Autonomous Cycles of Data Analysis based on Process Mining for the Study of the Curricular Behavior of Students
Abstract
In this work, the curricular behavior of the students of a master's degree program is evaluated through Process Mining. Specifically, what is related to the determination of the internal and external factors that affect the pursuit of their studies is analyzed. To understand student behavior, the MIDANO methodology is used, which has been used for the development of data analytics applications. In particular, it is specified the Autonomous Cycles of data analysis tasks that allow studying the dropout of the master's degree program during schooling or during the development of graduate thesis, in order to determine the causes or problems that arise during the pursuit of the studies. Very encouraging results were obtained on the causes of the dropout of the master's degree program, which discover the autonomous cycles.
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References
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