Implementation of a Predictive Information System for University Dropout Prevention

Guzmán-Castillo, Stefania; Körner, Franziska; Pantoja-García, Julia I.; Nieto-Ramos, Lainet; Gómez-Charris, Yulineth; Castro-Sarmiento, Alex; Romero-Conrado, Alfonso R.

doi:10.1016/j.procs.2021.12.287

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…However, they are limited to the engineering field of study [17], only discuss dropout predictions based on previous semester results [6], or are based on factors and educational agents [15]. Some develop dropout prediction systems by calculating predictions per student [10] and subsequently develop an uplift model to prevent dropouts [18]. These studies observe that psychological factors can increase dropout cases [21].…”

Section: E Discussionmentioning

confidence: 99%

“…These analyses also focus on the initial study [3][4][5][6]17] period. The progress is realized from one cluster to another during the study period [3,13], and its effect on the probability of dropping out is observed further [4,6,10,15,17,18].…”

Section: Introductionmentioning

confidence: 94%

“…The previous studies conducted [3][4][5][6][7][8][9][10] focus on determining the academic performance of undergraduates. The majority of these studies state that the observation process can be assisted using data mining [3-7, 9, 11-15], and clustering [3,5,11,12,14] techniques.…”

Section: Introductionmentioning

confidence: 99%

“…There is a need for early intervention before it is too late. Moreover, a predictive information system is used to prevent students from dropping out of university [10]. The developed model calculates the dropout risk per student by providing a warning procedure for the coordination of early preventive measures adjusted to the risk level.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Setiabudi,

Santoso

2023

CommIT (Communication and Information Technology) Journal

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Educational data mining is a technique to evaluate educational process of university students, especially in their early stages. Most preliminary studies focus on observing courses undertaken by students from one semester to the next to predict their success rate. However, besides studying, many students are also involved in non-academic activities, which tends to affect their grades. Therefore, the research aims to determine the effect of student activities on grades while taking into account their academic activities. The method used for clustering is K-Means. Data are collected by observing students’ activity patterns in lectures. The research is conducted in two study programs at Petra Christian University: Business Management and Architecture. The results show that the K-Means method gives good results. The clusters formed from the data show non-homogenous groups and produce insights from several groups. The results show a tendency for students’ performance to increase along with the number of activities and points earned. Most students have increased activities during busy times in the third, fourth, fifth, and sixth semesters. The peak is between the fifth and sixth semesters. Then, it starts to decrease in the seventh and eighth semesters. Therefore, students’ activities in the Business Management study program affect performance significantly. Meanwhile, in the Architecture study program, it has an insignificant effect on performance.

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Section: E Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Setiabudi,

Santoso

2023

CommIT (Communication and Information Technology) Journal

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“…Los cambios en la sociedad contemporánea deben ser tomados en cuenta a la hora de implementar la pedagogía, la cual requiere un enfoque diferente para que los estudiantes aprendan, así como la implementación de las tecnologías de la información (Chow-White & Flores-López, 2021; Sanhueza Gutiérrez et al, 2021). Los factores socioeconómicos representan un componente ineludible para determinar la permanencia del estudiante en una institución educativa (Núñez-Naranjo et al, 2021), dependiendo de las condiciones de la casa de estudios, y que muchas veces sobrepasan la capacidad socioeconómica de este, promoviendo el abandono universitario (Aina et al 2022;Guzmán-Castillo et al 2022). Los diferentes niveles socioeconómicos influyen significativamente en la relación entre los estudiantes y la universidad e incrementa notoriamente la posibilidad de abandono educativo (Mtshweni, 2021;Tayebi et al 2021).…”

Section: Factores Académicosunclassified

Los factores que influyen en la deserción universitaria

Vargas Gutierrez,

Tello Díaz

et al. 2023

rcli

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El abandono parcial o total en el proceso educativo universitario es un tema complejo, relacionado a problemas de fracaso personal, la falta de adaptación por parte del estudiante a las actividades universitarias, acompañado de rasgos de estrés y baja autoestima, provoca una insatisfacción sobre su actividad formativa. Los procesos educativos están afectados por la globalización, agrandando la brecha entre las características educativas de las instituciones públicas versus las privadas, obligando a los estudiantes a tomar decisiones que muchas veces desencadenan en un posterior abandono de la formación universitaria. La manera en que los cambios sociales, culturales y económicos afectan al proceso académico tienen relación con las políticas educativas motivando la renovación de los planes de estudio para contener el incremento en el incide abandono educativo. El presente artículo de revisión pretende analizar los factores que influyen en la deserción universitaria el cual expone los testimonios de diferentes autores analizando de manera técnica y reflexiva las dimensiones que originan la deserción universitaria, concluyendo en que la deserción de los estudiantes universitarios puede deberse a una variedad de factores, voluntarios o involuntarios, cualquiera que sea la causa, el ausentismo tiene un alto impacto no solo en los estudiantes que abandonan, sino en sus familias, instituciones y la sociedad en su conjunto.

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Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka-Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS-BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS-BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka-Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student-Drop-India2016 dataset with different metrics such as prediction accuracy, precision, recall, F-measure, as well as time. The result of proposed RBOOSEFS-BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state-of-the-art methods. K E Y W O R D Sbivariate kernelized regressive bootstrap aggregative classification, Otsuka-Ochiai stochastic neighbor embedded feature selection, preprocessing, rank biserial correlation-based feature extraction, school student dropout prediction INTRODUCTIONStudents' dropout is a difficult issue in the learning process for both the individual and society. Early prediction is a main challenge in the dropout prediction. Predicting the student's at-risk of dropout offers various benefits to educators, students, and the educational institution. Machine learning algorithms were applied in education to address the predicting dropout of students issue from schools. So that potential drop outs are documented

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Implementation of a Predictive Information System for University Dropout Prevention

Cited by 11 publications

References 14 publications

Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Los factores que influyen en la deserción universitaria

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

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