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E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
E-text in EFL - Four flavours
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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)

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