• KSLA Bulletin
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• s.2
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• pp.16-27
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• 2010

Recently, the valuation of research papers has been greatly emphasized, and their citation has been accepted as a very useful indicator. In this study, we performed correlation analyses between the paper citation counts and 11 explanatory variables of morphological and conceptual factors with a test dataset of the papers of 11 journals in library and information science. The analysis results of the correlations show that only the document similarity has 5% or more standardized variances(r2) with paper citation counts and the document similarity with citation counts get higher as the variable value increases.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.2
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• pp.357-379
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• 2010

The citation analysis is not applied to group measurements but to individual papers finding their impact factors among citation chains which are created by tracing citing-cited relationship between any two papers in data set. The individual impact factor is measured by adding each value derived from citation chain. Each paper's impact factor index is calculated by adding the values of each index by direct citing-cited relationship, and the values of each impact factor made by indirectly influencing to the papers in the citation chain. The research introduces a grace period, in which the system holds the papers not cited by other papers yet, but are expected to be cited within this period. Eventually not cited papers after the grace period would be eliminated by the system. The experiment suggests a reasonable database in which the highly influenced papers are gathered, and could be serviced in stead of buying databases filled with worthless not-cited-papers.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.