E-TEXT in E-FL: Four flavours

1. E-TEXT in E-FL:Four flavours Przemek Kaszubski Joanna Jendryczka-Wierszycka Michał Remiszewski Włodzimierz Sobkowiak

2. The advantages of e-text: • 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 Usby Michael Wesch for a good video treatment of these issues)

3. Presentation plan: • 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?

4. IFAConc – web-concordancing with EAP writing students Przemysław Kaszubski

5. Acknowledgements • 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

6. Concordancing • a form of e-text processing for a linguistic purpose: descriptive or pedagogical • paper concordance <computerisedconcordancing • 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

7. Corpora Search (click on picture to go to IFAConc; log in for best effect)

8. DDL issues and IFAConc • 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’)

9. E-text integration in IFAConc • 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)

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. Beyond bottom-up concordancing • 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 conductmore searchesthat aremore in-depth ... • teacherlearns about students’ linguistic and cognitive abilities... • ... while the database of relevant lg observations continues to grow (and to gradually feed ‘Shared’ History and Resources)

13. Concordancing with EAP students – basic stats • IFAConc (09.2006 – 02.2009) : • 206 participants • All searches > 37,000 • Students' All searches: > 17,000 • All annotated: > 2,200 • Students’ annotated – 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

14. Testimonials • “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

15. e-text annotation - why bother? Joanna Jendryczka-Wierszycka

16. annotation (tagging)

17. Facebook, Picasa, Gmail, Etc. Linguistic (e-text) annotation annotation (tagging)

18. definition different levels of annotation: explanations, examplesand 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 Semantic Discourse/pragmatic Stylistic Prosodic Lemmatization Markup e-Text annotation exemplified

21. adding information about word classes er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 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 Z8VV0 like E2+AT1 a Z5JJ particular A4.2+NN1 shade O4.3IO of Z5NN1 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 informationabout 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. lemmatization = 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. Towards competence mapping in language teaching/learning Michał Remiszewski

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. e-text in Second Life: reification of text? Włodzimierz Sobkowiak

39. Types of "ordinary" e-text in SL: • public text-chat, • Instant Messaging (IM), • notecards, • whiteboards, • object info fields, • avatar profile info fields, • inventory contents, • menu system

40. Unique e-text affordances in SL: 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, phoneticdominoes: 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.

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 You'll find my dominoes in my Virtlantis classroom in Second Life.

43. Other examples of e-text reification: David Merrill's (MIT) 'siftables' (click to watch on YouTube)