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E-text in EFL - Four flavours

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E-TEXT in E-FL : FOUR FLAVOURS …

E-TEXT in E-FL : FOUR FLAVOURS
Dr. Przemysław Kaszubski : IFAConc - web-concordancing with EAP writing students
Mgr Joanna Jendryczka-Wierszycka : E-text annotation - why bother?
Dr. Michał Remiszewski : Towards competence mapping in language teaching/learning
Prof. Włodzimierz Sobkowiak : E-text in Second Life: reification of text?
[ http://ifa.amu.edu.pl/fa/node/1144 ]
[ http://ifa.amu.edu.pl/fa/node/1123 ]

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  • 1. E-TEXT in E-FL: Four flavours
    • Przemek Kaszubski
    • Joanna Jendryczka-Wierszycka
    • Michał Remiszewski
    • Włodzimierz Sobkowiak
  • 2.
    • flexibility: fonts, formats, attributes
    • correctibility, accuracy, up-to-dateness
    • searchability: local and global
    • portability: PDA, Kindle, smartphone, etc.
    • manipulability: types, media, channels
    • annotability: tagging, parsing, semantic web
    • immediacy: speed of transmission and processing
    • (hyper-)linkability, nonlinearity
    • sharability, openness, low cost
    • popularity among 'digital natives'
    • (See The Machine is Using Us by Michael Wesch for a good video treatment of these issues)
    The advantages of e-text:
  • 3.
    • PK : IFAConc - web-concordancing with EAP writing students
    • JJW : e-text annotation - why bother?
    • MR : Towards competence mapping in language teaching / learning
    • WS : e-text in Second Life: reification of text?
    Presentation plan:
  • 4. Przemysław Kaszubski IFAConc – web-concordancing with EAP writing students
  • 5.
    • Developers:
      • Paweł Nowak
      • Dominique Stranz
    • Over 200 Student Participants :
      • 12 : MA and BA seminars 2005-6
      • 16 : 1MA and 2MA seminars 2007-8
      • 18 : 1BA Writing 2007-8
      • 32 : 1MA Academic Writing 2008-9
      • 140 : 3MA Acad. Discourse Part-Time Lecture 2008-9
    Acknowledgements
  • 6.
    • a form of e-text processing for a linguistic purpose: descriptive or pedagogical
      • paper concordance < computer ised concordanc ing
      • data-driven learning (DDL): operationalisation of gap-noticing (also: form-focused instruction ; awareness-raising)
    • ‘ shunting’ (Halliday):
      • vertical / paradigmatic reading – KWiC
      • horizontal / syntagmatic reading – KWiC + context
    • pedagogic concordancing for EAP/ESP learning:
      • repetitions / patterns (light theory: ‘extended units of meaning’ – Sinclair; ‘lexical primings’ – Hoey)
      • dispersion within corpus
      • variation across corpora
    Concordancing
  • 7. Corpora Search (click on picture to go to IFAConc; log in for best effect)
  • 8.
    • DDL under-practised and under-researched – few dedicated, student-friendly tools. Some needs:
      • facilitate training and current practice (time factors: what to search for and how ; inductive analysis)
      • facilitate (but not replace) noticing and deeper-processing
      • manage results
      • facilitate teacher control and teacher-student interaction
      • integrate with syllabus etc. (also ‘non-e-text’)
    • IFAConc and EAP writing – some assumptions:
      • trace relevant academic primings (interesting patterns are many)
      • students (meta)linguistically conscious = co-research possible
      • enable more complex search patterns and subtle observations
      • encourage autonomy and individualisation (personal ‘primings’)
    DDL issues and IFAConc
  • 9.
    • e-text sample
    • collection of e-text samples (= corpus; cline of spec. corpora)
    • selective structural markup (XML)
    • linguistic annotation (POS tagging)
    • conc. searchability (syntax language + options)
    • conc. manipulability: sampling, re-sorting, corpus switching
    • automatic conc. summary: stats table, collocate counting
    • unique URL search address – hyperlinking
    • note-taking ( annotation ) – personal and/or T-S collaborative
    • search logging (personal and global History database – browsable / searchable / hyper-linkable
    • towards dynamic conc-illustrated EAP textbook ( Resources )
    E-text integration in IFAConc
  • 10. History (click on picture to go to IFAConc History , log in when prompted)
  • 11. Resources (click on picture to go to IFAConc Resources – reg’d IFA users only)
  • 12.
