A critical review of SkELL (Sketch Engine for Language Learning) – V1.11.8

Abstract

In an era of technology transforming education, data-driven learning (DDL) has gained recognition as a powerful approach in language education. One way to harness technology in language education is through online resources, one example of which is SkELL (Sketch Engine for Language Learning) (Baisa & Suchomel, 2014). Sketch Engine’s simplified language learning interface offers learners authentic usage of words and phrases by tapping into its mother corpus query and management system’s large corpora. Designed as a user-friendly alternative to traditional corpus tools, SkELL bridges the gap between complex linguistic analysis and practical language learning. Embracing Topal’s (2022) framework, this media review critically evaluates SkELL by addressing its strengths and weaknesses as a language learning resource. Findings reveal that the platform provides authentic usage examples, vocabulary gains, collaborative DDL tasks, and writing improvement. Nonetheless, it suffers from several drawbacks, including the requirement for advanced language use, a lack of differentiation between homographs and polysemous words, and inaccurate/misleading input. Consequently, more research is urged to verify the review’s findings.

Keywords

• corpus
• data-driven learning
• language learning
• media review
• SkELL