Towards an open university based on machine learning for the teaching service support system using backpropagation neural networks

The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a common goal of improving education drive this transformative journey. In a time when resources and information are more readily available t...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 28; H. 5; S. 4531 - 4549
1. Verfasser:	Wang, Jianjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024 Springer Nature B.V
Schlagworte:	Adaptive algorithms Application of Soft Computing Artificial Intelligence Back propagation networks Colleges & universities Computational Intelligence Control Data analysis Data mining Distance learning Education Effectiveness Engineering Genetic algorithms Information technology Machine learning Mathematical Logic and Foundations Mechatronics Neural networks Online instruction Resource management Robotics Students Support services Support systems Teachers Teaching Teaching machines Teaching support service system Backpropagation neural network Development university network Adaptive genetic algorithm Machine learning
ISSN:	1432-7643, 1433-7479
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Abstract	The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a common goal of improving education drive this transformative journey. In a time when resources and information are more readily available than ever, traditional teaching strategies are changing to include digital tools that meet the various needs of students. For a teaching services support system (TSS), this paper proposed a novel machine learning-based model that utilizes the powerful backpropagation (BP) neural network, well known for its machine learning and data analysis capabilities. Increasing the effectiveness of online learning is the primary objective of the TSS, which focuses on contributing to a complete learning environment and promoting self-directed learning. This work closely examines the construction and functionality of the BP neural network within the TSS, contribution visions into input–output mechanisms, activation functions, and weight coefficients. This novel approach can bring concerning a digital age educational revolution by increasing the effectiveness and caliber of online learning, rekindling students’ enthusiasm for learning, and making the best use of teaching resources. In addition, the research explores the field of data-mining-based online teaching support services in Open Universities. It clarifies the system’s architecture, which includes virtual teaching modules, resource management, and user authentication. Machine learning methods such as adaptive genetic algorithms and BP neural networks optimize the system’s architecture. Experimental results achieved remarkable BP neural network accuracy and stability, significantly enhancing instructional quality and student engagement. The results show an impressive 83.4% accuracy, outperforming traditional methods such as SVM, KNN, DT, RF, and XGBoost. This demonstrates how learning outcomes and productivity can be enhanced by incorporating machine learning into education.
AbstractList	The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a common goal of improving education drive this transformative journey. In a time when resources and information are more readily available than ever, traditional teaching strategies are changing to include digital tools that meet the various needs of students. For a teaching services support system (TSS), this paper proposed a novel machine learning-based model that utilizes the powerful backpropagation (BP) neural network, well known for its machine learning and data analysis capabilities. Increasing the effectiveness of online learning is the primary objective of the TSS, which focuses on contributing to a complete learning environment and promoting self-directed learning. This work closely examines the construction and functionality of the BP neural network within the TSS, contribution visions into input–output mechanisms, activation functions, and weight coefficients. This novel approach can bring concerning a digital age educational revolution by increasing the effectiveness and caliber of online learning, rekindling students’ enthusiasm for learning, and making the best use of teaching resources. In addition, the research explores the field of data-mining-based online teaching support services in Open Universities. It clarifies the system’s architecture, which includes virtual teaching modules, resource management, and user authentication. Machine learning methods such as adaptive genetic algorithms and BP neural networks optimize the system’s architecture. Experimental results achieved remarkable BP neural network accuracy and stability, significantly enhancing instructional quality and student engagement. The results show an impressive 83.4% accuracy, outperforming traditional methods such as SVM, KNN, DT, RF, and XGBoost. This demonstrates how learning outcomes and productivity can be enhanced by incorporating machine learning into education.
Author	Wang, Jianjun
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Snippet	The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a...
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SubjectTerms	Adaptive algorithms Application of Soft Computing Artificial Intelligence Back propagation networks Colleges & universities Computational Intelligence Control Data analysis Data mining Distance learning Education Effectiveness Engineering Genetic algorithms Information technology Machine learning Mathematical Logic and Foundations Mechatronics Neural networks Online instruction Resource management Robotics Students Support services Support systems Teachers Teaching Teaching machines
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Title	Towards an open university based on machine learning for the teaching service support system using backpropagation neural networks
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