Predictive data analysis techniques applied to dropping out of university studies

Cindy Espinoza Aguirre, Jesus Carretero Perez

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Student dropout is a major problem in university studies all around the world. To alleviate this problem, it is important to detect as soon as possible student attrition before he or she becomes a deserter. A student may be considered a deserter when she/he has not completed her academic credits or leave the studies. In this paper we present a study made at a higher education institution, by analyzing the records of 530 higher education students from 52 different careers with application date 2015 to 2018, considering factors such as academic monitoring, financial situation, personal and social information. These are some issues or mix of problems that could affect dropout rates. Analyze student behavior by implementing predictive analytics techniques reduce the gaps between professional demands and applicants' competencies. We applied predictive analytical techniques to identify the relationship of factors characterizing students who leave the university. As a result, we have elaborated a conceptual model to predict the risk of defection and applied machine learning techniques to generate preventive and corrective alerts as a student permanence strategy. This study shows that information is important, but the application of machine learning in the student's prior knowledge and its relationship to a dynamic and pre-established profile of the deserter student is essential to generate early strategies that manage to reduce the gaps between professional demands and applicants' competencies. In addition, a data model has been created to give solution to the issue get generated preventive and corrective alerts.

    Original languageEnglish
    Title of host publicationProceedings - 2020 46th Latin American Computing Conference, CLEI 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages512-521
    Number of pages10
    ISBN (Electronic)9780738130644
    DOIs
    StatePublished - Oct 2020
    Event46th Latin American Computing Conference, CLEI 2020 - Virtual, Loja, Ecuador
    Duration: 19 Oct 202023 Oct 2020

    Publication series

    NameProceedings - 2020 46th Latin American Computing Conference, CLEI 2020

    Conference

    Conference46th Latin American Computing Conference, CLEI 2020
    Country/TerritoryEcuador
    CityVirtual, Loja
    Period19/10/2023/10/20

    Keywords

    • Dropout model
    • Student retention in higher education
    • University dropout prediction

    Fingerprint

    Dive into the research topics of 'Predictive data analysis techniques applied to dropping out of university studies'. Together they form a unique fingerprint.

    Cite this