• 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 Society of Radiology
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• v.18 no.2
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• pp.101-108
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• 2024

Recently, Metaverse technology has emerged as an important topic in various fields. Metaverse refers to a three-dimensional virtual space in which social and economic activities similar to the real world are possible. Among the 235 third-year students who applied the Metaversity app in the radiology department of this university from September to December 2023, 200 participated in a survey to determine the difference in student response and satisfaction when applying the Metaversity app. analyzed. First, the most satisfactory VOD viewing method was viewing through the Metaversity app, followed by viewing through the LMS. Second, 'I think online videos are appropriate for holiday reinforcement.' showed the highest score at 4.35±0.60, 'I want face-to-face classes and online classes to be held simultaneously.' was 4.25±0.87, and 'I think meta. 'I watched it well through the Metaversity app' was the lowest at 4.10±0.30, and 'VOD viewing through the Metaversity app was used appropriately in class' was the lowest at 3.99±0.75. Also, there was no significant difference in the response to the teaching method (p>0.05). Third, in terms of satisfaction with VOD viewing using the Metaversity app, 'Applying the Metaversity app was interesting and fun' ranked the highest at 4.24±0.88. The score was high, with 'Better improvement is needed to actively utilize the metaversity app' at 4.00±0.45, and 'I hope the metaversity app is implemented in other remote classes' at 3.77±0.88. appear. 'VOD classes through the Metaversity app are better than the existing LMS method.' was found to be 3.44±0.66. Additionally, there was no significant difference in satisfaction with classes according to age and gender (p>0.05). The correlation between response and satisfaction with the metaversity app is 0.601, which can be considered very significant (p>0.001). As a limitation of this study, although we surveyed students' satisfaction with using the Metaversity app, we were unable to investigate the satisfaction of instructors who interact with students. In the future, we did not consider the instructor's satisfaction in classes using the Metaversity app. Research must be conducted, and universities must have institutional support and continued interest until metaversity apps are selected and used to prepare for distance learning.