Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education

Hutagaol, Nindhia; Suharjito, Suharjito

doi:10.25046/aj040425

Cited by 26 publications

(35 citation statements)

References 20 publications

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“…Two of the analysed studies were conducted in Indonesian Universities. The work referred to in [94] considered data on 17,432 students enrolled between 2016 and 2018 in the Faculty of Social and Political Science of a private university in Jacarta. The study described in [95] refers to data on 425 students enrolled in information systems and in informatics engineering courses in an East Java university between the years 2009 and 2015.…”

Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

“…Furthermore, there are studies that evaluate and compare prediction models [73,77,92,94,100,108] in order to find the best suited ones for the problem at hand. There are also some approaches that aim at early prediction [88] for the anticipation of dropout [75] and risk of failure [86,107].…”

Section: How Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

“…Regarding personal information, the analysed studies take into account demographic data [71,84,105,119], such as the students' age [78][79][80][81][82]86,88,94,95,103,104,108,109,115], but also sociodemographic data [2], such as gender [78][79][80][81][82]86,88,94,95,98,103,104,[108][109][110]114,115], country of birth [88,104], race or ethnicity [108][109][110]115], regional origin or residence postal code [80,86,89,95,108,109,115], marital status ...…”

Section: In What Ways Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

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How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

Section: How Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

Section: In What Ways Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

See 1 more Smart Citation

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

View full text Add to dashboard Cite

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“…Nindhia Hutagaol [3] designed a system to find the best modeling approach for the identification of dropout student predictors from 17,432 private university student data in Jakarta. They also evaluated and calculated the association between demographic variables and academic performance to predict student dropout using three single classifiers: K-Nearest Neighbor (KNN), Naïve Bayes (NB) and Decision Tree (DT).…”

Section: Related Literaturementioning

confidence: 99%

“…Dropout determined as a consequence for students who cannot complete their education until the specified study period. It makes the skills and abilities of dropout students in their fields less than retention and significantly affects institution quality [3]. However, considering the importance of the subject, there is still a great deal of ignorance about the underlying indicators and consequences of dropout, as well as about the effective means of reducing student attrition [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

Gil¹

2020

IJATCSE

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Currently, one of the challenges of academic institutions is dropout student issues. It is critical to recognize which students are at risk of losing in school and what are the underlying factors of dropout. The study of students' dropout in Lawigan National High School enables teachers to identify the influential factors of dropout cases in school preemptively, to respond to it immediately, and to assist prospective dropout students in continuing studies and gaining knowledge for a better future. Lawigan National High School is a public school supervised with the Department of Education. The said school holds all the records of the students. The study aims to identify the underlying factors of dropout students that need to have an intervention to lessen the value of dropouts. The Weka Experiment Environment platform used to run simulations with machine learning algorithms from ready datasets. Two classification algorithms C4.5 and Naïve Bayes (NB) tested on a dataset containing student academic and demographic details, e.g., age, gender, marital status, written, performance, and quarterly examination grade, attendance percentage, home distance, parents' income, mothers' education, fathers' education, and dropout status, using a 10-fold cross-validation to estimate generalization accuracy. The experiment result shows that the top indicator for students' dropout cases is the academic performance with 98.9474% accuracy as perceived by the C4.5 algorithm, as shown in Table 2.

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Tucker's congruence regressive feature projected Tversky discriminant multiple instance learning boost data classification for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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Prediction of student dropout in high school is a significant concern in education that affects both a state's education system and its financial system. Early prediction of school student dropout is not an easy issue to resolve since many factors that can influence student retention. The traditional classification techniques were used to solve this problem normally but the higher accuracy was not obtained. In order to improve the accuracy, a novel technique called Tucker's Congruence Regressive Target Feature Matching‐based Tversky Discriminant MIL Boost Data Classification (TCRTFM‐TDMBDC) is introduced. The proposed TCRTFM‐TDMBDC technique consists of four different processes namely data preprocessing, feature extraction, feature selection, and classification. At first, the data preprocessing is carried out for cleaning and altering the raw input data into a valuable and understandable format to minimize the complexity of the classification. After the preprocessing, the feature extraction is carried out by applying Modified Tucker's congruence correlative regression. Thirdly, the feature selection process is performed using Gaussian kernelized target projection feature matching to select the feature subset for accurate classification with minimum time consumption. Finally, the ensemble technique called Tversky Indxive generalized discriminant MIL boost is applied for classifying the given input student data with help of the weak learners. Based on the classification results, the student dropout prediction is accurately performed with minimum time. Experimental results reveal that the proposed technique noticeably predicts student dropout by means of prediction accuracy, precision, recall, F‐measure, and prediction time with respect to the number of student data. The discussed results illustrate that the proposed TCRTFM‐TDMBDC technique achieves higher accuracy with minimum prediction time than the state‐of‐the‐art methods.

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Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education

Cited by 26 publications

References 20 publications

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

Tucker's congruence regressive feature projected Tversky discriminant multiple instance learning boost data classification for school student dropout prediction

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