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Responding to scientific writing using the five-filters approach

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Viewing written work using the five filters of accuracy, brevity, clarity, objectivity and formality provides a pragmatic way to respond to academic and scientific writing. The five-filters approach makes it easy for novice writers to understand both what needs to be done and how they can do so. The key to responding to writing effectively is to provide writers with actionable easy-to-understand feedback.

Teachers of academic and scientific writing may be faced with multiple submissions by many students over a semester. Teachers of writing need to maintain a work-life balance, and so there is invariably a trade-off between quantity and quality of responses. The expectations of writers vary; some valuing a quick turnaround with pithy comments while others prefer to wait longer for more detailed comments.

In order to write appropriately, it is necessary to be able to adhere to the expected conventions of the target genre in terms of content, form and format. A template analysis (King, 2004) of the pedagogic literature on scientific writing was conducted which uncovered three major criteria: accuracy, brevity and clarity; and two minor criteria: objectivity and formality.

Practical applications in which writers, peers and teachers can harness the five filters will be demonstrated. No-tech, low-tech and high-tech ways to harness the five-filters approach will be shared. The high-tech method uses a genre-specific online automated error detector tailored for writers of short articles in computer science. The errors for this detector were extracted from a learner corpus of all computer science theses (n = 629) submitted from 2014 in the University of Aizu, Japan. Where possible, regular expressions and easy-to-understand actionable advice were created for each error. Scripts were written that automatically colour, highlight, and display advice on the matched strings. A web interface was created to enable text submission around the clock.

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Responding to scientific writing using the five-filters approach

