Social Language and Academic Language
Most communicative tasks used in language classrooms focus on what Cummins (1981, 2000) has called social language, or basic interpersonal communication. Social language typically refers to concrete, observable things, people, and actions, and is typically supported by a considerable amount of contextual information (e.g., visual information about people and places). In social language, a lot of information can be conveyed nonverbally to support and clarify what our verbal communication leaves unclear.
Academic language, sometimes called school language, is different, and may develop independently of social language. Academic language proficiency is the ability to use cognitively demanding language (e.g. abstract nouns and complex syntax) as a tool for critical thinking, and it typically is used in situations where there may be comparatively little contextual support to aid in the sending and receiving of information.
Most communicative tasks used in language classrooms focus on what Cummins (1981, 2000) has called social language, or basic interpersonal communication. Social language typically refers to concrete, observable things, people, and actions, and is typically supported by a considerable amount of contextual information (e.g., visual information about people and places). In social language, a lot of information can be conveyed nonverbally to support and clarify what our verbal communication leaves unclear.
Academic language, sometimes called school language, is different, and may develop independently of social language. Academic language proficiency is the ability to use cognitively demanding language (e.g. abstract nouns and complex syntax) as a tool for critical thinking, and it typically is used in situations where there may be comparatively little contextual support to aid in the sending and receiving of information.
Language Complexity
Cognitive complexity is related to complexity in language, both sentence structure and lexicon. A functional linguistics perspective (Schleppegrell, 2004) can identify language forms that function to express a wide range of logical relationships in academic discourse. One such logical relationship occurs when one states an inference and the facts that support that inference. Typically the inference is framed using an abstract noun, such as ‘wealth’, while the facts may be realized as concrete nouns, such as ‘car’, ‘nice house’, or ‘satellite dish’. Linguistic forms are also needed to link inferences to supporting facts (Lackstrom, 1981). Native speakers of English in Tarone & Swierzbin (2009, p. 87) cited evidence in support of their inferences using phrases like ‘just based on’, ‘from looking at these’, ‘because’.
Academic language typically includes an increased variety in the lexicon, such as the use of rare and/or abstract nouns; very complex noun phrases with multiple levels of embedding; and an increased occurrence of relative, adverbial and complement clauses (Biber 2006). The syntactic and lexical variety that is characteristic of academic language is more cognitively demanding. In addition, the lack of contextual support puts pressure on the speaker to be more precise, to make sure the listener understands. Only at higher levels of proficiency do language learners appear to master the more complex registers and uses of the language.
Measuring Syntactic Complexity
Syntactic complexity can be measured in many ways. Some complexity measures may be more suitable for one purpose than another, or one language than another. Because spoken language is always messier than written language (with sentence fragments, false starts, and overlaps), complexity measures that work for written language often don’t work so well for spoken language. In looking at spoken English L2 learner language, Tarone & Swierzbin (2009) counted the number of sentences containing more than one verb. However, you could also measure syntactic complexity by looking at learners’ use of complex noun phrases, or increased number of verb arguments, or various types of dependent clauses, or something else (Ellis & Barkhuizen, 2005, p.147-156).
Cognitive complexity is related to complexity in language, both sentence structure and lexicon. A functional linguistics perspective (Schleppegrell, 2004) can identify language forms that function to express a wide range of logical relationships in academic discourse. One such logical relationship occurs when one states an inference and the facts that support that inference. Typically the inference is framed using an abstract noun, such as ‘wealth’, while the facts may be realized as concrete nouns, such as ‘car’, ‘nice house’, or ‘satellite dish’. Linguistic forms are also needed to link inferences to supporting facts (Lackstrom, 1981). Native speakers of English in Tarone & Swierzbin (2009, p. 87) cited evidence in support of their inferences using phrases like ‘just based on’, ‘from looking at these’, ‘because’.
Academic language typically includes an increased variety in the lexicon, such as the use of rare and/or abstract nouns; very complex noun phrases with multiple levels of embedding; and an increased occurrence of relative, adverbial and complement clauses (Biber 2006). The syntactic and lexical variety that is characteristic of academic language is more cognitively demanding. In addition, the lack of contextual support puts pressure on the speaker to be more precise, to make sure the listener understands. Only at higher levels of proficiency do language learners appear to master the more complex registers and uses of the language.
Measuring Syntactic Complexity
Syntactic complexity can be measured in many ways. Some complexity measures may be more suitable for one purpose than another, or one language than another. Because spoken language is always messier than written language (with sentence fragments, false starts, and overlaps), complexity measures that work for written language often don’t work so well for spoken language. In looking at spoken English L2 learner language, Tarone & Swierzbin (2009) counted the number of sentences containing more than one verb. However, you could also measure syntactic complexity by looking at learners’ use of complex noun phrases, or increased number of verb arguments, or various types of dependent clauses, or something else (Ellis & Barkhuizen, 2005, p.147-156).
In this section, we show how to use very simple measures of syntactic complexity that can enable teachers to estimate growth or change in complexity in spoken learner language. Professional researchers would probably use more sophisticated and precise measures of complexity in published research papers, but for our pedagogical purposes we can be content with estmates rather than precision.
Measuring Lexical Complexity
One useful way to measure complexity in vocabulary is to document variety in the words occurring in a segment of language. A type-token ratio (TTR) is the total number of different words (types) divided by the total number of words (tokens) in a given segment. For example, that last sentence contains 24 different words (tokens), but several of those words (like ‘a’, ’the’, ‘words’) occur more than once, so there are only 18 types. The TTR of that sentence is 18/24. The closer the TTR ratio is to 1, the greater the lexical richness of the segment. Usually written language has a higher TTR than spoken language.
Another interesting feature differentiating social and academic vocabulary is learners’ use of concrete vs. abstract nouns, as noted above. Increased use of abstract nouns might be one signal of increased cognitive complexity, since abstract nouns refer to concepts and categories in performing such higher level cognitive operations in Bloom’s revised taxonomy as analysis and evaluation (Anderson & Krathwohl, 2001).
http://www.carla.umn.edu/speechacts/index.html
One useful way to measure complexity in vocabulary is to document variety in the words occurring in a segment of language. A type-token ratio (TTR) is the total number of different words (types) divided by the total number of words (tokens) in a given segment. For example, that last sentence contains 24 different words (tokens), but several of those words (like ‘a’, ’the’, ‘words’) occur more than once, so there are only 18 types. The TTR of that sentence is 18/24. The closer the TTR ratio is to 1, the greater the lexical richness of the segment. Usually written language has a higher TTR than spoken language.
Another interesting feature differentiating social and academic vocabulary is learners’ use of concrete vs. abstract nouns, as noted above. Increased use of abstract nouns might be one signal of increased cognitive complexity, since abstract nouns refer to concepts and categories in performing such higher level cognitive operations in Bloom’s revised taxonomy as analysis and evaluation (Anderson & Krathwohl, 2001).
http://www.carla.umn.edu/speechacts/index.html
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