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Evaluation of Student?s Performance Using Decision Tree Model
Subject area: Science,Engineering and Technology · Area of research: Education
Abstract
Predicting and evaluating student academic performance is a crucial job for educational institutions since it enables teachers to modify their methods and help problematic pupils on time. In recent years, deep learning models have demonstrated impressive skills in a range of disciplines, including education. This study presents an in-depth analysis and assessment of the application of machine learning techniques for predicting and evaluating student academic achievement. The study starts with a discussion of the significance of accurate performance evaluation and its influence on students' learning experiences. Traditional evaluation systems usually fail to capture the intricacies of individual learning processes and appropriately predict future performance. Machine learning algorithms, which use complex patterns and correlations in educational data, provide a viable alternative. Machine learning models are a broad class of computing methods that play an important role in artificial intelligence. These models employ a variety of methodologies, ranging from supervised learning, in which they learn to map inputs to labelled outputs, to unsupervised learning, in which hidden patterns in unlabeled data are discovered. Semi-supervised learning bridges the gap by using both labelled and unlabeled data. Furthermore, reinforcement learning models teach agents how to make decisions and conduct actions by interacting with their surroundings, which is a basic notion in AI research.
Keywords
Student Assessment, Neural Networks, Classification Models, Regression Models, Predictive Modeling.
How to cite this paper
@article{1705455,
author = {Kunika Jangid, Dr. Santosh Singh, Sherilyn Kevin, Abhishek Singh},
title = {Evaluation of Student?s Performance Using Decision Tree Model},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {8},
pages = {34-40},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705455.pdf},
abstract = {Predicting and evaluating student academic performance is a crucial job for educational institutions since it enables teachers to modify their methods and help problematic pupils on time. In recent years, deep learning models have demonstrated impressive skills in a range of disciplines, including education. This study presents an in-depth analysis and assessment of the application of machine learning techniques for predicting and evaluating student academic achievement. The study starts with a discussion of the significance of accurate performance evaluation and its influence on students' learning experiences. Traditional evaluation systems usually fail to capture the intricacies of individual learning processes and appropriately predict future performance. Machine learning algorithms, which use complex patterns and correlations in educational data, provide a viable alternative. Machine learning models are a broad class of computing methods that play an important role in artificial intelligence. These models employ a variety of methodologies, ranging from supervised learning, in which they learn to map inputs to labelled outputs, to unsupervised learning, in which hidden patterns in unlabeled data are discovered. Semi-supervised learning bridges the gap by using both labelled and unlabeled data. Furthermore, reinforcement learning models teach agents how to make decisions and conduct actions by interacting with their surroundings, which is a basic notion in AI research. },
keywords = {Student Assessment, Neural Networks, Classification Models, Regression Models, Predictive Modeling.},
month = {February},
}