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

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Publicat a:Sage Open vol. 15, no. 1 (Jan 2025)
Autor principal: Zhao, Wanli
Altres autors: Tang Youjun, Ma, Xiaomei
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SAGE PUBLICATIONS, INC.
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Accés en línia:Citation/Abstract
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022 |a 2158-2440 
024 7 |a 10.1177/21582440251322564  |2 doi 
035 |a 3185526440 
045 2 |b d20250101  |b d20250131 
100 1 |a Zhao, Wanli  |u Xi’an Jiaotong University, Shaanxi, China; Xianyang Normal University, Shaanxi, China 
245 1 |a From Learning Science to Computer Science: A Scientometric Review of Deeper Learning in Foreign Languages (1993–2024) 
260 |b SAGE PUBLICATIONS, INC.  |c Jan 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Educational technology 
653 |a Second language learning 
653 |a Human-computer interaction 
653 |a Literacy 
653 |a Scientometrics 
653 |a Computer science 
653 |a Foreign language learning 
653 |a Education 
653 |a Cognition 
653 |a Natural language processing 
653 |a Artificial intelligence 
653 |a Intervention 
653 |a Research 
653 |a Future 
653 |a Computers 
653 |a Learning 
653 |a Scientific knowledge 
653 |a Foreign languages 
653 |a Languages 
653 |a Language acquisition 
653 |a Information technology 
653 |a Academic achievement 
653 |a Neurosciences 
653 |a Science and technology 
653 |a Science Instruction 
653 |a Influence of Technology 
653 |a Cognitive Processes 
653 |a Language Processing 
700 1 |a Tang Youjun  |u Xi’an Jiaotong University, Shaanxi, China; Qingdao Binhai University, Shandong, China 
700 1 |a Ma, Xiaomei  |u Xi’an Jiaotong University, Shaanxi, China 
773 0 |t Sage Open  |g vol. 15, no. 1 (Jan 2025) 
786 0 |d ProQuest  |t Social Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3185526440/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u https://journals.sagepub.com/doi/10.1177/21582440251322564