A Machine Learning Approach to Identifying Students at Risk of Dropout: A Case Study

Lottering, Roderick; Hans, Robert T.; Lall, Manoj

doi:10.14569/ijacsa.2020.0111052

Cited by 7 publications

(9 citation statements)

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“…On the other hand, the results of [26] showed a precision rate of 89.31% and a specificity rate of 91.25%, these measures are substantial to select classifiers since the researcher intends to minimize false negatives.…”

Section: Discussionmentioning

confidence: 98%

“…As indicated in [24], Machine Learning is a set of algorithms capable of learning to perform certain tasks from the generalization of examples. Machine Learning has been successfully applied to a variety of areas of human endeavor, and has recently been applied to the educational sector, whose purpose is oriented towards the design of algorithms, methods and models, which will allow the exploration of data from teaching-learning environments [25], [26].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Chamorro-Atalaya¹,

Chávez-Herrera²,

Santos³

et al. 2021

IJACSA

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The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the higher institution to have a database, to be used in the process of improving the quality of the educational service in relation to teaching performance.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Chamorro-Atalaya¹,

Chávez-Herrera²,

Santos³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…The features vary between demographic previous academic performance and current performance. To evaluate the algorithms, the authors prioritized accuracy, sensibility and sensitivity than the metrics TP (true positive), TN (true negative), FP (false positive) and FN (false negative), because in this case it is about binary classification [27].…”

Section: Related Workmentioning

confidence: 99%

“…There are articles that have opted for a single Machine Learning algorithm to implement a model for predicting students' abandonment and performance. These kinds of studies sometimes focus on a single curriculum or different programs or even different universities to compare the effectiveness of the prediction among them as it is in [5], [6], [14], [19], [20], [27].…”

Section: Comment and Syntax Presentationmentioning

confidence: 99%

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

Oqaidi

Aouhassi

Mansouri

2022

Int. J. Emerg. Technol. Learn.

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Using machine learning to predict students’ dropout in higher education institutions and programs has proven to be effective in many use cases. In an approach based on machine learning algorithms to detect students at risk of dropout, there are three main factors: the choice of features likely to influence a partial or total stop of the student, the choice of the algorithm to implement a prediction model, and the choice of the evaluation metrics to monitor and assess the credibility of the results. This paper aims to provide a diagnosis of machine learning techniques used to detect students’ dropout in higher education programs, a critical analysis of the limitations of the models proposed in the literature, as well as the major contribution of this arti-cle is to present recommendations that may resolve the lack of global model that can be generalized in all the higher education institutions at least in the same country or in the same university.

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“…It employs an iterative algorithm to acquire knowledge and discover hidden knowledge. Zeineddine et al [44] achieved up to 83% accuracy with their ensemble model, while Lottering et al [21] reported 94.14% ac-curacy using another classification algorithm. Baashar et al [6] analyzed student academic performance with Neural Network Back Propagation algorithm, obtaining an accuracy of 89%.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Supriyadi,

Purwanto,

Warsito

2023

mendel

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The features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%.

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A Machine Learning Approach to Identifying Students at Risk of Dropout: A Case Study

Cited by 7 publications

References 0 publications

Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

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