Many articles have been published in gifted education in recent years. This study aims to provide a comprehensive review of the evolution of academic studies in gifted education. In this context, the structural topic modeling (STM) method was used to analyze the topics and trends in the field. STM is a machine learning technique that utilizes natural language processing techniques based on text mining. It is a valuable methodology for identifying a text corpus’s main topics and trends. The corpus used in this study is 5,127 articles from nine leading journals in giftedness without any year limitations. As a result of the analysis, five topics that prominently emerged in the literature were discovered. These are curriculum and instruction, social-emotional characteristics, thinking skills, identification and assessment tools, and equity and policies. The research topics and trends discovered due to the analysis are discussed within the literature framework, and recommendations are presented.

中文翻译：

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资优教育的研究主题和趋势：结构性主题模型


近年在资优教育上发表了不少文章。本研究旨在全面回顾资优教育学术研究的演变。在此背景下，使用结构主题建模 （STM） 方法来分析该领域的主题和趋势。STM 是一种机器学习技术，它利用基于文本挖掘的自然语言处理技术。它是识别文本语料库主要主题和趋势的宝贵方法。本研究中使用的语料库是来自 9 个主要 Giftness 期刊的 5,127 篇文章，没有任何年份限制。作为分析的结果，发现了文献中突出出现的五个主题。这些是课程和教学、社会情感特征、思维技能、识别和评估工具以及公平和政策。在文献框架内讨论了由于分析而发现的研究主题和趋势，并提出了建议。