to sample the students for study. In 

this regard, schools were divided into 4 strata; national, extra-county, county and sub-county. Of the 

240 public secondary schools in the County, 2 are national schools, 8 are extra-county, 14 are county 

and 216 are sub-county schools. To get the number of students to be sampled from each stratum, a 

proportion was worked out based on the following formula: 

N=  x Sample size 

Where N is the number of students in each stratum. 

From this formula, 294 students were randomly selected from Sub-county schools, 19 from County 

schools, 11 from Extra-County schools and 3 from National schools.  

Ten groups comprising of 6 participants each were selected to take part in the Focus Group Discussion. 

Thirty Heads of Science Department from 30 schools were also selected for the study. 

2.4 Research Instruments 

Four tools were used for data collection; Science Self-Efficacy Scale (SSES), Science Achievement 

Test (SAT), Focus Group Discussion Guide and Head of Science Interview Schedule (HOSIS). The 

tools are described below. 

2.4.1 Science Self-Efficacy Scale (SSES) 

The Science Self-Efficacy Scale was used to measure the level of Science self-efficacy. The scale is an 

adaptation from Capa Aydin and Uzuntiryaki (2009), High School Chemistry Self-Efficacy Scale. The 

scale was modified to capture science in general rather than just focus on Chemistry which was the 

case with the original scale. The scale has 12 items scored in a 4-point Likert-type scale ranging from 

no confidence at all (very low level)=1, Very little confidence (moderately low level)=2, Much 

confidence (moderately high level)=3 to complete confidence very high level)=4. The original 

instrument gave a reliability of .90 (Uzuntiryaki, Capa Aydin, Ceylandag & Cömert, 2011). The current 

scale was subjected to reliability tests to ascertain its suitability in the local environment. 

2.4.2 Science Achievement Test (SAT) 

Science Achievement Test was used to measure the students’ performance in science subjects. The test 

was developed from the 2012 Kenya Certificate of Secondary Examination from which questions 

drawn from the Forms 1 and 2 syllabi were selected. The test was made up of 4 subjects; Physics, 



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Biology, Chemistry and Mathematics with each subject having a total of 25 marks. The items in the 

scale fell under 4 sub-scales of Physics, Biology Chemistry and Mathematics. The total score for the 4 

sub-scales was 100 with scores below 50 being weak performance while scores above 50 being good 

performance. The scale was subjected to a pilot study to ascertain its reliability before being adopted 

for use in the main study. Experts from the Department of Educational Psychology, Maseno University 

advised on the face and content validity of the instrument. 

2.4.3 Focus Group Discussion Guide (FGDG) 

The Focus Group Discussion Guide was used to get more information from the students on the study 

variables and was meant to give more insight to the quantitative data got from the questionnaire. The 

discussion served to complement the quantitative data and also provide peripheral information that may 

have not been covered by the study questionnaires. Focus Group Discussion Guide was used due to its 

ability to give a more detailed and in-depth insight of the issues provided. Consequently, it allows for 

integration of the quantitative and the qualitative data (Creswell, 2013). The instrument was be 

administered to groups from 10 randomly sampled schools. The groups will be made up of 6 students 

each who will have completed the questionnaires. The scale was subjected to content validity tests to 

remove the irrelevant or redundant content. 

2.4.4 Head of Science Interview Schedule (HOSIS) 

The Head of Science Interview Schedule was used to obtain qualitative data on the variations in science 

performance across gender from the teachers’ point of view. The schedule is made up of 5 open ended 

questions which seek to find out the factors responsible for gender disparity in science performance. 

2.5 Procedure for Data Collection 

Permission for data collection was first sought from the Maseno University School of Graduate Studies 

(SGS) and Maseno University Ethics Review Committee. Thereafter, the Migori County Director of 

Education (MCDE) was provided with information about the intended study. The Principals of the 

samples schools were then requested to seek permission from the Parents Association (PA) to allow 

students participate in the study. It was at this point that actual data were collected through 

administering questionnaires and interview schedules as well as conducting focus group discussions. 

2.6 Methods of Data Analysis 

Descriptive statistics, correlation analysis and simple linear regression were used to analyze 

quantitative data. The software used for quantitative data analysis was the Statistical Package for the 

Social Sciences (Version 24). Qualitative data was analyzed thematically. 

2.7 Ethical Considerations 

All the protocols for conducting research in psychology were observed. The study was approved by 

Maseno University Ethics Review Committee prior to data collection. 

