Factors influencing university drop out rates

Araque, Francisco; Roldán, Concepción; Salguero, Alberto

doi:10.1016/j.compedu.2009.03.013

Cited by 203 publications

(141 citation statements)

References 9 publications

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“…Mendez et al made PAPER PREDICTION OF STUDENT DROPOUT IN E-LEARNING PROGRAM THROUGH THE USE OF MACHINE LEARNING METHOD use of the Decision Tree (DT) and random forest algorithms for analyzing the data of 2,232 science and engineering students and concluded that attributions such as the academic performance prior to enrollment and in the first year following admission and the number of courses undertaken were related to dropping out [24]. Araque et al made use of principal component analysis in conjunction with the stepwise regression method to analyze the data of 75,839 students from three universities in Southern Spain; results showed that attributions such as age, parents' education, academic performance, number of courses retaken could be used for explaining dropout behavior [25]. Zhe Ji et al made use of the structural equation modeling method for proving that social integration, external attribution, academic integration and academic disadvantages had impacts on student dropout [26].…”

Section: B Empirical Researchmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

119

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Abstract-The dropout high rate is a serious problem in Elearning programs. Thus, it is a concern of education administrators and researchers. Predicting the dropout potential of students is a workable solution for preventing dropouts. Based on the analysis of related literature, this study selected students' personal characteristics and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Networks (BNs). A large sample of 62,375 students was utilized in the procedures of model training and testing. The results of each model were presented in a confusion matrix and were analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective for student dropout prediction, but DT presented a better performance. Finally, some suggestions were made for future research.

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Section: B Empirical Researchmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

119

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“…El rendimiento académico previo al ingreso en la universidad es una variable que ha confirmado su influencia en la permanencia del alumno universitario (Antoni, 2003;Araque et al, 2009;Belloc, Maruotti y Petrella, 2010;Di Pietro, 2006;Rodríguez, 2011;Smith y Naylor, 2001). Algunos autores, entre los que destaca Broc (2011), han constatado que esta variable es la que mayor correlación presenta con el rendimiento académico final.…”

Section: Conclusionesunclassified

Persistence in university studies: The importance of a good start

García¹,

Bernardo²,

Rodrı́guez-Muñiz³

2016

RIFIE

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“…Consejero, García, Ibañez, and Ortega (2008) aim to identify and describe some psychosocial causes that affect the process of permanence at the University of Cadiz. There are other several studies in Spain regarding this topic (Araque, Roldán, & Salguero, 2009;Cabrera, Bethencourt, González, & Álvarez, 2006;Cobo, 2010;Corominas-Rovira, 2001;González, Álvarez, Bethencourth, & Cabrera, 2005;Lassibille & Navarro-Gómez, 2008;Rodríguez-Marín, 2004;Ruè-Domingo, 2014;Sposetti & Echeverría, 2000).…”

Section: Introductionmentioning

confidence: 99%

Higher Education Drop-Out in Spain—Particular Case of Universities in Galicia

Arce¹,

Crespo²,

Míguez‐Álvarez³

2015

IES

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The existence of a high university dropout rate in Spain is undeniable. Data shows that approximately one out of five students drop out from college. During the economic expansion period (2001)(2002)(2003)(2004)(2005)(2006)(2007) more students abandoned their studies than during the crisis period (2007)(2008)(2009)(2010)(2011). This situation also affects unemployment rates due to the fact that higher education leads to lower unemployment rates. Galicia presented slightly higher dropout rates than Spain from 2006 to 2009; nevertheless during the 2009/2010 academic year this rate was lower. The reasons for dropout can be divided into six distinct factors: psycho educational, evolutionary, family, economic, institutional and social factors. The main drop-out motives are related, among others, to incompatibility with work, economy, university (environment, teachers and difficulties) and so on. According to the BBVA research, the dropout rate imposes a great cost on the entire economy reaching the sum of 5,772 euros per year and per student and exceeding the amount of 1,500 million euros if the student drops out during the first cycle. Due to the urgent need of reducing the dropout rate, the Ministry of Education developed an action plan to increase the number of young people enrolled in a higher education course; so that, consequently, Spain can redirect its economic growth model.

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Factors influencing university drop out rates

Cited by 203 publications

References 9 publications

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Persistence in university studies: The importance of a good start

Higher Education Drop-Out in Spain—Particular Case of Universities in Galicia

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