Knowledge Discovery on the Data on Dissolution of Classes of the Ateneo De Davao University

AUTHOR/S: Michelle P. Banawan, Antonio G. Bulao II, Jerry B. Canale , Jocel G. Catambacan

DATE COMPLETED: September 10, 2014



             Dissolution of classes is a constant dilemma of the Ateneo de Davao University. Although data on dissolved classes are not directly available, large and distributed data sets on similar and related context are present like data on student registration and academic classes, enrolment logs, class schedules and effective cuA complete list of SQL-query based findings is presented in trriculum which can be analyzed to discover patterns that lead to class dissolution and its understanding. At present, human intervention on the the prevention of dissolved classes still fails to address this phenomenon. With the understanding of its patterns, initial conditions, and behaviour (order or disorder), class dissolution will be better understood through this research study.

             Data on the classes dissolved since SY 2004 up to SY 2013 has been gathered, and analyzed using statistical analysis, query-based analysis and data mining techniques. Data analysis revealed the non-linear nature of the data. However, non linear regression models built and cross validated gives an R of 0.9929. Also association rules derived (confidence)99% and support)35%) revealed interesting rules on addressing the class dissolution problem in this University. Further, the results revealed the general tendencies of the data towards non linearity and dynamism. yet some order and pattern also exist thus allowing predictability thru the M5 based Pruned Tree Model derived in this study. With the behavior of the data, this paper used the knowledge derived from the data analysis to perform a qualitative analysis of this specific organizational system using the lenses of Chaos Theory.

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