 

 



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3. Results 

3.1 Reliability Analysis 

Cronbach’s alpha gave a reliability index of 0.78 for Science Achievement Test and 0.77 for 

Self-Efficacy Questionnaire. Thus, both tools were reliable because their reliability exceeded the 

threshold of 0.70. 

3.2 Students’ Performance in Science across Gender 

Table 1 contains mean scores in the Science Achievement Test sub-scales as well as the overall mean 

score across gender. The overall mean score for boys (Mean=39.21) was higher than for girls 

(Mean=30.80). Boys consistently outperformed girls in all the four science subjects. The best done 

subject Mathematics followed by Biology and then Physics. The worst performance was in Chemistry. 

 

Table 1. Level of Performance in Science across Gender 

 Mean Score by Gender 

 Boys  Girls 

Physics    9.90    7.02 

Chemistry    7.39    5.78 

Mathematics  11.25    9.08 

Biology  10.66    8.92 

Overall Mean  39.21  30.80 

Valid N (listwise) 200  127 

 

The highest gender disparity was recorded in Physics followed by Mathematics. Biology had the third 

highest gender disparity in performance while Chemistry had the lowest gender disparity T. 

In order to establish if the mean difference in science performance between the mean for boys and that 

for girls is statistically significant or not, the independent samples t-test was used at α=.05 (two-tailed). 

Table 3 shows the outcome of the analysis which indicates that the fundamental assumption for t-test 

regarding the equality of variances was satisfied at α=.05 (F=.83, p=.37). With equal variances, the 

difference in science performance between boys and girls was statistically significant at α=.05 (t=3.89, 

p=.000). Therefore, the mean difference in science performance between boys and girls was a true 

difference in the population from which the sample was drawn and not a result of chance or sampling 

error. 

 

 

 

 

 



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Table 2. Test of Significance for Gender Difference in Science Performance 

 Levene’s Test for 

Equality of Variances 

t-test for Equality of Means 

F Sig. t df Sig. (2-tailed) 

Performance in Science 
Equal variances assumed .83 .37 3.89 173 .000 

Equal variances not assumed   4.08 100.60 .000 

 

When asked whether there was a gender disparity in science performance, the Heads of Science 

Department all agreed that girls have been performing poorer than boys in sciences. As an example, 

one teacher who had served as Head of Science Department for 12 years observed that: 

“Generally, for the time I have served as a head of science department, I have seen a 

consistent gender gap in science performance. The girls show a weaker performance 

in sciences that the science field appears as a preserve of the males. Although there is 

a slight improvement in girl performance in science, that doesn’t mean that girls are 

now performing any better than boys in sciences, actually there still exists a 

significant gap in the performance and I think something should be done to improve 

the female student’s performance in science.” 

This observation further confirms that girls tend to perform at a lower level than boys in science. 

During focus group discussion, students were asked which science subject has the highest disparity in 

performance in terms of gender. All the students who took part in the discussion were in agreement that 

Physics was better performed by boys than girls unlike other science subjects where participants gave 

conflicting opinions. For example, one student responded as follows: 

“The answer is definitely Physics. Boys by far perform better than the girls in 

Physics and the subject is actually considered a male subject. Even the number of 

girls who select Physics is so low in comparison with the boys. But above all, all the 

science subjects show a remarkable difference in science performance with boys 

topping the girls in these subjects.”  

3.3 Level of Self-Efficacy across Gender 

Table 3 presents mean scores for science self-efficacy across gender. The overall mean score for boys is 

2.89 and the overall mean for girls is 2.81. This implies that the level of self-efficacy for boys was 

higher than that for girls. 

 

 

 

 



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Table 3. Mean Scores for Self-efficacy across Gender 

 Mean 

 Boys Girls 

Explain Scientific terms 2.74 2.62 

Choose an appropriate formula to solve a scientific problem 2.86 2.66 

Carry out experimental procedures in the laboratory 2.87 2.76 

Describe scientific processes 2.60 2.54 

Correctly write scientific formulas and terms 2.78 2.56 

Interpret findings during laboratory practical 2.72 2.56 

Solve Mathematical problems using a log table 3.14 3.08 

Interpret various equations 2.70 2.76 

Explain the particulate nature of matter 3.22 3.08 

Solve Mathematical problems 3.14 3.18 

Correctly answer various questions in science subjects 2.92 2.68 

Correctly observe results of laboratory experiments 2.99 3.22 

Overall Mean 2.89 2.81 

Valid N (listwise) 200 127 

 

