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Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
Practical cases, Applied linguistics course (MUI)
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Practical cases, Applied linguistics course (MUI)

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Three practical cases where corpus linguistics is applied for teaching / learning EFL

Three practical cases where corpus linguistics is applied for teaching / learning EFL

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  • --Learning how to draw information from lexical items in specific context --(Dp) MORE GENERAL = “guy” in which nationality of English? Why? GETTING MORE SPECIFIC = what type of English (register) = formal? Informal? Conversational? Written? / fixed phrases? (idioms?) / etc --(Dp) Main meaning of guy? In which nationality now? What registers? Press? Fiction? Newspaper article? (genre) MORE SPECIFIC --(Dp) MUCH MORE SPECIFIC = REGISTER + GENRE + SUBJECT IDENTIFICATION: WHICH IS WHICH? (Dp)
  • --(Dp) home-made electronic glossaries w/ activities to exploit lexical items (BIT LANGUAGE TO BE EXPLOITED ___ C.S. students design electronic resources (DATABASES -- END OF MAJOR PROJECT) (DP) --(DP) BABYLON FOR WRITING -- COMING HANDY WITH SPECIFIC COLLOCATIONS AND STRUCTURES (DECODING & ENCODING) = GOOD THING IS ALSO TO USE VISUALS
  • Página de inicio una vez registrado el programa WordSmith ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Comenzar a utilizar siempre una herramienta en el botón verde de la izquierda ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Para elegir textos “Choose texts” y siempre buscar textos en unidades de abajo a la izquierda (donde estén textos) y luego posicionarse sobre los mismos cuando éstos aparezcan en la pantallita de la derecha (en verde arriba se eligen con “ALL” si se quiere todos, o uno a uno con cursor), y después “store” para guardar y seguir eligiendo de otras carpetas en la izquierda, o si ya se han elegido todos, “store” y “ok”. ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Cuando se elige una palabra dada desde una lista y se pincha en la C (concord) se hace la búsqueda automática, pero también podemos refinar búsquedas indicando alguna palabra como contexto a la que se busca (ver imagen), o palabra con asterisco * para buscar derivaciones (ej., compr* en español daría todas las formas de comprar) o más palabras separadas por barra ( / ) para buscar todas las que queramos (ej., compr*/ vend*) ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Una vez hayamos dado al icono de clusters (barras en rosita en la parte de la derecha arriba en concord), si queremos copiar unas expresiones dadas, siempre podemos hacer “copy” to the clipboard (portapapeles) y luego pegar en cualquier documento word, etc. Lo mismo desde la lista de palabras (wordlist): elegimos las que queramos con el cursor y copiamos y pegamos. ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Un ejemplo de pegar una serie de expresiones dadas en el tema de contabilidad ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------
  • Ejemplo de colocaciones tras dar al icono de collocations en concord (dibujo parecido a un cazamariposas): en rojo aparecen las frecuencias más altas en las posiciones a la izquierda o derecha de un término que queramos ver (en este caso, por ejemplo, el nombre accounting en textos de contabilidad: por ejemplo “creative accounting” ocurre 96 veces, o “accounting data” 49, o Journal –luego espacio con alguna otra palabra y luego accounting, 21 veces).
  • Ejemplo de lista de palabras por frecuencia de este corpus sobre contabilidad (en textos escritos en inglés de este tipo– registro académico -- la primera palabra es siempre el artículo “the”). Como veis, en el icono de C podríamos dar para ejecutar la concordancia en una palabra dada (una vez nos posicionemos sobre ella). Recordad que para ejecutar una lista de palabras clave, este tipo de lista de frecuencia tiene que ser guardada antes en el programa (en File, save as) y le dais un nombre corto.
  • Ejemplo de lista detallada de consistencia – no lo veremos en este curso, pero se pueden utilizar para no sólo ver frecuencia, sino también distribución de palabras en más de una lista
  • Ejemplo de “aligner”– tampoco lo veremos en este curso, pero se utiliza para hacer corpus de textos paralelos (por ejemplo, para el análisis de la traducción).
