2. E-TEXT in E-FL: Four flavours 1. Przemek Kaszubski 2. Joanna Jendryczka-Wierszycka 3. Michał Remiszewski 4. Włodzimierz Sobkowiak

3. 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 Machine is Using Us The advantages of e-text:

4. PK: IFAConc - web-concordancing with EAP writing studentsIFAConc - web-concordancing with EAP writing students JJW: e-text annotation - why bother?e-text annotation - why bother? MR: Towards competence mapping in language teaching / learningTowards competence mapping in language teaching / learning WS: e-text in Second Life: reification of text?e-text in Second Life: reification of text? Presentation plan:

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

6. 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

7. a form of e-text processing for a linguistic purpose: descriptive or pedagogical – paper concordance < computerised concordancing – 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

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

9. 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

10. 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

11. History (click on picture to go to IFAConc History, log in when prompted)

12. Resources (click on picture to go to IFAConc Resources – reg’d IFA users only)

13. 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

14. 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 Concordancing with EAP students – basic stats

15. – “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

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

17. annotation (tagging)

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

19. definition different levels of annotation: explanations, examples and utility limitations of annotation answer to “Why bother?” e-Text annotation - contents

20. 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

21. Part-of-Speech Parsing Semantic Discourse/ pragmatic Stylistic Prosodic Lemmatization Markup e-Text annotation exemplified

22. 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

23. 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

24. 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

25. 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

26. 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

27. adding information about the modalization, phraseological units, metaphor, kinds of speech act, etc. that occur in a spoken dialog ok? a bird in the hand is worth two in the bush May I open the window, please? e-Text annotation - pragmatics

28. 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) 'Where've you got in mind, sir?' e-Text annotation - stylistics

29. 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

30. 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

31. 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

32. “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?

33. Michał Remiszewski Towards competence mapping in language teaching/learning

34. Technology-driven Practice-driven Reasons for e-learning

35. Structured syllabus No access to the structure of competence Problem

36. Synchronic view Dynamic view Solution: competence mapping

37. CLIP ; AMBER ONE

38. 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

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

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

41. 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:

42. 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.pork chops

43. Phonetic dominoes: close-up view of pork chops You'll find my dominoes in my Virtlantis classroommy Virtlantis classroom in Second Life.

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