Analysis of student performance using Machine learning Algorithms

Kour, Sunpreet; Kumar, Rakesh; Gupta, Meenu

doi:10.1109/icirca51532.2021.9544935

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…The benefits of leveraging machine learning and AI for predicting student performance are multifaceted [13,14]. Beyond providing early intervention opportunities, these technologies enable educational institutions to allocate resources more efficiently, ensuring that support services are directed towards areas with the greatest need [15]. Moreover, the predictive analytics generated by ML algorithms offer valuable insights into the factors influencing student success, allowing for the development of targeted strategies to enhance overall educational outcomes [16].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach

Maulana,

Idroes,

Kemala

et al. 2023

J. Educ. Manag. Learn.

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This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.

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Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach

Maulana,

Idroes,

Kemala

et al. 2023

J. Educ. Manag. Learn.

View full text Add to dashboard Cite

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The evolution of frailty assessment using inertial measurement sensor‐based gait parameter measurements: A detailed analysis

Amjad,

Qaiser,

Błaszczyszyn

et al. 2024

WIREs Data Min & Knowl

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Frailty is a significant issue in geriatric health, may cause adverse effects such as falls, delirium, weight loss, or physical decline. Over time, various methods have been developed for measuring frailty, including clinical judgment, the frailty index, the clinical frailty scale, and the comprehensive geriatric assessment. These traditional frailty assessment approaches rely on healthcare professionals, which can lead to inaccuracy and require frequent clinic visits, making it burdensome for elderly patients. This review paper explores the latest trends in frailty assessment by measuring gait parameters using wearable sensors, specifically the inertial measurement unit (IMU). The aim of this study is to provide a comprehensive overview of objective methods for evaluating and quantifying frailty. We focus on the application of machine learning (ML) and deep learning (DL) techniques to IMU gait data, highlighting key aspects of recent publications such as algorithms, sensor types, sample sizes, and performance evaluations. By examining the strengths and challenges of each technique, this review aims to guide future studies on utilizing cost‐effective and portable devices integrated with clinical data. This integration can help to propose optimized IMU gait parameters or ML models to detect early‐stage frailty. This advances the emerging trend of intelligent, individualized, and efficient healthcare systems for older adults.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

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Analysis of Machine learning algorithms for predicting a student's grade

Palkhiwala

Shah

2022

J. of Data, Inf. and Manag.

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Analysis of student performance using Machine learning Algorithms

Cited by 10 publications

References 12 publications

Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach

Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach

The evolution of frailty assessment using inertial measurement sensor‐based gait parameter measurements: A detailed analysis

Analysis of Machine learning algorithms for predicting a student's grade

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