  • --(Dp) home-made electronic glossaries w/ activities to exploit lexical items (BIT LANGUAGE TO BE EXPLOITED ___ C.S. students design electronic resources (DATABASES -- END OF MAJOR PROJECT) (DP) --(DP) BABYLON FOR WRITING -- COMING HANDY WITH SPECIFIC COLLOCATIONS AND STRUCTURES (DECODING & ENCODING) = GOOD THING IS ALSO TO USE VISUALS
  • Transcript

    • 1. The vocabulary / grammar component? Look at these examples and determine what components in language can be observed (e.g., vocabulary, grammar, text, register…? Explain why) a) To relate to + something vs. to be related to + someone b) Had he + past participle… / he would + past participle c) Could I please have … ?(in a restaurant) d) To make + someone + adjective versus to make + something + preposition (up, into…) e) The data are examined vs. The data is run
    • 2. Approaches to lexis and grammar in text units
    • 3. Discourse-based (top-down) e.g., Bhatia, 1998; Carter, 1998; Tribble, 2001; Scott, 2001; Flowerdew, 2004; Hüttner et al, 2009 (...) AIMS: To identify characteristic lexico-grammatical traits / discoursive items / movements, etc ...
    • 4. Sentence level (bottom-up approach) e.g., Johns, 1991; Aston, 1997; Bernardini, 2000; Curado, 2002; De Cock (2003); Yeung, 2009, Boulton (2010) AIMS: To discover / assess linguistic traits for language learning via empirical observation
    • 5. Specific aims (e.g., writing in a discipline): ESP / EAP /EPP … --EFL countries (Brazil, France, Spain...) --Working with specialized corpora (academic, professional...) to both identify and propose language / teaching solutions (key phraseology, rhetorical items, etc) within or across disciplines
    • 6. Academic register (Example 1 of lexico-grammatical approach) Start = “the good old semi-technical lexis” with hugely different frequencies, collocations, and meanings across disciplines (cf. Hyland, 2009; Durrant, 2009 …) e.g.; applied linguistics (on the other hand + textual act) vs. Electrical engineering (as shown in figure + research oriented) In a discipline, e.g.: 1) Computer Science (+ empirical / experimental, + research...) 2) Analysis and DDL for academic discourse competence (Spanish faculty / graduate students inform about research)
    • 7. EXAMPLE: C.S. (NNS) vs. Humanities..NS) vs CS (NS) -10 0 10 20 30 40 50 60 Tokens (in 10,000s) Types (in 1,000s) STTR # oftexts# ofdisciplines # ofgenres /texttypes NNS Corpus BNC selection NS Computer Science
    • 8. Relative word frequencies   WORD NNS Corpus for  Case Study BNC selection IN > TO < FOR > AS > THAT > IS < WE >> HAVE > CAN >> AT > USE >> FROM < WHICH = BUT > C.S. NS (40,180 tokens) Is For That Be Are As With This By On It > From Was > Can Not > Which Have We < Within These At Were Also
    • 9. Relative word frequencies 0 10 20 30 40 50 60 70 80 90 100 on by with be we can use from also should into both each so some may such I if our NNS texts BNC texts NS C.S.
    • 10. T-scores = + 2.0; M.I. Scores = + 3.0T-scores = + 2.0; M.I. Scores = + 3.0 (Collocational strength– Clear, 1999)(Collocational strength– Clear, 1999) Freq of node ‘new’ f(n): 221 Freq of collocate ‘technologies’ f(c): 123 Freq of node and collocate within span: 16 Size of corpus: 500120 We observe that (2.6 / 11.4)2.6 / 11.4) (CS NNS)(CS NNS)
    • 11. Lexical / grammatical patternsLexical / grammatical patterns Specialized collocationsSpecialized collocations (Topic / area) Eg. record + file Eg. New information technologies Eg. The use of [+ technology] eg. information + available on + digital media Lexical-rhetoricalLexical-rhetorical (Genre / text type) Ej. With respect to (+ concept) Ej. In this paper we Ej. As far as [+ subject] is concerned Ej. This is found to be (passive)
    • 12. Contrastive information: collocations, colligations, semantic associations, textual (Hoey, 2005) *Similar use *C.S. NNS gap = 15% more in BNC *C.S. NNS use = 15% more in NNS e.g. Appear* to be (similar) / to ensure that (NS) / in this sense (NNS) *[and NS and NNS field-driven?]
