WSEAS Transactions on Advances in Engineering Education
Print ISSN: 1790-1979, E-ISSN: 2224-3410
Volume 18, 2021
Using Affinity Analysis-Driven Adaptive Data Mining Life Cycle for the Development of a Student Retention DSS
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Abstract: Technological development has engaged educational institutions in fierce global competition. To be competitive in meeting the changing needs of today’s student population, educational institutions find it imperative to prioritize student retention efforts and to develop strategies that interact and serve students more effectively in providing them more value and service. In this research we proposed a three-phase-six-stage adaptive data mining development life cycle, and we applied the affinity analysis to this methodology in identifying more than 400 association relationships with student retention, refining iteratively the association rule set down to less than 30 rules, and developing useful strategic implications regarding how the important factors were associated with a student’s decision. This set of implications and factors could then be integrated into the development of strategies for student retention
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Keywords: Affinity analysis, Association rules, Adaptive Data Mining Development Cycle, Student retention
Pages: 135-147
DOI: 10.37394/232010.2021.18.12