Mining and Analyzing Patron’s Book-Loan Data and University Data to Understand Library Use Patterns

Journal of College Teaching & Learning (TLC)

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Field Value
 
Title Mining and Analyzing Patron’s Book-Loan Data and University Data to Understand Library Use Patterns
 
Creator Silwattananusarn, Tipawan
Kulkanjanapiban, Pachisa
 
Subject Data Mining; Association Rules; Cluster Analysis; Knowledge Dependency;Data Mining; Association Rules; Cluster Analysis; Knowledge Dependency;Data Analytics;Information Management Data Analytics;Information Management
Data Mining; Association Rules; Cluster Analysis; Knowledge Dependency; Data Analytics;Information Management
 
Description The purpose of this paper is to study the patron’s usage behavior in an academic library. This study investigates on pattern of patron’s books borrowing in Khunying Long Athakravisunthorn Learning Resources Center, Prince of Songkla University that influence patron’s academic achievement during on academic year 2015-2018. The study collected and analyzed data from the libraries, registrar, and human resources. The students’ performance data was obtained from PSU Student Information System and the rest from ALIST library information system. WEKA was used as the data mining tool employing data mining techniques of association rules and clustering. All data sets were mined and analyzed to identify characteristics of the patron’s book borrowing, to discover the association rules of patron’s interest, and to analyze the relationships between academic library use and undergraduate students’ achievement. The results reveal patterns of patron’s book loan behavior, patterns of book usage, patterns of interest rules with respect to patron’s interest in book borrowing, and patterns of relationships between patron’s borrowing and their grade. The ability to clearly identify and describe library patron’s behavior pattern can help library in managing resources and services more effectively. This study provides a sample model as guideline or campus partnerships and for future collaborations that will take advantage of the academic library information and data mining to improve library management and library services.
 
Publisher Regional Information Center for Science & Technology
 
Contributor
 
Date 2020-08-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://ijism.ricest.ac.ir/index.php/ijism/article/view/1803
 
Source International Journal of Information Science and Management (IJISM); Vol 18, No 2 (2020); 151-172
2008-8310
2008-8302
 
Language eng
 
Relation https://ijism.ricest.ac.ir/index.php/ijism/article/view/1803/402
 
Rights Copyright (c) 2020 International Journal of Information Science and Management (IJISM)
 

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