Science education becomes more important for future national development globally in high-technology-based society. In reaction to the trend, the International Association for the Evaluation of Educational Achievement (IEA) has conducted achievement tests in science along with mathematics, called TIMSS every four years. In TIMSS 2003, while Korea was a higher-performing country, South Africa was ranked in the lower-performing countries. Korea features homogenous demography, centralized curriculum, and competitive educational zeal while South Africa is characterized by multicultural demography with various languages, and previously segregated schools based on races. The current research, which is a secondary analysis of TIMSS 2003 data, aimed at explaining the differences and similarities by identifying factors most likely to influence science achievement in the two countries. A conceptual research framework was built on the comprehensive literature review which involved mainly school effectiveness research and factors related to science achievement. The conceptual framework consists of multi-levels, viz., student, classroom, school, and context, and three key concepts, namely time on task, opportunity to learn, and quality. Two research questions were formulated to reach the goal of the research and the first question is: To what extent does TIMSS 2003 reflect factors related to effective science education? Data from the student, teacher and school questionnaires were included in conjunction with the achievement data and analysed by means of factor, reliability and correlation analyses. The factors found to influence science achievement in three levels are as follows: at the student level, books at home, attitudes towards science, time on task; at the classroom level, time scheduled for science and teacher interaction; at the school level, school size, community size, and student background. The second research question is: To what extent do the factors derived from the analysis explain the differences in the achievement of Korean and South African students? To answer this question, the current research used multilevel modeling techniques to deconstruct the total variance in achievement into within- and between-classroom/school level. The strongest predictor is attitudes towards science in both countries at the student level. Student background in Korea and safety in school in South Africa is the strongest predictor of science achievement at the classroom/school level. Furthermore, educational resources such as books at home and educational level of father are significant in Korea while language, teacher qualification, physical resources, and educational leadership are significant in South Africa. For Korea, 93% of total variance in science achievement occurred at the student level while only 7% was attributable to the classroom/school level. For South Africa, 41% of the total variance was assigned at the student level and 59% at the class/school level. From this comparative study, it was recommended that development of student-centred teaching practices to address negative attitudes to science in Korea be considered as opposed to basic issues such as improving teachers’ subject knowledge, developing language skills, and fostering a culture of learning to improve science performance in South Africa.
Bornman, Maria S. (Riana); Aneck-Hahn, Natalie H.(Health and Medical Publishing Group, 2012-05)
Male factor infertility is solely responsible in ∼20% of cases and contributory in ∼30 - 40%. Therefore, in at least 50% of cases a male factor for infertility contributes to failure to conceive. A semen analysis is still ...
Eikelboom, Robert H.; Tan, Hsern Ern; Santa Maria, Peter Luke; Anandacoomaraswamy, Surendran; Atlas, Marcus David(Lippincott Williams and Wilkins, 2016-06)
OBJECTIVE : To determine which independent variables influencing the efficacy of type I tympanoplasty in adult and pediatric populations.
DATA SOURCES : A search of the PubMed database and Cochrane Database of Systematic ...
Marchal, Kathleen; Pulido-Tamayo, Sergio; Duitama, Jorge(Oxford University Press, 2016-04-21)
Identification of genomic regions associated with a
phenotype of interest is a fundamental step toward
solving questions in biology and improving industrial
research. Bulk segregant analysis (BSA) combined