Open AccessUsing Affinity Analysis-Driven Adaptive Data Mining Life Cycle for the Development of a Student Retention DSS
Author(s) -
Pi-Sheng Deng
Publication year - 2021
Publication title -
wseas transactions on advances in engineering education
Language(s) - English
Resource type - Journals
eISSN - 2224-3410
pISSN - 1790-1979
DOI - 10.37394/232010.2021.18.12
Subject(s) - competition (biology) , set (abstract data type) , association rule learning , service (business) , retention rate , population , knowledge management , computer science , data science , process management , business , marketing , data mining , sociology , biology , ecology , demography , programming language
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