In order to establish if the mean difference in self-efficacy between the mean for boys and that for girls 

is statistically significant or not, the independent samples t-test was used at α=.05 (two-tailed). Table 5 

shows the outcome of the analysis which indicates that the fundamental assumption for t-test regarding 

the equality of variances was violated at α < .05 (F=11.724, p=.001). This notwithstanding, and 

assuming equal variances, the difference in self-efficacy between boys and girls was not statistically 

significant at α < .05 (t (173)=1.564, p=.120). 

 

Table 4. Test of Significance for Gender Difference in Self-efficacy 

 Levene’s Test for Equality of 

Variances 

t-test for Equality of Means 

F Sig. t df Sig. (2-tailed) 

Self-efficacy 

Equal variances 

assumed 
11.724 .001 1.564 173 .120 

Equal variances 

not assumed 

  
1.862 136.37 .065 

 

 



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3.4 Relationship between Self-Efficacy and Performance in Science 

The overall correlation between self-efficacy and performance in science was found to be statistically 

significant at α=.05 with r=.236 (p=.002, n=327). Thus, an increase in self-efficacy is associated with 

an increase in science performance. When data were disaggregated by gender, the correlation between 

self-efficacy and performance in science for boys was statistically significant at α=.05 with r=.250 

(p=.005, n=200). The correlation between self-efficacy and performance in science for girls on the 

other hand was not statistically significant at α=.05 with r=.085 (p=.558, n=127). Thus, self-efficacy 

explained 6.25% of the variance in science performance for boys and a meagre 0.72% for girls. Put 

differently, the correlation between the two variables was positive, significant and stronger for boys but 

positive, weaker and non-significant for girls. This outcome suggests that it is accurate to predict 

performance in science from self-efficacy for boys. However, predicting performance in science using 

self-efficacy as the predictor is inaccurate for girls.  

Considering that the correlation between science self-efficacy and performance in science was 

statistically significant for boys, Table 5 is a linear regression output for predicting performance in 

science from self-efficacy for boys. 

 

Table 5. Prediction of Performance in Science from Self-efficacy for Boys 

 Unstandardized Coefficients Standardized Coefficients t Sig. 

B Std. Error Beta 

 
(Constant) 16.598 7.966  2.084 .039 

Self-Efficacy 7.823 2.727 . 250 2.869 .005 

a. Dependent Variable: Performance in Science 

 

The Table indicates that the regression coefficient is B=7.823 (p=.005). Therefore, the slope of the 

regression line for predicting boys’ performance in science from their self-efficacy scores is 

significantly different from zero at α=.05. This means that a change of 7.823 units in science 

performance by boys is associated with a corresponding change of one unit in science self-efficacy. 

This finding affirms the FGD responses where participants were of the opinion that the level of 

confidence one has in science determines their level of performance in science. One male participant 

reported that: 

“You see, there is no way one can do what he or she feels is difficult. I do well in 

sciences since I am quite confident about my ability. At times I come across very 

challenging tasks but the fact that I believe in my capabilities, makes me go out of my 

way and succeed. And therefore self-efficacy is key in science performance and that is 

why those who doubt their abilities in science always fail in very simple tasks that 

don’t require much effort.” 



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4. Discussion 

The fact that boys did better than girls is consistent with the findings of Eriba and Sesugh (2006) and 

KNEC (2019). Eriba and Sesugh (2006) found that boys outperformed girls in integrated science and 

mathematics achievement. KNEC (2019) also found that boys scored better than girls in all Sciences in 

the 2017 KCSE examination. Similarly, the finding corresponds with that of Ochwa-Echel (2011) in 

Uganda in a project, dubbed Female Education in Mathematics and Science in Africa. The study found 

that girls’ performance in science subjects (which is the gateway to computer science studies) in the 

Uganda Certificate Examinations is very low compared to that of boys. 

Likewise, the finding is a true reflection of the situation in Kenya as recent literature shows that there 

has been poor performance in STEM subjects as a whole with girls performing even worse compared to 

boys (Forum for African Women Educationalists, 2008). The same position is held by Wambua (2007) 

who found that boys performed better than girls in STEM subjects. 

