Data mining to identify risk factors associated with university students dropout
Pre-Publicación
2019-07-26
Communications in Computer and Information Science
. This paper presents the identification of university students dropout
patterns by means of data mining techniques. The database consists of a series of
questionnaires and interviews to students from several universities in Colombia.
The information was processed by the Weka software following the Knowledge
Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial
results of data mining processing on the information about the generations of
students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues
that indicate a probable desertion risk in students with common behaviors.
These relationships provide enough and appropriate information for the
decision-making process in the treatment of university dropout.
- Artículos científicos [3156]
Descripción:
Data Mining to Identify Risk Factors.pdf
Título: Data Mining to Identify Risk Factors.pdf
Tamaño: 145.0Kb
PDFLEER EN FLIP
Título: Data Mining to Identify Risk Factors.pdf
Tamaño: 145.0Kb
PDFLEER EN FLIP
The following license files are associated with this item: