The Design of Mathematics Teaching Optimization Scheme Based on Data-Driven Decision-Making Technology

Lv, Zongming

doi:10.1155/2021/5377784

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…At the same time, we have also rapidly entered the era of big data with the rapid development of the Internet. The big data has had a significant impact on finance, business, medical care, and other fields [ 1 , 2 ]. Accordingly, how to reasonably design or carry out Chinese teaching under the platform of big data is an important problem that restricts the development of the teaching level of the universities in China.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Shi

2022

Journal of Environmental and Public Health

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The pivotal and most important technology point of the application of big data in modern network teaching is the establishment of teaching platform. Based on Hadoop big data technology, this paper establishes a big data platform designed by Chinese teaching institutions in colleges and universities. The teaching system not only realizes the diversified development of network teaching but also realizes the discovery of teaching platform users’ needs and the push of resources. In addition, it is found that a small number of people do not have a high understanding of the teaching mode of big data through extensive studies, which also shows that traditional teaching is still lagging behind in big data education. However, on the whole, the Chinese teaching model in universities based on the big data environment deserves further promotion and application in universities.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Shi

2022

Journal of Environmental and Public Health

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“…The implementation of learning strategies that are responsive to the individual needs of students, improvement of learning facilities, and increased student engagement are concrete steps that can be taken based on insights from the results of this data analysis. Thus, Data-Driven Education not only facilitates evaluation, but also guides more effective actions and policies in achieving better educational goals (Lv 2021).…”

Section: Student Engagement Data On Class Discussionmentioning

confidence: 99%

Data-Driven Education: Data Processing as a Key to Improving the Quality of Mathematics Education

Hartati,

Fitria,

Harahap

et al. 2023

ALSYSTECH. J. of. Educ. Technol.

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Education is a major pillar in the development of a nation, and the quality of education becomes a key factor in determining the progress of a society. In this digital era, there is a rapid development in the use of information technology, and this has a significant impact on the education sector. The purpose of this study is for data-driven education: data processing as the key to improving the quality of mathematics education. This research uses a mixed method that combines qualitative and quantitative methods. This approach was chosen to address the complexity of data management and the comprehensiveness of this topic. The research begins with an in-depth literature analysis to understand the potential of Data-Driven Education. By using data as a guide, educators can make smarter decisions, detail instructional strategies, and create an educational environment that allows each student to reach his or her full potential. This is done by combining the results of various data of educator learners who can make informational decisions to improve the quality of mathematics learning. The application of data management in the context of mathematics education can provide concrete solutions to overcome challenges and improve the quality of learning. The right use of data provides the basis for smarter decisions and more adaptive learning, creating a learning environment that fits the needs of each student. By integrating Data-Driven Education effectively, we can move towards a more inclusive, personalized and successful mathematics education. The use of data contributes significantly in improving the quality and inclusivity of mathematics education. The integration of data management in mathematics education is a critical step towards a more adaptive, inclusive, and effective education

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“…AI and ML are poised to play significant roles in 6G-network management [27]. Emerging studies focus on applications like dynamic resource allocation and predictive maintenance [28], with challenges including data privacy and real-time processing [29]. Efforts in multi-objective optimization [30] and security [31], alongside standardization initiatives [32], highlight the growing importance of data-driven strategies in network slicing.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

Changala

et al. 2024

Int. Res. J. multidiscip. Technovation

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This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

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The Design of Mathematics Teaching Optimization Scheme Based on Data-Driven Decision-Making Technology

Cited by 5 publications

References 18 publications

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Data-Driven Education: Data Processing as a Key to Improving the Quality of Mathematics Education

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

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