Mapping the Knowledge Domain of Meta-Analysis Used in SLA Research: A Scientometric Review (2000-2022)

Abstract

The study examined intellectual domains of meta-analysis used in SLA research based on Chinese core journals and English peer-reviewed journals from 2000 to 2022. Two datasets were created via CSSCI and Web of Science (WoS): 1) a Chinese dataset of core Linguistic and educational journals comprising 50 articles, and 2) a WoS dataset of 167 articles published in the reputed journals within registers of Linguistics and educational research. It employed document co-citation analysis (DCA) and author co-citation analysis (ACA) to capture the underlying intellectual structure and characterize distinct clusters in the WoS dataset. Co-word analysis and burst detection were adopted to discern the research trends, frontiers, and hotspots displayed in the two datasets. Results show that the WoS dataset focuses on the research topics: L2 instruction, corrective feedback, computer-mediated communication, self-motivation, and L2 writing with papers about procedures or amendments to meta-analysis as the knowledge base. The author intellectual groups categorized under specific contexts demonstrate a profound influence. Keywords captured in the WoS dataset can be mainly grouped into three types: research subjects, research topics, and terminologies relevant to meta-analysis. In contrast, research topics and terms related to meta-analysis are the dominant lexical chunks in the Chinese dataset. The emerging research spots, individual factors, language achievements, and knowledge maps provide directions for future research.