Early Dropout Prediction Model: A Case Study of University Leveling Course Students

Sandoval-Palis, Iván; Naranjo, David; Vidal, Jack; Corbí, Raquel Gilar

doi:10.3390/su12229314

Cited by 37 publications

(41 citation statements)

References 32 publications

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“…As our results shown, given the impact of previous academic performance [70,92] and socio-emotional [32][33][34][35] and motivational aspects [88][89][90] on academic success or failure, it would be important to implement actions that enhance these to reduce the chances of academic failure. Therefore, improving the quality of teaching offered in higher education institutions, and making them more equitable and sustainable, is imperative to try to provide students with the resources and strategies necessary to successfully complete their training, and also to ensure that potential good professionals are not lost.…”

Section: Discussionmentioning

confidence: 99%

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

et al. 2020

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Universities are committed to offering quality education; however, a high rate of academic failure is often observed in the first year of studies. Considering the impact that motivation and emotional aspects can have on students’ commitment to study and therefore on their academic performance, achievement, and well-being, this study aims to identify the factors associated with academic success or failure in 1071 students entering the National Polytechnic School (Quito, Ecuador). The data were compiled from the existing computer records of the university with the permission of the responsible administrative staff. A predictive model has been used and a binary logistic regression analysis was carried out through the step-forward regression procedure based on the Wald statistic to analyze the predictive capacity of the variables related to emotional intelligence, motivational and self- regulated socio-cognitive skills, goal orientation, and prior academic achievement (measured by university entrance marks and through a knowledge test carried out at the beginning of the university academic year). To determine the cut-off point for the best discriminatory power of each of the variables, a Receiver Operating Characteristics (ROC) curve analysis has been used. The results indicate that the variables that are significant in the prediction of academic success or failure are the two academic performance measures: the emotional attention variable, and the performance-approach goals and the motivational self-efficacy variable. Additionally, the highest predictive power is displayed by the prior academic performance measure obtained through the knowledge test conducted at the beginning of the university course.

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Section: Discussionmentioning

confidence: 99%

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

et al. 2020

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“…In higher education research, student retention has been one of the most widely studied topics [22]. Historically, research on dropout has focused on freshmen and sophomore students [5,23], since the highest rates of dropout correspond to these groups. However, the existence of more rigorous courses after freshman year, may present a barrier to completion for many students, who accumulate credits slowly and finally drop out [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the number of graduates in undergraduate programs remains low, and pressure is being placed on universities in many countries to increase the number of graduates in order to meet the needs of society [4]. The main focus on dropout research has been on early dropout [5]. However, the problem of late dropout has been gaining importance in higher education [6].…”

Section: Introductionmentioning

confidence: 99%

Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout

et al. 2021

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Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention.

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“…Early school dropout should be seen as a complex dynamic process of progressive educational disengagement that involves individual, institutional and contextual factors [11][12][13][14]. The influence of student engagement is central in the analysis of the processes of early leaving from education and training and school perseverance [15,16]; that is to say, "active student engagement" positively influences learning processes and academic success [17] and acts as a preventive factor of dropout [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Emotional and Behavioural Engagement among Spanish Students in Vocational Education and Training

et al. 2021

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The purpose of this study is to analyse the importance of student engagement in Vocational Education and Training (VET) in Spain. In accordance with this concept, we analyse how emotional engagement (relations with teachers, relations with peers, family support for learning, and perception of family commitment) influences the behavioural engagement (school effort and commitment, school indiscipline) in academic activities of students in basic and intermediate VET. The sample comprises 1180 students (65.4% male), 28.8% in basic VET and 71.2% in intermediate VET, ranging in age from 14 to 19 years. Data analysis bases on linear regression and regression trees enable the prediction of behavioural engagement according to the subdimensions of emotional engagement, sociodemographic characteristics of the subpopulations, and level of studies. Significant differences were found (t(1013.8) = 8.37, p < 0.001) for the variable of sex (a higher value in females), and variable of the level of studies (t(579.1) = 3.60, p < 0.001) in behavioural engagement. All correlations between the indicators for the behavioural and emotional dimensions were significant. The results provide favourable profiles of behavioural engagement related to having good relationships with the teaching staff, being female, and being enrolled in intermediate VET. These findings imply the reorientation of educational intervention.

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Early Dropout Prediction Model: A Case Study of University Leveling Course Students

Cited by 37 publications

References 32 publications

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout

Emotional and Behavioural Engagement among Spanish Students in Vocational Education and Training

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