  1. 1. John Blake Center for Language Research, University of Aizu Responding to scientific writing using the five-filters approach jb11.org
  2. 2. Overview 02 1. Background: responding to scientific writing 2. Pedagogic literature: survey and analysis 3. Five filters: description 4. Practical applications: A. No-tech: paper-based B. Low-tech: word processor-based C. High-tech web-based
  3. 3. 1. Background: Teacher or student? 03 Students Write thesis (i.e. short research article) or don`t graduate • Reluctant to ask for help • do not read feedback (e.g. thesis morphed) • misinterpret feedback • cannot understand feedback. (e.g. illegible, too pithy) Teachers Busy or lazy • Time eater (one-many ratio) • Predictable surface-level mistakes • Expectations of “correction” • Intended vs. perceived message • Lack of empirical evidence
  4. 4. 1: Background: How to respond Some of the choices • product-focus vs. process-focus • positive aspects vs. negative aspects Sugaring the pill (Hyland & Hyland, 2001). • grammar vs. style - Teachers tend to focus on errors (Lee, 2008). • all vs. some • first instance vs. each instance • teacher vs. peer vs. self • text vs. audio vs. video • code vs phrases vs. full sentences • discontinuous vs. continuous • stand-alone vs. consultation • group vs. individual • open (all students) vs. closed (only one student) • direct vs. indirect • automated vs. manual • same-day vs. next-day vs. delayed • actionable vs. not actionable • Immediately better vs. long-term focused 04 Hyland, F. & Hyland, K. (2001). Sugaring the pill: Praise and criticism in written feedback. Journal of Second Language Writing 10, 185-212. Lee, I. (2008). Understanding teachers` written feedback practices in Hong Kong secondary classrooms. Journal of Second Language Writing, 17 (2), 69-85.
  5. 5. 1. Background: Errors Brown , H. (2000). Principles of Language Learning and Teaching. New Jersey: Prentice Hall. Edge, J. (1990). Mistakes and correction. Harlow: Longman. Orr, T., & Yamazaki, A. K. (2004). Twenty problems frequently found in English research papers authored by Japanese researchers. In Professional Communication Conference Proceedings International (pp. 23-35). Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10(3), 209-231 Source • Intralingual vs. interlingual errors (Selinker, 1972; Brown, 2000) • Accidental slips, ingrained errors vs. attempts (Edge, 1990) • Learner-induced vs. teacher-induced Form and frequency • Lexical, grammatical vs. discoursal • Grammatical category (Orr & Yamazaki, 2004) Effect • Intrusive vs. non-intrusive errors • Errors that lead to rejection/failure vs. other errors  My focus 05
  6. 6. 1: Background: Reasons for rejection Bordage, 2001; McKercher et al, 2007; Pierson, 2004; Thrower, 2012 and others report the main reasons as: • Unoriginal • Unimportant • Flawed (method, analysis, etc.) • Poor language  My focus Bordage G. (2001). Reasons reviewers reject and accept manuscripts: the strengths and weaknesses in medical education reports. Acad Med, 76(9), 889–896 McKercher B, Law R, Weber K, Song H, Hsu C (2007). Why referees reject manuscripts. Journal of Hospitality & Tourism Research, 31(4): 455-470 Pierson D.J. (2004). The top 10 reasons why manuscripts are not accepted for publication. Respiratory Care, 49(10): 1246-52. Thrower, P. (2012). Eight reasons I rejected your article. Elsevier connect.06
  7. 7. 2. Pedagogic literature King, N. (2004). Using templates in the thematic analysis of text. In C.Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 256–270). London: Sage. Method • Survey of all English-language books on scientific writing provided in national research institution for researchers • Survey of top ten web results using keywords provided by Japanese researcher • Used template analysis (King, 2004) and coded advice according to content. • Refined code during analysis (e.g. tense  grammar  accuracy) cf. Grounded approach – Template analysis – Content analysis Results • 15 English language books on scientific or research writing • 5 catch-all groups 07
  8. 8. Type Typical problem areas Accuracy* mistakes in facts, meaning, grammar, usage and spelling Brevity* too many words to say something simple Clarity* vague or ambiguous terms Objectivity overly subjective terms Formality abbreviations, contractions and informal terms 3. Five filters * Initial coding used only three types. 08
  9. 9. Type Example errors Accuracy The population of Japan is 12,734,100 [1] Brevity …providing the user with various XXX and asking him/her to... Clarity Referring to Smith [10], Jones notes that he… Objectivity We are confident that XXX will become… Formality A bunch of IT engineers collaborated and launched… 3. Five filters: example errors 09
  10. 10. 4. Practical applications No-tech • Highlighter pens Low-tech • Categorize/classify errors High-tech • Online genre-specific error detector 10
  11. 11. 4A. No-tech 11
  12. 12. 4B. Low-tech 12 Highlight error, number error and provide feedback sheet
  13. 13. 4C. High-tech: Online error detector 13 Common Error Detector (Morrall, 2000-17, PolyU)
  14. 14. Proposal: Rule-based pattern matching 1. Writer inputs text. 2. Text is searched. 3. Errors are identified. 4. Actionable feedback is displayed. 5. Writer acts on feedback. Specific genre with high generic integrity (Bhatia, 1993) • can target to user errors • can rule out unsuitable phraseologies unlike MS and Grammarly , e.g. There happened (to be a solution). 14
  15. 15. Method Corpus collection 629 graduation theses (2014 – 2017) Error collection and matching Identify, classify and code Interface Design, develop, deploy and evaluate 15 bbetweenW+(?:w+W+){1,2}?tob/gi
  16. 16. Online writing tool: Corpus-based error detector 16
  17. 17. Accuracy errors 1. The population of Japan is 12,734,100 [1]. 2. There are two types of… First,.. Second, ..Third,.. 3. All students … 4. XXX will play a key factor in the near future. 5. form XX to YY 6. p < 0.5 cf. (p < 0.05) cf. (p = 0.03) 17
  18. 18. Accuracy errors 1. Factual errors related to world knowledge 2. Factual errors related to research topic 3. Overgeneralization errors 4. Overly bold claims 5. Spelling and grammar errors, esp. LaTeX users 6. Statistical errors 18
  19. 19. Brevity errors 1. The concept that was chosen as the primary focus of this research is XXX 2. ..providing the user with various XXX and asking him/her to XXX. 3. We analyze XXX regarding the XXX qualities, XXX qualities and XXX qualities. c
  20. 20. Brevity errors 1. Using multiple vague words 2. Redundant words 3. Repeated words d
  21. 21. Clarity errors 1. XXX is something which is XXX from XXX of somewhere of, is something which XXX 2. Referring to Smith [10], Jones notes that he… 3. XXX found two AAA and one BBB, which CCC 4. The journal plans to publish this paper were just a rumour* *not in corpus 21
  22. 22. Clarity errors 1. Vague expressions 2. Lexical ambiguity 3. Referential ambiguity 4. Syntactic ambiguity 5. Garden path sentences 22
  23. 23. 1. We are confident that XXX will become XXXX 2. We are pleased to announce that XXX 3. …such as services to your XXX, to your XXX, and to XXX. Objectivity errors * ‘taming’ one’s subjectivity (Peshkin,1988) Peshkin, A. (1988). In search of subjectivity. One`s own. Educational Researcher, 17 (7), 17-21.23
  24. 24. Objectivity errors 1. Focus on people & feelings, not things & ideas 2. Emotive wording 3. Excessive personalization, e.g. use of pronouns 24
  25. 25. 1. To be more precise, act doesn’t directly cause the effect (E). 2. This is the RQ of this paper. 3. They launched the website right after the earthquake… Formality errors 25
  26. 26. Formality errors 1. Contractions 2. Abbreviations 3. Slang 4. Informal terms 5. Rhetorical questions 26
  27. 27. Online writing tool: Corpus-based error detector 27 Emoticons • Less intrusive • Key given outside text • Reduces repetition of text Alphanumeric code • Enables specific feedback e.g. C2-1, C2-2, C2-3 • Key given outside text • Reduces repetition
  28. 28. Conclusion Benefit for students • Bitesize actionable filters • Great for multiple drafts • Online tool: Accessible anytime anywhere Benefit for teachers • Reduces repetitive “correction” • Time-saving so can focus on deeper learning (or your own research) 28
  29. 29. http://web-ext.u-aizu.ac.jp/~jblake/ErrorDetector.html jb11.org jblake@u-aizu.ac.jp Any questions, comments or suggestions?

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