Predicting Students’ Performance of the Private Universities of Bangladesh using Machine Learning Approaches

Zulfiker, Md. Sabab; Kabir, Nasrin; Amin, Al; Chakraborty, Partha; Mahfujur, Md.

doi:10.14569/ijacsa.2020.0110383

Cited by 29 publications

(6 citation statements)

References 14 publications

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“…In their study, they used 82 ischemic stroke patient data sets, two ANN models, and the accuracy values of 79 and 95 percent. Cheon et al [7][8][9] conducted research to determine the predictability of a stroke patient death. ey identified the stroke incidence using 15,099 individuals in their research.…”

Section: Introductionmentioning

confidence: 99%

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Tazin

Alam

Dola

et al. 2021

Journal of Healthcare Engineering

104

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Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction. Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable. Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis.

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Section: Introductionmentioning

confidence: 99%

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Tazin

Alam

Dola

et al. 2021

Journal of Healthcare Engineering

104

View full text Add to dashboard Cite

show abstract

“…Zulfiker et al [9] discussed the students who were accepted each year into various universities in Bangladesh. They can improve their grades by taking the necessary action and forecasting their results before the final exam.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based Stream Selection of Secondary School Students in Bangladesh

Ahmad

Alam

Uddin

et al. 2023

IJEEI

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In the Bangladeshi education system, there are three stages up to the secondary school certificate (SSC)-the primary (Primary Education Completion Certificate, or PEC), middle school (Junior School Certificate, or JSC), and SSC. A separate stream has to be chosen after the eighth grade, which could be any of the following streams: Science, Business Studies, and Humanities. The selection of a stream is very important for their future higher studies and career planning. Usually, students take the decision of selecting a stream based on PSC and JSC results only. To address this challenge, we have collected a dataset from different Bangladeshi schools, which consists of PSC and JSC students' records. There are 26 data for each student record including subject-wise student results, parent's academic qualification, parent's profession, parent's monthly income, sibling information, district, etc. In the experimental analysis, a series of machine learning regression algorithms have been utilized. Moreover, we have employed various performance metrics in order to validate our model's performance. The experimental results demonstrate that among the regressors, extreme gradient boosting algorithm's performance were superior in both science and humanities streams. In the business stream however, Support Vector Machine's performance is considerably better. It is expected that the analysis will help prospective students and stakeholders in their future decisions. Moreover, we have utilized Local Interpretable Model Agnostic Explanations that helps to increase the interpretability of the model.

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“…Another research study compared the accuracy of several methods namely, k-nearest neighbor (KNN), support vector machine (SVM), decision tree, logistic regression, multilayer perceptron (MLP), adaboost, and extra tree classifier in the prediction of students' achievement [19]. Although the first classification included predicting four classes which are excellent, good, poor, and fail, the accuracy was highly improved after categorizing the excellent and good into one class and the poor and fail into the other class.…”

Section: Related Literaturementioning

confidence: 99%

Using Socio-Demographic Information in Predicting Students' Degree Completion based on a Dynamic Model

2022

IJIES

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Educational institutions prioritize identifying variables and features that can help understand students' completion in higher education. This research aims at predicting students' completion based on socio-demographic information. Unlike previous work, a dynamic prediction model was proposed here. Real data of 13262 undergraduate students at a public university in Iraq was integrated and cleaned first. This was followed by applying several feature selection techniques to identify relevant socio-demographic features that can affect the prediction of students' completion. This includes using correlation-based feature subset selection (CfsSubsetEval), symmetrical uncertainty with respect to the class (SymmetricalUncertAttributeEval), GainRatioAttributeEval, ReliefFAttributeEval, and CorrelationAttributeEval. Finally, a prediction model was built based on six prediction approaches which are Bagging, DecisionTable, HoeffdingTree, IBK, J48, RandomForest, and RandomTree. Overall, several different features showed a significant effect on students' completion in which societal regression and the type of secondary school had the highest weight. Furthermore, comparing the accuracy of the implemented classification techniques can reveal that the Bagging classifier outperforms other approaches. The accuracy of predicting students' completion based on this method was 87.56%. Drawing on the research outcomes, educational institutions should pay further consideration to the identified features to ensure students' success and degree completion.

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Predicting Students’ Performance of the Private Universities of Bangladesh using Machine Learning Approaches

Cited by 29 publications

References 14 publications

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Machine Learning based Stream Selection of Secondary School Students in Bangladesh

Using Socio-Demographic Information in Predicting Students' Degree Completion based on a Dynamic Model

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