@article{3330,
  keywords = {Academic Analytics, Dropout, Students Interaction, Learning Analytics, Prediction, Intelligent Tutoring Systems},
  author = {A. Llauró and David Fonseca and E. Villegas and M. Aláez and S. Romero},
  title = {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},
  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.},
  year = {9998},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {In Press},
  chapter = {1},
  number = {In Press},
  pages = {1-12},
  month = {06/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-06/ip2023_06_002.pdf},
  doi = {10.9781/ijimai.2023.06.002},
}