Model based on tutorial action and the undergraduate student's entry profile for the identification of variables that affect the risk of early dropout

Scientifically translate to English: The early dropout of students in the context of the first year of university degree programs is a subject of study, research, and prevention that involves both universities and public and private entities, families, and other student support entities. Most studies conducted have focused on quantifying this phenomenon based on student-associated variables. The present doctoral thesis, as an innovative approach, undertakes research to identify and quantify the factors that characterize the profile of entry-level students in private university degree programs in the Spanish state (in collaboration with three universities from different regions and disciplines), taking into account the tutor's perception. The research objective is to identify the potential risk of early dropout of first-year university degree students based on their entry profile and study-associated variables, and the intersection with the tutor's perception during the first two months of academic monitoring.

The research uses an ethnographic method based on two key factors: the influence of certain student characteristics at the beginning of the academic year collected through a personal survey by the tutors, and the perception of these tutors regarding the responses to the survey. The study focuses on two elements that inspire the current proposal: the need to quickly identify students who, due to their initial characteristics, are at higher risk of dropping out, as well as the importance of ensuring a successful start in the first year of their study program.

To conduct the research, a mixed approach combining qualitative and quantitative methods for data analysis is employed. On one hand, a quantitative instrument is designed and validated, which aggregates six dimensions: personal data, university access, current data, choice of major, study habits, and study time dedication. This set of variables identifies the potential dropout risk of the student. On the other hand, the weighting of variables and instrument refinement is done qualitatively through validation with tutors from each field of knowledge, school, and university, allowing for the characterization of student dropout risk by degree, school, and geographical area.

The results obtained allow for the categorization of the main student profile variables that influence dropout risk, establishing them as aspects to be monitored by tutors, who can use them as a guide to identify students in situations of greater vulnerability and provide them with appropriate support from the early stages of their academic journey.