Educational Data Mining: Enhancement of Student Performance model using Ensemble Methods

Ajibade, Samuel-Soma M.; Ahmad, Nor Ezzaty; Shamsuddin, Siti Mariyam

doi:10.1088/1757-899x/551/1/012061

Cited by 26 publications

(8 citation statements)

References 4 publications

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“…Excessive internet use also contributes to a variety of difficulties in people's lives, such as decreased work performance [9] [10] . It impairs one's ability to limit the amount of time spent online [11] . Internet addiction is described as the mental or emotional disturbance in a person's psychological state brought on by excessive internet usage [10] .…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of the Impact of Social Media Addiction on Academic Engagement of Students

2022

pnr

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The study's goal is to comprehend how internet addiction affects students' academic performance. However, very few research has been able to explain how excessive internet use causes students to lose interest in their academic work. Many studies have examined the detrimental association between addictions and academic performance. This research consists of two factors: internet addiction (emotional and cognitive preoccupation with internet and loss of control and interference with daily life) and academic engagement (enthusiasm and commitment). Through questionnaires, data was gathered from 186 students at a higher institution in Nigeria. Both correlation and regression were used to evaluate the data. The results of the investigation demonstrated that internet addiction significantly and unfavorably affects enthusiasm and commitment. It's interesting to note that internet obsession on an emotional or cognitive level was not shown to be a reliable indicator of internet addiction or loss of control.

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Section: Literature Reviewmentioning

confidence: 99%

An Analysis of the Impact of Social Media Addiction on Academic Engagement of Students

2022

pnr

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“…The student performance in a learning management system based on behavioral features was predicted by applying ensemble methods including bagging, boosting, and random forest to augment the performance of classifiers. An accuracy of 91.5% was achieved through the application of ensemble methods to the classifiers to enhance academic performance [60]. Ragab et al introduced a data mining-based forecast model to determine students' accomplishments.…”

Section: Comparison Of Applied Approach With Existing Approachesmentioning

confidence: 99%

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

et al. 2021

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In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

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“…The results shown that a deep learning model with binary crossentropy loss and sigmoid activation was best performer with classification accuracy 95.34%. Later in 2019, Ajibade et al [17] applied numerous classification algorithms on behavioural learning data of students to forecast the performance. In addition, they used Differential Evolution (DE) for behavioural feature selection.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning Techniques

Verma¹,

Srivastava²,

Singh³

2021

JUSST

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The COVID-19 pandemic has drastically changed the way od of learning. During this pandemic the learning has shifted from offline to online. student’s performance prediction based on their relevant information has emerged new area for educational institutions for improving teaching learning process, changes in course curriculum. Machine leaning technology can be helpful in predicting the performance of student and accordingly the institutions can make required changes in in their lecture delivery and curriculum. This paper utilized some machine learning methodologies to predict the students’ performance. Educational data of open University(OU) is analyzed Based on parameters that are demographic, engagement and performance. In the experimental analysis. In the experimental analysis, the k-NN approach performed best in some cases and ANN performed best in other cases among all compared algorithms on OU dataset.

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Educational Data Mining: Enhancement of Student Performance model using Ensemble Methods

Cited by 26 publications

References 4 publications

An Analysis of the Impact of Social Media Addiction on Academic Engagement of Students

An Analysis of the Impact of Social Media Addiction on Academic Engagement of Students

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning Techniques

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