    • 13. WORD USE Similar Use NS only NNS only Collocation Appear* + to be (20 / 20.4 / 19%) It is possible to (28 / 8 / 28.4%) We observe that (0 / 14.7 / 0%) Colligation The basis for (Direct Object) (26.3 / 17.6 / 21%) Noun + to (no purpose / no reported speech) (26.5 / 1.2 / 17%) Be + asked to (present tense) (0 / 61.5 / 13%) Semantic Association In the field of + area (20 / 11.5 / 16%) To be seeking + functionality (28 / 0 / 18%) Related to + concept (26 / 76.9 / 36%) Textual Colligation As a result of (beg. paragraphs) (20 / 31.5 / 26%) One of the most + adj. (beg. sentences) (23.2 / 4.3 / 19%) For this reason, (beg. sentences) (2.9 / 20 / 3%)
    • 14. Genre and subject / field Lexical use Genre Subject Collocation Such as + examples (52 / 56% --C.S. papers) If and only if (71.4% --BNC: Logic) Colligation I had + past participle (47% --BNC reports) is + to be + past participle (22 / 17.8% --C.S.: IT and networking) Semantic Association Be + applied to + area (17 / 25.6 % --C.S. paper Introductions & Method) Be / appear + on the right + side (19 / 26.6% --C.S.: graphical design) Textual Colligation There is no + noun (beg. paragraphs) (34.8% -- BNC articles) This form + be completed (beg. paragraphs) (16.4% -- BNC: survey reports)
    • 15. Correlating frequency and use e.g., e.g., “we + observe” vs. “subj + has been / was observed” (also CS) 1) There is a more open use of words in patterns by NS authors (e.g., observe > this is observed to be / we observe / this has been observed to … ) 2) The NS limitation often obeys the rigid influence of formulaic items & fossilization (K. Hyland’s claim that the semi-technical items should follow the research- oriented stylistic inclination more in engineering = many choices for patterns)
    • 16. Word use and context Lexical use according to variables 0 5 10 15 20 Collocations Colligations Semantic Associations Textual Colligations number of items NNS and NS Subject Genre NS NNS
    • 17. Discipline versus NNS (Spanish) writing interference: How much? • L1 transfer problems with collocates & also, fossilized structures
    • 18. Data management on-line (e.g., Sketch Engine) Double objective: Distinguish most appropriate use & work with more phraseological possibilities = enrich writing (genre & field)
    • 19. Key points so far --Relative frequencies as key references of use --the lexico-grammatical component in specific text (top- down) --Statistical information on word behavior (bottom-up) --Exploring content + content / function + content elements: Overusing, under-using, misusing by Non-native --L1 transfer problem and fossilized items
    • 20. Text type-focus (more examples) In our organisation, we are just in the process of finalising our new 3-year rolling Strategic Plan. Crucial to achieving the objectivesachieving the objectives set in the Planset in the Plan will be the implementation of a large number ofwill be the implementation of a large number of new projects/initiatives that will have an impactnew projects/initiatives that will have an impact on every part of the organisation.on every part of the organisation.   A computer technical report? An academic lecture? The Pet Rock, the White Power Rangers, the Beanie Babies and the Furbies were toys that achievedachieved successsuccess in different years without coming fromin different years without coming from the rule-book or the experience database of anythe rule-book or the experience database of any single company. Despite their yesterday's success,single company. Despite their yesterday's success, the producers of such toys are not guaranteedthe producers of such toys are not guaranteed a place in the future that doesn't computea place in the future that doesn't compute based on yesterday's historical data.based on yesterday's historical data. A business review? A piece of news? (Give reasons for your choice)
