Lesson plan
Data-driven learning
Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven learning treats language as data and students as researchers undertaking guided discovery tasks. Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW pyramid). It is informed by a pattern-based approach to grammar and vocabulary, and a lexicogrammatical approach to language in general. Thus the basic task in DDL is to identify patterns at all levels of language. From their findings, foreign language students can see how an aspect of language is typically used, which in turn informs how they can use it in their own speaking and writing. Learning how to frame language questions and use the resources to obtain data and interpret it is fundamental to learner autonomy. When students arrive at their own conclusions through such procedures, they use their higher order thinking skills (see Bloom's taxonomy) and are creating knowledge (see Vygotsky). In DDL, students use the same types of tools that professional linguists use, namely a corpus of texts that have been sampled and stored electronically, and a concordancer, which is a search engine designed for linguistic analysis. Some tools have been specifically created for data-driven learning, such as SkELL, WriteBetter, and Micro-concord.…
Article summary
Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven learning treats language as data and students as researchers undertaking guided discovery tasks. Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW pyramid). It is informed by a pattern-based approach to grammar and vocabulary, and a lexicogrammatical approach to language in general. Thus the basic task in DDL is to identify patterns at all levels of language. From their findings, foreign language students can see how an aspect of language is typically used, which in turn informs how they can use it in their own speaking and writing. Learning how to frame language questions and use the resources to obtain data and interpret it is fundamental to learner autonomy. When students arrive at their own conclusions through such procedures, they use their higher order thinking skills (see Bloom's taxonomy) and are creating knowledge (see Vygotsky). In DDL, students use the same types of tools that professional linguists use, namely a corpus of texts that have been sampled and stored electronically, and a concordancer, which is a search engine designed for linguistic analysis. Some tools have been specifically created for data-driven learning, such as SkELL, WriteBetter, and Micro-concord.…
Key claims
- publication date: 1993 — source: Wikipedia · confidence 0.95
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- The record states that the publication date is "1993". What evidence would count against this claim?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
- Wikipedia https://en.wikipedia.org/w/index.php?oldid=1223178041
Key claims and bibliography are generated from the same tracked claims and sources as the article page.