{"title":"Research Topics and Trends in Gifted Education: A Structural Topic Model","authors":"Seda Şakar, Sema Tan","doi":"10.1177/00169862241285041","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47514,"journal":{"name":"Gifted Child Quarterly","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gifted Child Quarterly","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/00169862241285041","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SPECIAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Gifted Child Quarterly (GCQ) is the official journal of the National Association for Gifted Children. As a leading journal in the field, GCQ publishes original scholarly reviews of the literature and quantitative or qualitative research studies. GCQ welcomes manuscripts offering new or creative insights about giftedness and talent development in the context of the school, the home, and the wider society. Manuscripts that explore policy and policy implications are also welcome. Additionally, GCQ reviews selected books relevant to the field, with an emphasis on scholarly texts or text with policy implications, and publishes reviews, essay reviews, and critiques.
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