    • 21. Activities in class and collocations / patternsActivities in class and collocations / patterns Another type of encryption designed primarily for business-to- business information exchange involves both a public and a private key. The company that plans to exchange data with another company provides it with a public key. This public key is used to encrypt data for transmission between the companies but is not used for decryption. The receiving company uses its private key to decrypt the data upon receipt. Data sent over the Internet runs the risk of being changed by a hacker during transmission. Data alteration includes deleting data, adding a virus to destroy data or report data back to the hacker, and altering a business transaction. Using digital signatures can reduce these risks. A digital signature contains a hash code derived from the data per se. Any data modification will cause a different hash code that will not match the digital signature. After the digital signature is encrypted within the message, the message is sent to the recipient, first with the sender’s private key and then with the receiver’s public key. Furthermore, the recipient must decrypt the message first with its private keys and then with the sender’s public key. This method ensures that the message can come only from the sender. Unregistered transactions: A business transaction may run the risk of being sent but not received. This risk can be costly if the transaction is in response to a limited-time offer, such as a bid on a government contract. The receipt of an important transaction should be confirmed by sending an acknowledgment message back to the sender. Corporations find themselves at the mercy of Internet hackers and vandals. They are looking for different ways to protect their own networks against intrusion from hackers. Companies must not only prevent unauthorized users from accessing private and sensitive data and resources but must also prevent unauthorized
    • 22. •NEED FOR TECHNICAL COMPOUNDS—Collin et al. (2004); Kaplan (2000) •Management control system -- management control •bit array -- number of bits •online tax preparation software -- tax software resource-based view of the firm -- view of the firm •Discussing ‘solutions’: (L.A. Robb, 1996) •sistema de gestión controlada; un string de bits; Un bit array*; software de tasas**; preparación de tasas online**; visión de la firma basada en riqueza??***; GENRE / TEXT TYPE VARIATIONS: In this paper we…-- En este papel*** RESEARCH PAPER It was argued that…-- Se argumentó que* PROCEEDINGS (company+) Sales analysis reported that…-- El análisis de ventas reportó* -- TECHNICAL REPORT Get your company started-- Coge a tu compañía empezada*** -- WEB SITE Our paper-- Nuestro papel*** --ABSTRACT In the current example-- En el corriente ejemplo*** TEXTBOOK Rhetorical-discoursive markersRhetorical-discoursive markers 1. Nominal wages increase because ofbecause of a demand impulse in 2. experienced tremendous growth because ofbecause of the demand 3. for when the market for enforcement is tighter, either because ofbecause of high demand or because ofbecause of low supply 4. service sectors are picking up because ofbecause of strengthening demand. ¿debido a …? COLLOCATIONS ______________corporate____ + LAW + IMAGE + GOVERNANCE + CONTROL + REPORT + PERFORMANCE + FINANCE + SECTOR CHECK for instance: EG. INFORME TÉCNICO / RENDIMIENTO DE LA EMPRESA… Observing language / L1 / L2
    • 23. Text type & field / topic focus (can one guess?) According to our historical data, …According to our historical data, … The paper describes our research findings…The paper describes our research findings… For the results above, a similar phenomenon hasFor the results above, a similar phenomenon has been found in a different site…been found in a different site… Maybe I should emphasize the importance of thatMaybe I should emphasize the importance of that Concept…Concept… If and only if X > Y can we then assume…If and only if X > Y can we then assume… Sorry, I couldn’t hear your questions…Sorry, I couldn’t hear your questions…
