Prediction of Student’s Dropout from a University Program

Shynarbek, Nurdaulet; Orynbassar, Alibek; Sapazhanov, Yershat; Kadyrov, Shirali

doi:10.1109/icecco53203.2021.9663763

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…The ANN and the Naïve Bayes models were selected due to their reported high performance in classification tasks [39,44]. Two classification tasks were carried out in both models: one to predict students' regularity and the other to predict their irregularity.…”

Section: Data Mining Modelsmentioning

confidence: 99%

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

Monteverde-Suárez,

González-Flores,

Santos-Solórzano

et al. 2024

BMC Med Educ

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Background Dropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history. Methods Data from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis. Results ANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable. Conclusions Both ANN and Naïve Bayes methods can be useful for predicting medical students’ academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

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Section: Data Mining Modelsmentioning

confidence: 99%

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

Monteverde-Suárez,

González-Flores,

Santos-Solórzano

et al. 2024

BMC Med Educ

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“…Despite the high dropout rate of 72.8% in their data, the problem of data imbalance was not discussed in their paper. Shynarbek et al [33] used data collected from 366 students in the Department of Computer Science at Suleyman Demirel University. As an algorithm to implement a model, DNN, LR, NB, and SVM were used, and the NB model showed the highest F1-score of 0.96.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding [23], it was excluded from our comparison because there was a problem with its classification criterion. In the case of [33], the data used for verification was insufficient, and the dropout rate was not also presented, which makes direct comparison difficult. So, it was also excluded from the comparison.…”

Section: Related Workmentioning

confidence: 99%

A Study on Dropout Prediction for University Students Using Machine Learning

Cho,

Yu,

Kim

2023

Applied Sciences

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Student dropout is a serious issue in that it not only affects the individual students who drop out but also has negative impacts on the former university, family, and society together. To resolve this, various attempts have been made to predict student dropout using machine learning. This paper presents a model to predict student dropout at Sahmyook University using machine learning. Academic records collected from 20,050 students of the university were analyzed and used for learning. Various machine learning algorithms were used to implement the model, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Deep Neural Network, and LightGBM (Light Gradient Boosting Machine), and their performances were compared through experiments. We also discuss the influence of oversampling used to resolve data imbalance issues in the dropout data. For this purpose, various oversampling algorithms such as SMOTE, ADASYN, and Borderline-SMOTE were tested. Our experimental results showed that the proposed model implemented using LightGBM provided the best performance with an F1-score of 0.840, which is higher than the results of previous studies discussing the dropout prediction with the issue of class imbalance.

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“…In order to achieve this, academic data of first-year undergraduate Computer Science of the University of Benin between year 2016 to 2020 were considered. In previous study, Nurdaulet et al (2021) predicted dropout and graduation from an undergraduate computer science program in a higher educational institution. The data used were sourced from the students who started the degree programme in the year 2016 and 2017.…”

Section: Introductionmentioning

confidence: 96%

Student Dropout Prediction Using Machine Learning

Osemwegie,

Amadin,

Uduehi

2023

FJS

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In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn't much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.

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Prediction of Student’s Dropout from a University Program

Cited by 9 publications

References 15 publications

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

A Study on Dropout Prediction for University Students Using Machine Learning

Student Dropout Prediction Using Machine Learning

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