• 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.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.2
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• pp.209-217
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• 2003

Object：This study examined the diagnostic validity of the K-ABC and the K-LDES for identifying the cognitive deficits and the learning difficulty of children with learning disorder and to diagnose the learning disorder. Method：The clinical group consisted of 15 children with learning disorder or attention deficit hyperactivity disorder accompanying learning problem(LP) and 14 children with attention deficit hyperactivity disorder. They were diagnosed either learning disorder or attention deficit hyperactivity disorder based on DSM-IV criteria by child psychiatrists and clinical psychologists visiting Seoul National University Children’s Hospital. The normal group was composed of 15 children be going to an elementary school. All groups were between the age of 7 and 12. The K-ABC was administered to the clinical and the normal group. The K-LDES was also administered to mothers of all groups. Result：There were no significant differences on sequential, simultaneous, mental processing subscales of the K-ABC in three groups. However, The LP group showed slightly lower scores on Achievement scale and significant low scores on Reading/Decoding than the other groups. On K-LDES, LP group showed significant low scores on Listing, Thinking, Reading, Writing, Spelling, Mathematical calculation, Learning quotient(LQ) than the other groups. Also there were significant correlations between K-ABC and K-LDES subscales. Conclusion：The result of present study showed that the K-ABC and the K-LDES are a valid and effective instruments for evaluating and diagnose the learning disorder.