Determinants of Academic Achievement among Secondary School Students in Kericho County, Kenya
Naftali, Sang Kipchirchir
MetadataAfficher la notice complète
The purpose of this study was to find out influence of family background on learner’s cats (mocks) performance among Kipkelion east sub-county students. The Kenyan government had injected a lot of resources in education which include equipping schools by employing more teachers, building classrooms and pay students school fees in public secondary school. The mean score for mocks exams has been reducing for the last three years as follows, 2014 mean score was 5.52, 2015 the mean was 4.5, 2016 the mean was 4.13. Therefore study tried to find out if student mocks (cats) correlate with family background from which student came from. The objectives of this study were to investigate the effects of parental level of education, parental level of income, parental occupation and family size on student’s academic achievement. Correlational research design was applied to analyze the data. Education production function theory was used to guide the study. 1809 form four boys and girls aged between 17 and 20 years old enrolled in form four 2017 were the target population for the study. Simple random and stratified sampling method was employed to achieve the sample. The sample for the study were 10 schools using 30% (Mugenda and Mugenda, 2003) rule 200 students calculated using (stauffer and Etal, 2006) formula. Questionnaire for student was used to collect data. The instruments were presented to educational experts in education administration from the department of educational management policy and curriculum studies of Kenyatta University for face and content validation. Reliability of the instrument was obtained through test-retest. 0.7 correlation coeficiency was taken to mean that the instrument was reliable. The findings helped the policy makers, principals and parents to understand the effects of family background on student academic achievement and come up with mitigation measures. Two schools which are not part of the sample were used for piloting purpose. Multiple linear regression models were used to analyze the collected data and quantitative data was presented in tables. Researcher analyzed data collected quantitatively and quantitatively. Frequencies, regression coefficient and Pearson’s coefficient correlation was used to present data analyzed. SPSS version 20 was utilized to generate summarized information in tables. Analyzed results showed that parent level of education, parental occupation and parental income express positive relationship with the student academic achievement however the size of the family express negative relationship with student academic achievement. Parents should participate in academic activities of their children and further studies regarding student academic achievement and family background should be done.