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http://hdl.handle.net/123456789/1770
Title: | Identification of outstanding performances in senior high school examination |
Authors: | Awi, Lawrence Kwesi |
Issue Date: | Jul-2010 |
Publisher: | University of Cape Coast |
Abstract: | The study looks at identification of outstanding and abysmal performances of students in examinations involving seven common subjects taken in the first year at Ghana Senior High School in Koforidua. The scores of the students, who were grouped into six different program classes, in the seven subjects constituted the data for the study. In order to achieve the objective of the study using this high dimensional data set, a multivariate data analysis technique (the Principal Components Analysis) was used as the main statistical tool to examine the variance-covariance structure of the performance in the seven subjects. The study reveals that the first principal component is a weighted sum of all the subjects offered by the students. As a result, the first component is found to be the most appropriate index in determining the general performances of the students. Core Mathematics and French are observed to be the two most influential subjects in the formation of the first component. Thus, in the determination of general performance of students, Core Mathematics and French are the most influential of all the seven subjects. Using the scores of the first principal component, it is discovered that the three best students are all members of the General Arts class. The worst scores are recorded by a Science student; the second worst scores are obtained by a student of Home Economics class; and the third worst student was from the Science class. The study also reveals that in general, the Visual Arts class is the strongest class whilst the Agric class is the weakest among the six classes. The performances of Business, General Arts and the Science classes are quite normal. |
Description: | viii, 92p. : ill. |
URI: | http://hdl.handle.net/123456789/1770 |
ISSN: | 23105496 |
Appears in Collections: | Department of Mathematics & Statistics |
Files in This Item:
File | Description | Size | Format | |
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AWI 2010.pdf | Thesis | 713.79 kB | Adobe PDF | View/Open |
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