Prediction of student attrition risk using machine learning

Barramuño, Mauricio; Meza-Narváez, Claudia; Gálvez‐García, Germán

doi:10.1108/jarhe-02-2021-0073

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Different approaches are used with predicting students' performance such as [11]- [14], analyzing and predicting learners' performance [15]- [18], and academic institution performance [19]. Many approaches are used in prediction such as supervised machine learning approaches [20]- [24], unsupervised machine learning approaches [25]- [29] and semi-supervised machine learning approaches [30]- [33].…”

Section: Introductionmentioning

confidence: 99%

A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

Hamoud

Kamel²,

Gaafar³

et al. 2022

IJEECS

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The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (LMT, Simple Logistic, and Random Forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, EM, and Farthest First) are enhanced after applying feature selection approaches and SMOTE filter.

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Section: Introductionmentioning

confidence: 99%

A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

Hamoud

Kamel²,

Gaafar³

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Even though they are the high school dropout reasons, they can be applied to the university type of study. Several studies describing and modeling the students' dropout rate from the qualitative point of view have been conducted [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Success/Leaving Rate of the Military Students

VÁGNER¹,

HASILOVÁ²

2022

Challenges to National Defence in Contemporary Geopolitical Situation

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The military university type of education in the Czech Republic is provided by the University of Defence. As other universities, it has to deal with students prematurely leaving their studies. We have collected available data to assess students’ dropout rate. Using parametric and nonparametric methods, we identified that the highest leaving rate occurs during the first study year reaching a maximum after five and a half months from the studies beginning, then it gradually slows down. We assume that our results can help the academics and commanders to make timely decision and positively encourage the students in their studies.

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Latest Algorithms in Machine and Deep Learning Methods to Predict Retention Rates and Dropout in Higher Education: A Literature Review

Utomo,

Purwanto,

Surarso

2023

E3S Web Conf.

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External factors, such as global impact, or internal factors, such as educational services or the quality of learning, can affect the Retention rate or Number of Dropouts (DO) of students in higher education. Higher education institutions must have a strategy to manage retention rates properly. They can take an initial approach by knowing the estimated retention rate or the number of DOs so they can anticipate it by determining the right strategy. Several researchers have researched retention prediction or DO using specific methods and algorithms. This literature review aims to provide an overview and analysis of the methods and algorithms used to predict retention rates or the Number of DOs of students in higher education and to know the latest trends in developing the algorithms used in predicting retention rates or DO. The method used in this research is the traditional literature review. We have identified Twenty-one articles according to the theme. From the article, there are 21 machine learning algorithms, 13 deep learning algorithms used, seven time-series algorithms, four feature selection algorithms, and three combinations of algorithms used. The review results show that deep learning algorithms provide higher accuracy values than machine learning algorithms, and the recent trend of using algorithms to predict retention levels or DO is towards using time series algorithms in deep learning methods.

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Prediction of student attrition risk using machine learning

Cited by 7 publications

References 37 publications

A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

Success/Leaving Rate of the Military Students

Latest Algorithms in Machine and Deep Learning Methods to Predict Retention Rates and Dropout in Higher Education: A Literature Review

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