PREDICTION OF THE QUALITY OF UNIVERSITY LIFE THROUGH DATA MINING TECHNIQUES
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Abstract
The objective of this article is to measure through intelligent techniques, the quality of university life in a university population. In this investigation, a dependent variable called quality of university life is taken, as well as 10 independent variables: Academic load, economic resources, relationship with classmates, relationship with professors, curriculum, extracurricular activities, current housing, family relationships, emotional state and university environment. In the samplings of these variables, 127 surveys were carried out on university students of a public university located in the central region of Colombia. Subsequently, the most relevant variables were selected throughout statistical techniques, in order to establish a file to be analyzed through the decision tree classification algorithm J48from the Weka platform. The results show, with over an 80 % effectiveness, that the most influential variables in the quality of life of a university student are: University environment, current housing, emotional state, and relationships with professors. Finding a lot of times that the quality of university life can also depend of external variables to the university such as: Current housing and emotional state. These results are of great importance in the design of new university policies.
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