Generating descriptive model for student dropout: a review of clustering approach

The implementation of data mining is widely considered as a powerful instrument for acquiring new knowledge from a pile of historical data, which is normally left unstudied. This data driven methodology has proven effective to improve the quality of decision-making in several domains such as business, medical and complex engineering problems. Recently, educational data mining (EDM) has obtained a great deal of attention among educational researchers and computer scientists. In general, publications in the field of EDM focus on understanding student types and targeted marketing, using both descriptive and predictive models to maximize student retention. Inspired by previous attempts, this paper aims to establish the clustering approach as a practical guideline to explore student categories and characteristics, with the working example on a real dataset to illustrate analytical procedures and results.

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