Data Mining: A prediction for performance improvement using classification

Bhardwaj, Brijesh Kumar; Pal, Saurabh

doi:10.48550/arxiv.1201.3418

Cited by 12 publications

(9 citation statements)

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“…In learning environments, the capacity to determine a student's performance is critical. The utilization of Data Mining is a very promising approach for achieving this goal [24]. Data mining techniques are applied to large amounts of data to uncover hidden patterns and relationships that aid decision-making.…”

Section: Data Mining Techniques and Resultsmentioning

confidence: 99%

A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook

Abdel‐Rahman

Soliman

Taloba

et al. 2022

Preprint

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With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into the performance of their students and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms on data from a data structures and algorithms course (CS2) offered in a large public research university. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification for this data was Random Forest Classifier with an accuracy equal to 91.7% while in the regression it was Random Forest Regression with an R² equal to 0.977.

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Section: Data Mining Techniques and Resultsmentioning

confidence: 99%

A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook

Abdel‐Rahman

Soliman

Taloba

et al. 2022

Preprint

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“…This technique is necessary to differentiate between advanced beginners and gradual beginners. Bhardwaj et al, [23] proposed a predictive student achievement model incorporating multiple classifiers. The suggested model, AODE, combines the aggregation of One-Dependence Estimators and K-Nearest neighbors.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Naseem

Mahmood

et al. 2023

Preprint

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Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X template outcomes. Educational Data Mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel Federated Learning (FL) standard that ensures the confidentiality of the dataset and allows predicting student grades, categorized into four levels: Low, Good, Average, and Drop. To enhance model precision, optimized features are incorporated into the training process. This study evaluates the optimized dataset using five machine learning (ML) algorithms , namely Support Vector Machine (SVM), Decision Tree, Nã A¯ve Bayes, K-Nearest Neighbours, and the proposed Federated Learning Model. The models’ performance is assessed regarding accuracy, precision , recall, and F1 score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of Federated Learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

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“…Various studies were conducted to find out what factors could affect students' academic performance. According to Bhardwaj and Pal [2], the academic performance of students depends on personal, social, economic and environmental factors. Feng et al [3] conducted a study on the effect of social media use on academic performance on 100 students at a University in Hong Kong, the result showing a positive relationship between the high social media use on the low academic performance rate of students.…”

Section: Introduction 11 Research Backgroundmentioning

confidence: 99%

“…Referring from conducted studies related to academic performance prediction, a number of influential factors can be shown, namely personal, social media, parents' economy, the environment, and parents' education levels. Unfortunately a number of studies have been conducted using only a few factors [2][3][4][5], so it cannot be shown which factors are actually the most dominant of all the factors that have been used in a number of such studies.…”

Section: Introduction 11 Research Backgroundmentioning

confidence: 99%

Student Performance Prediction in Sebelas Maret University Based on the Random Forest Algorithm

Gusnina¹,

Wiharto²,

Salamah³

2022

ISI

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Students who have low levels of academic performance may result in such students having drop out. Various factors influence the level of academic performance of such students. Preventive action would be better to cope with the drop out. This study aims to conduct prediction of students' academic performance at Sebelas Maret University based on three categories of factors namely social, economic, and academic factors. Methods used include, data acquisition stages, data preprocessing, feature selection, classification, and analysis of results. Feature selection uses information gain (IG) and Random Forest (RF) classification algorithms, with 10-fold cross validation. The test results showed an accuracy of 90.7%, such performance outperforms support vector machine (SVM) and Decision Tree (DT) algorithms.

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Data Mining: A prediction for performance improvement using classification

Cited by 12 publications

References 0 publications

A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook

A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Student Performance Prediction in Sebelas Maret University Based on the Random Forest Algorithm

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