    • hyperlink-assisted concordancing
      • Corpora Search hyperlinks
      • History search hyperlinks
        • also Corpora Search ID and History Search ID options
      • integrated with other materials
        • e.g. feedback links; resources for self-exploration
    • T-S interactive annotation
    • = less time-costly, more meaningful concordancing:
      • more students conduct more searches that are more in-depth ...
      • teacher learns about students ’ linguistic and cognitive abilities ...
      • ... while the database of relevant lg observations continues to grow (and to gradually feed ‘Shared’ History and Resources)
    Beyond bottom-up concordancing
  • 13.
    • IFAConc (09.2006 – 02.2009 ) :
      • 206 participants
      • All searches > 37,000
      • Students' A ll searches: > 17,000
      • All a nnotated: > 2 ,200
      • Students’ a nnotated – c. 1,000
    • PICLE Conc (04.2004 – 08.2005)
      • 125 ... IP numbers (15-20 active users...)
      • All (?)students’ searches < 3,700
      • Students’ annotated (non-interactive) – about 40
    Concordancing with EAP students – basic stats
  • 14.
      • “ I found this research valuable as I used a few examples from Concordance database in my MA dissertation. I value the research as it provides me with proper examples of native uses. Whenever I look for a word usage I Google it, yet it never gives me 100% certainty that the internet source is a reliable one. Conc on the other hand is a reliable tool which a student can trust. ” ( agooska , H - 37145)
      • “ I regret I didn’t search these Conc pages before I wrote the majority of my dissertation…It is really a vital source - very helpful!” ( Aleksandra, Resources Textbook comment )
    • Some more practical applications will be shown at ELT training on 27th March
    Testimonials
  • 15. Joanna Jendryczka-Wierszycka e-text annotation - why bother?
  • 16. annotation (tagging)
  • 17.
    • Facebook, Picasa, Gmail, Etc.
    • Linguistic (e-text) annotation
    annotation (tagging)
  • 18.
    • definition
    • different levels of annotation: explanations, examples and utility
    • limitations of annotation
    • answer to “Why bother?”
    e-Text annotation - contents
  • 19.
    • corpus annotation is „the practice of adding interpretative, linguistic information to an electronic corpus of spoken and/or written language data” (Leech, 1997: 2)
    • It „is widely accepted as a crucial contribution to the benefit a corpus brings, since it enriches the corpus as a source of linguistic information for future research and development” (ibid.)
    e-Text annotation defined
  • 20.
    • Part-of-Speech
    • Parsing
    • S emantic
    • Discourse/ pragmatic
    • Stylistic
    • Prosodic
    • Lemmatization
    • M arkup
    e-Text annotation exemplified
  • 21.
    • adding information about word classes
    • er 93 FU she 93 PPHS1 was 93 VBDZ terrific 93 JJ in 97 [II/1] CS21%/ that 97 [DD1/1] CS22@/ film 93 [NN1/1] VV%/
    • er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
    e-Text annotation - POS
  • 22.
    • by far most frequent annotation
    • useful in: frequency lists or frequency dictionaries with grammatical classification, MT, Translation studies, contrastive linguistics, lg teaching, TTS synthesis
    POS-tagging ctd
  • 23.
    • syntactic analysis into such units as phrases and clauses (sentence structure)
    • [S[N Nemo_NP1 ,_, [N the_AT killer_NN1 whale_NN1 N] ,_, [Fr[N who_PNQS N][V 'd_VHD grown_VVN [J too_RG big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] ,_, [V has_VHZ arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1 park_NNL1 ]N]P]N]P]V] ._. S]
    e-Text annotation - parsing
  • 24.
    • adding information about the semantic category of words, e.g. “bark”
    • for translation and lexicography
    • PPIS1 I Z8 VV0 like E2+ AT1 a Z5 JJ particular A4.2+ NN1 shade O4.3 IO of Z5 NN1 lipstick B4
    e-Text annotation - semantics
  • 25.
    • adding information about anaphoric links, e.g. for MT
    • S.1 (0) The state Supreme Court has refused to release{1 [2 Rahway State Prison 2] inmate 1}} (1 James Scott 1) onbail . S.2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction . S.3 (1 Scott 1) had asked for freedom while <1 he waits for an appeal decision .
    e-Text annotation - discourse anaphora
  • 26.
