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There are several statistical tools being used for students' performance analysis for information extraction and knowledge discovery. This paper presents data mining approach applied to discover students' performance patterns in supervised and unsupervised assessment instruments of a course in an engineering degree program. The interesting patterns emerging from this analysis promise to offer some helpful and constructive suggestions to educational administrators and decision makers in the sector of higher education for the improvement and revision of assessment methodologies, restructuring the curriculum, and trimming down the mismatch between the two modes of assessments.
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