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Abstract
Educational data mining is a growing research area defined as the application of data mining techniques on raw data from academic institutions to solve educational problems by discovering the information hidden in the educational data. Educational data mining incorporates developing predictive models, visualizing educational data and employing machine learning algorithms and statistical methods to explore hidden relationship using the statistical methods. In this research, we have employed relationship mining to explore the correlation between the features in student dataset. In correlational analysis to investigate the relationship among student dataset features, we have employed Pearson’s Correlation Coefficient (R). The dataset consists of senior undergraduate (second year, third year and fourth year) Computer Science, Information Technology and Information System female students’ data collected through questionnaire at Aksum University (Axum, Ethiopia). The goal of the correlational analysis is to investigate the feature (s) that have strong relationship to female students’ academic performance or cumulative grade point average (CGPA). The investigation on the relationship among the female students’ dataset features is important to determine the feature(s) that have strong correlation to the academic performance or the cumulative grade point average (CGPA) of the female student. The findings of this research reveals that class attendance has strong relationship with academic performance of female student.
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References
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