Research on Optimized Allocation Model of Educational Resources in Colleges and Universities Based on Big Data DOI Open Access

Yiran Xu

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract The characteristics of educational information, such as quantization, decentralization, redundancy and unstructuredness, bring complex multi-level problems to the construction high-quality resources in colleges universities. study constructs a model for optimal allocation universities using collaborative filtering algorithm. Taking big data background, existing ratio is analyzed, current demand predicted through cosine similarity algorithm Pearson algorithm, recommended resource items are derived recommended. takes university district city Z an example uses this paper optimize teaching auxiliary room area, sports hall number books, computers, value instruments equipment. After universities, pass rate students’ exams increased more than 70%, students were satisfied with subjective evaluation model. This paper’s plays significant role resources, can effectively utilize reference it.

Язык: Английский

Higher Education 4.0 for Technological Literacy and Inclusion: Exploring Key 4.0 Technologies in the MIT Technology Review DOI
Beatríz Plaza, Ibon Aranburu, Maria Inês Pinho

и другие.

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 213 - 222

Опубликована: Дек. 16, 2024

Язык: Английский

Процитировано

0

Research on Optimized Allocation Model of Educational Resources in Colleges and Universities Based on Big Data DOI Open Access

Yiran Xu

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract The characteristics of educational information, such as quantization, decentralization, redundancy and unstructuredness, bring complex multi-level problems to the construction high-quality resources in colleges universities. study constructs a model for optimal allocation universities using collaborative filtering algorithm. Taking big data background, existing ratio is analyzed, current demand predicted through cosine similarity algorithm Pearson algorithm, recommended resource items are derived recommended. takes university district city Z an example uses this paper optimize teaching auxiliary room area, sports hall number books, computers, value instruments equipment. After universities, pass rate students’ exams increased more than 70%, students were satisfied with subjective evaluation model. This paper’s plays significant role resources, can effectively utilize reference it.

Язык: Английский

Процитировано

0