Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study

Alba Llauró, David Fonseca, Eva Villegas, Marian Aláez, Susana Romero

Research output: Indexed journal article Articlepeer-review

2 Citations (Scopus)

Abstract

The field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown and unlike traditional education, this has often involved taking online classes. In this scenario, the students' motivation and the way they are able to cope with the difficulties of the first year of a university course are very relevant, especially in technical degrees. Previous studies show that a large number of undergraduate students drop out prematurely. In order to act to reduce dropout rates, schools, especially technical schools, should be able to map the entry profile of students and identify the factors that promote early dropout. This paper focuses on identifying, categorizing and evaluating a number of indicators according to the perception of tutors and the field of study, based on the application of quantitative and qualitative techniques. The results support the approach taken, as they show how tutors can identify students at risk of dropping out at the beginning of the course and act proactively to monitor and motivate them.

Original languageEnglish
Pages (from-to)92-103
Number of pages12
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
Volume9
Issue number1
DOIs
Publication statusPublished - 2024

Keywords

  • Academic Analytics
  • Early Dropout
  • First-Year Students
  • Learning Analytics
  • Predictions
  • Tutoring

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