    • adding information about the modalization, phraseological units, metaphor, kinds of speech act , etc. that occur in a spoken dialog
    • <IP MOD=interactive>ok?<IP>
    • <IP PU=proverb MET=true Source=nature Target=prudence>a bird in the hand is worth two in the bush</IP>
    • <IP SA=request> May I open the window, please?</IP>
    e-Text annotation - pragmatics
  • 27.
    • it's about “stylistic features in literary texts” usually S&TP (McEnery et al. 2006:41)
    • S&TP = direct speech, indirect speech, free indirect thought, etc (Leech 2004)
    • <sptag cat=FDS who=K next=FDS whonext=J s=1 w=6> 'Where've you got in mind, sir?'
    e-Text annotation - stylistics
  • 28.
    • segmental pronunciation
    • prosodic boundaries, prominent syllables and abnormal sound lengthening
    • Both highly valuable in accent studies
    • ik heb he%m% | n^e^gen maal ontvangen denk ik
    • speaker A : jan | en ook piet waren hier al eerder twee jaar geleden
    • speaker B : ja| dat weet ik || maar wanneer
    e-Text annotation - prosody
  • 29.
    • lem m atization = adding the identity of the lemma (base form) of each word form in a text
    • markup = originally text division into paragraphs, font characteristics (all noninterpretative, text -inherent qualities)
    • also: markup for speaker/writer identification, useful in sociolinguistics
    e-Text annotation - lemmatization & markup
  • 30.
    • accuracy
    • annotation= always interpretation. It's never theory free (MWUs, -ing)
    • ambiguity tags nothing bad! (better than wrong tags) – e.g. CLAWS ditto tags, portmonteau tags – if consistent!
    • the importance to keep “pure” text separately (Sinclair)
    • which one, how, where, when applied and by whom ?
    Limitations of annotation
  • 31.
    • “ it enriches the corpus a source of linguistic information for future research and development” (Leech 1997)
    • fields possibly profiting from it: lexicography, MT, translation studies, discourse studies, pragmatics, literary studies, contrastive linguistics, lg teaching, grammatical lg analysis, TTS synthesis, accent studies, sociolinguistics
    • “ no one in their right mind would offer to predict the future uses of a corpus ” Leech, 2004
    • References
    Why bother?
  • 32. Michał Remiszewski Towards competence mapping in language teaching/learning
  • 33.
    • Technology-driven
    • Practice-driven
    Reasons for e-learning
  • 34.
    • Structured syllabus
    • No access to the structure of competence
    Problem
  • 35.
    • Synchronic view
    • Dynamic view
    Solution: competence mapping
  • 36. CLIP ; AMBER ONE
  • 37.
    • It will allow the creation and administration of interactive language tasks for learners.
    • It will automatically check the accuracy of learners’ answers, and not just the obvious multiple choice, but also gap input going way beyond one or two words.
    • It will provide exhaustive student performance reports both as stats for large groups as well as individuals. Reports will be delivered to the learner and to the teacher.
    • It will help identify problem areas and dynamics in learners’ linguistic competence.
    AMBER ONE
  • 38. Włodzimierz Sobkowiak e-text in Second Life: reification of text?
  • 39.
    • public text-chat ,
    • Instant Messaging (IM),
    • notecards ,
    • whiteboards ,
    • object info fields ,
    • avatar profile info fields ,
    • inventory contents ,
    • menu system
    Types of &quot;ordinary&quot; e-text in SL:
  • 40.
    • Linguistic symbols, from phonemes/letters to whole texts can be reified into 'rezzed' (created) three-dimensional objects, thus creating innovative manipulative affordances, impossible in First Life and appealing especially to kinaesthetic learners. For example, phonetic dominoes : words reified as moveable and audio-enhanced blocks which attract or repel each other, according to e-FL-relevant phonetic criteria, such as segmental makeup, syllable number, stress pattern, etc.
    Unique e-text affordances in SL:
  • 41. Phonetic dominoes ( view from above ) Arrange the nine coloured cubes domino-style to match sounds at the edges of words. Cubes say their name when left-clicked. Here's the list (in alphabet order): apricot, cereal, cream, ketchup, lettuce, milk, pork chops , spoon, T-bone steak .
  • 42. Phonetic dominoes : close-up view of pork chops ; click to hear You'll find my dominoes in my Virtlantis class room in Second Life.
  • 43. Other examples of e-text reification: David Merrill's (MIT) 'siftables' (click to watch on YouTube)