From Learning Science to Computer Science: A Scientometric Review of Deeper Learning in Foreign Languages (1993-2024)

Abstract

Deeper learning (DL) is firmly rooted in learning science and computer science. However, a dearth of review studies has probed its trajectory in DL in foreign languages (DLFL). Utilizing SSCI from the Web of Science Core Collection, we employ Citespace and Vosviewer to analyze the scientific knowledge graph of DLFL literature. Our analysis elucidates its geographical spread over time, highlights critical areas for further research, and identifies current trends in its evolution. The results show that DLFL research advances with the United States, China, the United Kingdom, Spain, and Australia ranking in the top five in terms of the number of articles published; the research hotspots focus on factors influencing DLFL, learners' cognitive processes through language acquisition and information technology intervention in DLFL. The field of DLFL pertains to learning science, which is dedicated to enhancing learners' performance, while computer science emphasizes utilizing advanced educational technologies as intervention tools. From learning science to computer science, both fields have followed distinct paths in their respective developments with a trend of integration, and the latter provided the former with a continuous supply of technology-mediated educational tools, including the future uses of computational thinking and ChatGPTs. As for future research directions, the development trajectory of DLFL will focus on natural language processing, cognitive neuroscience, and artificial intelligence. The findings will offer insights for future research on DLFL by enhancing the informational and computational literacy of both instructors and learners, empowering them to navigate and leverage the transformative potential of DLFL.