Trajectory of University Dropout: Investigating the Cumulative Effect of Academic Vulnerability and Proximity to Family Support

Abstract

University dropout is a major policy concern around the world because of its consequences for the individual, institutions and society. In this study, we offer new evidence by examining the cumulative effect of academic vulnerability and family support on trajectory of dropout among cohorts of undergraduate students in Thailand. Data were drawn from administrative records of two cohorts of students (n = 1,613), and consisted of information on which semester individual's dropped out of university. Using discrete time survival analysis, we first modeled the trajectory of dropout without predictors followed by a conditional model which examined the effects of predictors on the trajectory of dropout. Cumulative effects of predictors were then examined by plotting the probabilities of their combined effects on dropout in each semester. Our findings show that while the beginning of the second year was a critical period of dropout with almost 20% of students leaving by this time, as much as 10% of students drop out between the second and final year. Students with the lowest entry grades were about 2.17 times more likely to dropout while those who were farther away from family support were 1.32 times more likely to drop out across each semester. The cumulative effect of low entry grades and living away from family support resulted in a 30% probability of dropping out in the second year. The dropout rate among this category of students by the final year was 60% compared to only 14% for students with high entry grades and who live close to their families. Among other things, we recommend that interventions to reduce dropout should encompass both helping students to access family support and develop personal connections at university to compensate for absence of family support, as well as academically focused support for student who do not have a strong entry qualification.

Is it still worth it? Applying expectancy-value theory to investigate the intraindividual motivational process of forming intentions to drop out from university

“…Many theoretical models and studies on study dropout have not investigated direct dropout behavior (cf. Sosu and Pheunpha 2019). Rather, they have focused on the probability to drop out.…”

A Rapid Systematic Review Exploring the Involvement of Medical Students in Pandemics and Other Global Health Emergencies

“…28,29,39 Consequently, stress can lead 25 Increased severity (very dangerous and contagious compared to very infectious) 35,36 Greater level of knowledge 17,19,34 Pediatric patient care 34 History of a severe illness to them or family member 25 Respiratory transmission compared to contact 37 Less than 24 y old 25 Medical role (compared to admin role) 23 More than 100 h of previous volunteering 25 Natural disaster (compared to an infectious disease) 23 PPE availability 36 Training before work 23 Travel compensation 23 Well run organization 23 23,32 Academic commitments 24 CV improvement and gaining future contacts 23 Family health and safety concerns 26 Increase self-confidence in sim situations 23 Family/social commitments 24 Obligation/social responsibility 23,25 Financial implications 24 Professional and skills development 23 Inefficiency 26 Reduce guilt about less fortunate 23 Lack of confidence in skills 19 Sense of ethics 25 Lack of information 24 Needless sacrifice 26 Personal health and safety concerns 19 Work commitments 24 Involvement of Medical Students in Pandemics and Past Global Health Emergencies Disaster Medicine and Public Health Preparedness to a higher prevalence of university student dropout. 44 To avoid negative mental health impacts, adequate support must be provided to medical students if they are to be involved. This also highlights the need for further research that explores the short-and long-term effects on the students involved, and more specifically, the impact on their mental health and their future careers.…”

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

“…In the experiment, the study utilized two classification algorithms, namely: Naïve Bayesian and Decision Tree, using a 10-fold cross-validation strategy to arrive on the best model with the best prediction accuracy. The said model can be used in the analysis of student dropouts using the students' cumulative record for the academic administrations to come-up with necessary remediation activities to avoid if not minimize dropouts for students in the whole duration of students' journeys in the college [61], [62], [63], [64].…”