Modeling of university dropout using Markov chains

Abstract

Access to higher education is only a first step in achieving equity in education; the following step is improving student retention, or lowering dropout rates, which is the same thing. The present study focused on the definition of an index as an estimator of the risk of individuals dropping out of a university using a Markov chain model, based on the randomness of the occurrence of dropping out. The suggested index was applied to a sample of 5,700 university students from the 2012-2015 annual cohorts of 8 university departments of a public regional university in Chile. The results indicate that the highest average probability of dropping out (slightly more than 39%) occurs in the first 2 semesters of university studies, and then decreases through time. This indicates the need for institutional retention policies that pay particular attention to the first year of university studies. Having this index also allows a formal estimation of changes or temporary variations in the risk, as well as quantifying the impact of interventions, not only for the case under study but for the entire higher education system.

Deserción Estudiantil De La Licenciatura en Docencia De Matemática De La Universidad De Panamá

“…Tinto (1975) define dos tipos de deserción: los que no terminan la matrícula y los que no obtienen el título de licenciatura. En ambos casos existe una implicación a los recursos del país, ya que el estado invierte parte del PIB en estudiantes que abandonan sus estudios en el primer año (González-Campos et al, 2020;Sandoval-Palis et al, 2020).…”

Probability of Continuous use of the Delivery Service in a Lima Community: an Application of the Markov Chains

“…As for methodological procedures, the Markov chain is a technique that allows probabilistic forecasts, as well as being a methodological alternative with which potential These tools are considered successive continuous processes of random variables that are dependent; Thus, the future state of an event depends on its current position and not on past events. (González-Campos et al, 2020), because it incorporates systems that modify its state over time; therefore, it is highly plausible to execute the decision-making that will affect or favor our interests. (Panday et al, 2020).…”

“…In the states, we denote as 0 the representation of the The initial probability would be denoted as P(X0= 0) symbolized as the probability that the user will use the delivery service. In the following state, the probability in normality with the same user behavior is denoted as P(Xn+1=0/Xn=0); in addition, the one who is a non-user and who would use in the future would be the following P(Xn+1=1/Xn=0); P(Xn+1=1/Xn=1); P(Xn+1=0/Xn=1) (González-Campos et al, 2020).…”

Modelagem e Simulação do Progresso Estudantil no Ensino Superior com Sistemas de Eventos Discretos para Gerenciamento de Capacidade de Turmas Extras

“…A pesquisa de [González-Campos et al 2020] propôs um modelo de estimativa de risco de evasão individual, utilizando cadeias de Markov, com base na aleatoriedade da ocorrência do fenômeno de evasão. A pesquisa foi aplicada com 5700 estudantes de 8 universidades.…”