An Evaluation Model of the Efficiency of Agricultural Information Resources Allocation in the Big Data Environment

The advent of big data era brings new development and opportunities for the construction of library information resources. This paper analyzes the impact of big data on the library at home and abroad, and points out the necessary new reform of construction of library information resources in colleges and universities from the perspective of information resource configuration. Under the big data environment, library should develop the data analysis service of big data information resources, establish the literature resources allocation scheme with circulation data as the assessment indicator and develop towards intelligent library.

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Construction of Multimedia Teaching Resource Management Evaluation Model in the Big Data Environment

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v13i03.8365 ◽

2018 ◽

Vol 13 (03) ◽

pp. 4

Author(s):

Wei Bian

Keyword(s):

Big Data ◽

Resource Management ◽

Evaluation Method ◽

Evaluation Model ◽

Management Evaluation ◽

Teaching Resource ◽

Multimedia Teaching ◽

Proposed Model ◽

Multi Media ◽

Data Environment

In view of the deficiencies in the studies of the multi-media teaching resource management method and resource management evaluation model, a multi-media teaching resource management evaluation model in the big data environment is proposed in this paper based on the existing work. The teaching resource management evaluation method of the proposed model is further studied based on the elaboration of the component elements of the model. Guided by the multimedia teaching resource management evaluation model, the construction method for the multimedia teaching resource management evaluation model is studied in the open big data environment. And the multimedia remote teaching data of 360 encyclopedia and news web pages are used as the basis for experimental verification. The results show that the proposed model and method can effectively evaluate the multimedia teaching resource management.

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University online education file management under the background of big data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219098 ◽

2021 ◽

pp. 1-7

Author(s):

Yun Hong ◽

Yuchan Chen

Keyword(s):

Big Data ◽

Online Education ◽

Evaluation Model ◽

Rapid Development ◽

Reference Value ◽

Colleges And Universities ◽

Information Resources ◽

Network Education ◽

University Archives ◽

The Times

With the rapid development of computer communication technology, the level of archives information services in colleges and universities continues to improve. More and more universities have created archives management halls to promote digital archives services in depth. As an important part of information resources, the development and use of archive information resources have also attracted the attention of all walks of life. University archives are the pioneers in the development of archives in my country, and their computerization level will directly affect the development and utilization of information resources in my country’s archives. This article aims to analyze the management of online education archives in colleges and universities under the background of big data, analyze the management of online education archives in colleges and universities, and explore the management of archives under the background of online education in colleges and universities. Use the university network archives construction evaluation model calculation and investigation research method to study the current situation and mode of university network education archives management, and provide reference value for the rational connection of various tasks of university network education archives management under the background of big data. The experimental results of this article show that 55% of college students believe that the current college archives need to be combined with the requirements of the development of the times, and it is necessary to innovate the archive management methods of colleges and universities to improve the quality of archive management services in universities and ensure the real-time storage of network information and maximize the development of archive information the value of.

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Teaching ability evaluation of ideological and political teachers based on big data fuzzy clustering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189933 ◽

2021 ◽

pp. 1-6

Author(s):

Jinhui Fu

Keyword(s):

Big Data ◽

Fuzzy Clustering ◽

Evaluation Method ◽

Evaluation Model ◽

Quadratic Function ◽

Information Resources ◽

Utilization Rate ◽

Residual Value ◽

Teaching Ability ◽

Double Cluster

To improve the accuracy and utilization of the evaluation of ideological and political teachers’ teaching ability, this study proposes an evaluation method of ideological and political teachers’ teaching ability based on big data fuzzy clustering. According to the detected information resources and the characteristics that the smaller the average square residual value in the clustering is, the higher the similarity of information required by users is, the user needs to solve the minimum mean square residual value problem and information of given double cluster. The problem of information resource detection was transformed, and the missing features of the data were supplemented. The clustering minimum means square residual value problem with missing data was solved by using the idea of quadratic function, to realize the detection of information resources needed by ideological and political teachers in big data. Through the optimization process of the evaluation model of ideological and political teachers’ teaching ability, the method of big data fuzzy clustering and information fusion was used to cluster and fuse the information resources needed by teachers, to realize the evaluation of ideological and political teachers’ teaching ability. Results show that through the evaluation test of ideological and political teachers’ teaching ability, the evaluation accuracy and utilization rate of this method is higher than 95% in the evaluation cycle. The filling principle of multi-dimensional data missing features is better in the detection of information resources needed by users and promotes the improvement of Ideological and political teachers’ teaching ability.

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