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Technical Writing: Literacy Thresholds

Technical Writing: Literacy Thresholds T. R. Girill Society for Technical Communication/Lawrence Livermore National Lab. Technical Writing: Literacy Thresholds The Problem In "Arguing About a Chemical Change," Patrick Brown admits that "When I first started using writing in science, I expected [my ninth-grade] students to be able to easily explain their ideas and support them with evidence" (Science Teacher, 37(4), Nov-Dec, 2019, p. 20). Instead, he found that many students lack the underlying linguistic skills needed and so they "often have trouble articulating their understanding through writing." Only when Brown "provided [explicit] guidance and broke down argumentative essays into smaller parts" (p. 20) was progress made. The Computing Comparison Similar challenges arise when introducing computer science concepts and techniques in high school (where less than 10% of students now take a separate computer science course). CS insights apply in many situations and classes but will only benefit students "across the curriculum" if those students have crossed certain thresholds of mastery already, just as Brown found with literacy/linguistic skills and science class. In a recent article, Mark Guzdial and colleagues argue that what high-school students need is not so much a separate "computer programming" class as a working knowledge of and respect for "using abstraction and decomposing see the representational possibilities of the computer...needed to make and understand systems" in ANY context (Guzdial, Kay, Norris, and Soloway, "Computational thinking should just be good thinking," Communications of ACM, November 2019, p. 29, doi: 10.1145/3363181). But "important thresholds of understanding must be reached [by students] before they [CS concepts] can be part of one's thinking tools" (p. 30). Underlying Skill Thresholds In the case of effective literacy for science/engineering, including Brown's desired "argumentative essays," the relevant learning thresholds involve: * being able to plan and quickly draft seed text--what engineers call "rapid prototyping" design, * revising that draft text to iteratively improve it in THREE ways (understandability, reader relevance, and findability), probably through several improvement cycles, and * focusing on reader needs (not author preferences) through attentive audience analysis. It is accumulated skill in all of these areas that enables a student to effectively respond to Brown's request to organize and itemize their evidence in a written discussion of one of his chemical-change case studies. Cognitive Apprenticeship How are such science-literacy skills best developed? Again, "computational thinking" offers a revealing comparison. The most reliable path in both cases is not an isolated course but support where students would actually apply such techniques, "across the curriculum"--burdensome for busy science teachers but authentic, the way students will encounter chances to tap these abilities in life beyond school. In both cases, the apprenticeship model offers a reliable path to stronger skills--seeing effective writing (or computational) moves overtly modeled as a science or engineering class unfolds, with explicit feedback on student practice attempts. Slower to build are each student's METAcognitive strengths, their confidence to deploy computational thinking and science/engineering literacy whenever they encounter explanatory or analytical challenges on their own. [Want more background on technical writing in science class? See Want to help students see their writing as another case of engineering design--as "text engineering"? See ]
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