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Title:
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IDENTIFYING INFLUENTIAL FACTORS IN STUDENT
DROPOUT USING DECISION TREES |
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Author(s):
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Daniel Plúa Morán, Mónica Martínez Gómez and Víctor Yeste |
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ISBN:
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978-989-8704-61-0 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías |
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Year:
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2024 |
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Edition:
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Single |
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Keywords:
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Decision Tree, KDD, Student Dropout |
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Type:
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Short |
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First Page:
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345 |
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Last Page:
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348 |
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Language:
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English |
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Cover:
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Full Contents:
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Paper Abstract:
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This document presents the development of a classification model to analyze the factors that influence a student at the
Universidad Politécnica Salesiana to drop out of their degree program. This analysis is based on data provided by the
university. The approach is based on classifications using decision trees. The methodology follows the Knowledge
Discovery in Databases (KDD) process and consists of five steps: selection, processing, transformation, data mining, and
evaluation. Using Python's Classification and Regression Tree (CART) algorithm, a tree with five levels and seventeen
rules was created to identify potential dropouts. It concludes that factors such as the level of studies, academic performance,
and the number of subjects taken by the student in a term are decisive in the decision to drop out. |
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