With the Science Achievement Test being developed from science questions in the KCSE examination, 

the study finding mirrors the true performance scenario on the ground as recent national KCSE 

examination results of 2017, 2018 and 2019 have shown that boys continue to perform better than girls 

in all the science subjects. For instance, in the 2017 KCSE results, of all the examinable subjects, boys 

scored better than girls in 23 subjects, defeating girls in all sciences, whereas girls only defeated boys 

in 6 subjects; English, Kiswahili, CRE, Home Science, Art, and Design and Electricity (KNEC Report, 

2018). Similarly, in the 2018 KCSE examination, girls lagged behind the boys in all the science and 

technology-related courses, including Biology, Chemistry, Computer Studies, Electricity, General 

Science, Mathematics, Physics, Power Mechanics, Agriculture, and Aviation. 

The current finding also corroborates other studies done in the local environment. According to the 

records at the Migori County Office, boys have continued to perform better than girls in the sciences. 

For instance, in the 2018 KCSE results, Migori County had girls scoring an average of 22.63% in all 

the sciences against the boys’ 26.65%, giving a gender disparity of 4.02% (Migori County Education 

Office Records, 2019). 

These outcomes are congruent with other reports that have found that among the other science subjects, 

Physics is normally the worst performed subject by girls. As observed by Carlone (2003), the gender 

gaps in Physics are among the most pronounced. 

The above notwithstanding, the current findings contradict that of Omoniyi (2006) who found that 

males are becoming the disadvantaged gender in schools, and that fewer males are interested in science. 

However, the result of his study was based on qualitative reports from respondents and not on actual 

performance of students. Therefore it is difficult to authenticate Omoniyi’s findings because students 

tend to exalt themselves before an interviewer thereby masking their true ability. 

The present study found that male students had higher levels of self-efficacy in science than their 

female counterparts. This is consistent with that of Ochieng (2015) who sought to establish the gender 



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differences in mathematics self-efficacy. In his study, Ochieng (2015) found that male students had 

higher levels of self–efficacy than female students. 

The relationship between self-efficacy and performance in science was found to be statistically 

significant in the present study. This is in agreement with Simpkins, Davis-Kean and Eccles (2006), 

Britner (2002, 2008); Britner and Pajare (2001, 2006); Zeldin and Pajares (2000), Hu,and Garcia (2001) 

and Silver, Smith and Greene (2001) who also found a significant relationship between the two 

variables. The studies have established that science self-efficacy has the ability to influence 

performance in science. In particular, they have argued that self-efficacy predicts intellectual 

performance better than skills alone, and it directly influences academic performance through cognition. 

Further, they suggest that although past achievement raises self-efficacy, it is student interpretation of 

past successes and failures that may be responsible for subsequent success, and self-efficacy predicts 

future achievement better than past performance. 

Similar to the findings of the current study, Diane (2003), Yazachew (2013), Aurah (2017), Farkota 

(2003) and Mustafa, Esma and Ertan (2012) found that self-efficacy is an important factor influencing 

students’ performance in science, and it affects their achievement positively. It is imperative that 

students with high science self-efficacy are likely to perform well in academic tasks. It may be argued 

that this happens because their high level of self-efficacy makes them approach challenging tasks with 

confidence and are therefore able to learn by practice. As a result, they are able to register high scores 

on tests. 

Noteworthy is that the finding in the current study is inconsistent with that of Titilayo, Oloyede and 

Adekunie (2016) who found that there was no significant relationship between self-efficacy and 

academic achievement of students in Chemistry in Nigeria. The Nigerian study however focused on 

Chemistry alone and left out other science subjects which may have led to the inconsistent results. The 

failure to have a relationship between the two variables could therefore be explained by the fact that 

Chemistry alone does not represent the entire science domain. Thus, lack of a significant relationship in 

the Nigerian study could have occurred due to focus on just one segment of science. 

 

5. Conclusion 

In light of the findings of the study, it is concluded that male students perform better than female 

students in science subjects in Migori County, Kenya. In addition, science self-efficacy influences 

performance in science, and an increase in the level of self-efficacy translates to a corresponding 

increase in the level of performance. However, there was more variance shared in common between the 

two variables for boys than for girls. It is recommended that teachers and counselors should work 

towards building the students’ level of science self-efficacy by providing extrinsic motivation. This 

should apply more particularly to the female students. It is through such an approach that gender 

disparity in science performance may be minimized. 



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