Application of Probabilistic Random Matrix Management Mode in Student Information Management for Higher Education

Yongqiang Sun and Ning Cao

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Traditional management method of student information is not only slow in operation, poor in confidentiality, and low in work efficiency, but also prone to statistical errors and data loss, which can no longer meet the needs of new situations. Probabilistic random matrix management mode can effectively coordinate the development of various businesses and strengthen their information flow through horizontal and vertical management across functional departments. On the basis of summarizing and analyzing previous literature, this study expounded the research status and significance of student information management of higher education, elaborated the development background, current status, and future challenges of probabilistic random matrix management mode, introduced the methods and principles of probabilistic matrix factorization algorithm and random matrix factorization model, discussed the service and supervision functions of student information management, analyzed the incentive and guiding functions of student information management, conducted the process analysis of student information management for higher education based on probabilistic random matrix management mode, established student identity document and student status management modules, designed student scholarship, statistics, and data management modules, constructed a student information management system for higher education based on probabilistic random matrix management mode, and finally carried out a case application and its result analysis. The study results show that the probabilistic random matrix management mode is a combination of linear and flat organizational structures and has the advantages of short information lines, fast information feedback, and high operation efficiency; it can input the matrix form of prior data and use statistical probability knowledge to derive the probability density function of posterior feature vector and predict the recommendation result through feature vector. The probabilistic random matrix management mode first calculates student’s behavior sequence through their management information and then calculates the student’s preference sequence according to their behavior sequence and label information and subsequently calculates the similarity matrixes about the students and their information and finally integrates the obtained student similarity matrix into the probabilistic matrix factorization model.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/9219955.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/9219955.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9219955

DOI: 10.1155/2022/9219955

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().