Scholarlink

This entry examines the evolving landscape of data-driven learning (DDL) in language education, addressing both opportunities and challenges. As DDL expands beyond higher education, issues such as the lack of mobile-ready tools, teacher training, and accessibility to diverse corpora are highlighted. Methodological concerns in research are also discussed. Opportunities arise from the integration of artificial intelligence (AI), multimodal tools, and personalized learning, which can enhance DDL’s impact across diverse learning contexts. The entry emphasizes the need for collaboration between DDL, computer-assisted language learning (CALL) and AI researchers to optimize future language education practices.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Future Challenges and Opportunities for Data-Driven Learning

  • 动态工具书词条

  • 发表日期：

    2025年

查看全文
  • Pascual Pérez-Paredes,
  • Carlos Ordoñana-Guillamón

摘要

This entry examines the evolving landscape of data-driven learning (DDL) in language education, addressing both opportunities and challenges. As DDL expands beyond higher education, issues such as the lack of mobile-ready tools, teacher training, and accessibility to diverse corpora are highlighted. Methodological concerns in research are also discussed. Opportunities arise from the integration of artificial intelligence (AI), multimodal tools, and personalized learning, which can enhance DDL’s impact across diverse learning contexts. The entry emphasizes the need for collaboration between DDL, computer-assisted language learning (CALL) and AI researchers to optimize future language education practices.