An Approach of Improving Students Academic Performance by using k means clustering algorithm and Decision tree

Shovon, Md. Hedayetul Islam; Haque, Mahfuza

doi:10.48550/arxiv.1211.6340

Cited by 5 publications

(8 citation statements)

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“…Several of these studies have utilized data mining techniques, particularly classification algorithms, to enhance the quality of higher education systems and predict student performance. In this section, a number of the related studies, especially those focusing on the application of the and classification in estimating the academic performance of the students, are presented [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31]. For instance, Qasem, Emad, and Mustafa [32] employed the CRISP framework to evaluate students' data in C++ courses, comparing classifiers such as , and Naive Bayes .…”

Section: Related Work Reviewmentioning

confidence: 99%

“…Dorina Kabakchieva [40] compared algorithms to predict student performance, classifying students as strong or weak, with the neural network achieving high accuracy for the strong class. Shovon and Mahfuza [41] proposed a hybrid approach combining clustering and classification to categorize students into high, medium, and low standards and make informed decisions about their academic performance, ultimately enhancing their final examination results. These studies collectively demonstrate the versatility of and classification algorithms in addressing various facets of student performance and academic success, aiding both educators and educational institutions in improving their educational processes and outcomes.…”

Section: Related Work Reviewmentioning

confidence: 99%

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Efficiency of Hybrid Decision Tree Algorithms in Evaluating the Academic Performance of Students

Xie

2024

IJACSA

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Educational institutions are anticipated to take substantial and proactive roles in guaranteeing students' successful program completion. Academic performance is conventionally employed to categorize and forecast students' future ability to confront post-graduation challenges. A student's academic accomplishments are instrumental in shaping exceptional individuals who may become future leaders. Using algorithms to assess and predict academic performance is a wellestablished practice in machine learning, encompassing techniques such as neural networks , logistic regression , decision trees , and others. The goal of this project is to improve decision trees' ability to predict students' academic achievement via the use of data mining methods and metaheuristic algorithms. Educational data mining involves the utilization of data analysis methodologies and tools to examine the extensive data generated within educational establishments as a result of students' interactions and activities throughout their academic journey. Pelican Optimization Algorithm (POA) and Runge Kutta optimization (RKO) are utilized algorithms in developing hybrid models, both of which can efficiently search for optimal or near-optimal splits by fine-tuning the hyperparameters of decision tree models. Students' final grades were predicted through training and testing models and categorized into four classes: Excellent, Good, Acceptable, and Poor. The classification capability of a single model and optimized counterparts was evaluated using Accuracy, Recall, Precision, and F1-score in separate phases for each category. Obtained results for all models revealed that POA and RKO developed Accuracy of DTC by 1.86% and 0.87%. Also, Precision and Recall metric analysis further manifest the superiority of DTPO. Prediction based on classifiers, especially workable optimized versions such as DTPO, paves the way for institutions to raise student success rates.

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Section: Related Work Reviewmentioning

confidence: 99%

Section: Related Work Reviewmentioning

confidence: 99%

Efficiency of Hybrid Decision Tree Algorithms in Evaluating the Academic Performance of Students

Xie

2024

IJACSA

View full text Add to dashboard Cite

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“…Their pivotal role extends to the identification of group characteristics, exploration of relationships between variables, and application in predicting various educational outcomes, including student performance. Jorda and Raqueno [11] underscore the significance of diverse decision tree algorithms such as C&R Tree, CHAID, C 5.0, and QUEST, emphasizing their role in the development of classification systems [12], [13], [14].…”

Section: A Backgroundmentioning

confidence: 99%

Elevating Student Performance Prediction using Extra-Trees Classifier and Meta-Heuristic Optimization Algorithms

Li,

2024

IJACSA

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In the highly competitive landscape of academia, the study addresses the multifaceted challenge of analyzing voluminous and diverse educational datasets through the application of machine learning, specifically emphasizing dimensionality reduction techniques. This sophisticated approach facilitates educators in making data-informed decisions, providing timely guidance for targeted academic improvement, and enhancing the overall educational experience by stratifying individuals based on their innate aptitudes and mitigating failure rates. To fortify predictive capabilities, the study employs the robust Extra-Trees Classifier (ETC) model for classification tasks. This model is enhanced by integrating the Gorilla Troops Optimizer (GTO) and Reptile Search Algorithm (RSA), cuttingedge optimization algorithms designed to refine decision-making processes and improve predictive precision. This strategic amalgamation underscores the research's commitment to leveraging advanced machine learning and bio-inspired algorithms to achieve more accurate and resilient student performance predictions in the mathematics course, ultimately aiming to elevate educational outcomes. Analyses of G1 and G3 showcase the efficacy of the ETRS model, demonstrating 97.5% Accuracy, F1-Score, and Recall in predicting the G1 values.Similarly, the ETRS model emerges as the premier predictor for G3, attaining 95.3% Accuracy, Recall, and F1-Score, respectively. These outcomes underscore the significant contributions of the proposed models in advancing precision and discernment in student performance prediction, aligning with the overarching goal of refining educational outcomes.

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“…It is also exploited to group common learning profiles of different students so that a learning policy could be established and applied to particular groups for further improvement [52]. Clustering in learning management systems and e-portals can also help form groups of successful or struggling students when there is no labeling provided by the portals or management systems [53].…”

Section: Association Rules and Clustering In Academic Analyticsmentioning

confidence: 99%

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

et al. 2022

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Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

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An Approach of Improving Students Academic Performance by using k means clustering algorithm and Decision tree

Cited by 5 publications

References 0 publications

Efficiency of Hybrid Decision Tree Algorithms in Evaluating the Academic Performance of Students

Efficiency of Hybrid Decision Tree Algorithms in Evaluating the Academic Performance of Students

Elevating Student Performance Prediction using Extra-Trees Classifier and Meta-Heuristic Optimization Algorithms

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

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