Empirical study of college students’ extracurricular reading preference by functional data analysis of the library book borrowing behavior

Fan Zhang, Yuling Liu, Chao Song, Chun Yang and Shaoyong Hong

PLOS ONE, 2024, vol. 19, issue 1, 1-16

Abstract: Library data contains many students’ reading records that reflect their general knowledge acquisition. The purpose of this study is to deeply mine the library book-borrowing data, with concerns on different book catalogues and properties to predict the students’ extracurricular interests. An intelligent computing framework is proposed by the fusion of a neural network architecture and a partial differential equations (PDE) function module. In model designs, the architecture is constructed as an adaptive learning backpropagation neural network (BPNN), with automatic tuning of its hyperparameters. The PDE module is embedded into the network structure to enhance the loss functions of each neural perceptron. For model evaluation, a novel comprehensive index is designed using the calculus of information entropy. Empirical experiments are conducted on a diverse and multimodal time-series dataset of library book borrowing records to demonstrate the effectiveness of the proposed methodology. Results validate that the proposed framework is capable of revealing the students’ extracurricular reading interests by processing related book borrowing records, and expected to be applied to “big data” analysis for a wide range of various libraries.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297357 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 97357&type=printable (application/pdf)

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:plo:pone00:0297357

DOI: 10.1371/journal.pone.0297357

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().