    • 24. Enhancing tools for the relation between lexico-Enhancing tools for the relation between lexico- grammatical items and text / discoursegrammatical items and text / discourse 14
    • 25. Enhancing learning (possible resources)Enhancing learning (possible resources) 14
    • 26. Lexis and grammar in the conversation register (Example with children)   • Speakers use parallel forms / e.g., pattern question and answer replies (Carter, 2004) • language-in-action collaborative tasks among speakers (McCarthy, 1998) • Categories: Age, nationality, situation / topic… • Example: Children in USA– English / Spanish Child Age Aimee 5;4.0 Justin 4;6.0 Melissa 3;4.0 Trevor 4;3.0 Willie 6;1.0
    • 27. Oral texts: CHILDES 0 2 4 6 8 10 12 14 16 18 Directories (3-6 yr.) Directories (otheryrs.) Sources (3-6 yr.) Sources (otheryrs.) English Spanish Bilingual 0 20 40 60 80 A. English S. Spanish B. Spanish Average lengths Aver. W.length Aver. S.length • Children / adult (+or –familiar = situations; Carter) • Production / reception (tagging adults’) Standardised ratios (STTR) 0 10 20 30 3-year old 4-year old 5-year old 6-year old nºwordsper1000tokenss A. English S. Spanish B. Spanish
    • 28. Frequency + dispersion (DCLs) • Overall similarities and differences: 1. + inter-personal statements 2. + everyday words / worlds (coche / boy) 3. + markers, references (esto / aquí / then) 4. 2nd vs. 3rd persons 5. Concise / short sentences vs. Longer ones; less vs. More opinion (me parece) 6. Age levels American English (monolingual) Spain’s Spanish (monolingual) Spanish / English (Bilingual Latin American in USA) Word TOTAL You 30921 I 27118 A 23615 Be 23388 The 20701 It 20222 What 16925 To 15343 Do 14944 That 14056 Dem 10622 Not 9415 And 8774 Go 8507 This 7871 In 7848 No 7597 On 7351 One 7227 Have 7128 Word TOTAL A 25204 No 23096 Que 19932 La 16372 El 16303 Es 13580 Se 12636 Qué 12477 De 10391 Sí 10365 Éh 8511 Lo 7069 En 6673 O 6071 Me 5999 Aquí 5951 Está 5317 Mira 5298 Los 5201 Mí 4610 Word TOTAL No 3485 A 3468 Y 3209 Que 2843 El 2162 La 2010 Sí 1723 Es 1609 Eh 1482 Aquí 1386 Lo 1272 Un 1261 De 1226 Se 1191 Me 1111 Cómo 1078 Te 1076 Ya 1047 Está 946 Yo 889 American English (monolingual) Spain’s Spanish (monolingual) Spanish / English (Bilingual Latin American in USA)   I don’t know I’m goin(g) to (5 & 4 years)  Mommy, you… (all) I’m not gonna  (5 years) I want ta go   (4 years) You want to…? (4 & 3 years) I’m gonna (6 years) You have to You open it  I not going to (3 years) A ver si A lo mejor No sé qué es (6 & 5 years) Es que como no… (6 years) Porque no + verb (6 & 5 years) A mí no me gusta (6, 5 & 4 years) Mira lo que + verb (4 years) Pues creo que Lo tienes que Y luego (5, 4 & 3 years) Y ya está Y lo pone en Y luego (6 & 5 years) Me voy a + verb (all) No me acuerdo (all except 6 years) No se puede Me parece que (4 years) Sí es eso Y yo también (all) Mamita, el de… (3 years)
    • 29. Examples: • Questions asked by adults vs. Children (3 / 4) • Structures (e.g., Be + going to / gonna (3 / 4) Age level-related development 3 and 4 Fr eq. Field – Year 3 Field – Year 4 1 Do you have... / would you like (adult) / where did you ... (adult) / what else did you... (adult) / why don't you... (adult) / what do you call... (adult) I don't (want) / I don't see (no birds) / I'm finished 2 I don't know / I don't think you (adult) / I want to (go) / I going to / I don't want to / I want some (more) / mommy, I want (a) you have to / mommy, you... / how you do it / how do you do it / where you going 3 Chug a chug a chug / make a (dog) (adult) / make a (plane) (child) / it looks like a / dis is a / I never heard of a / it's gonna be a 4 Oh yeah? Oh look it what does it say / you turn it / 5 what kind of... (adult) I like to / would you like to (mother) 6 play with (toy) what is dis / what is that (mother)
    • 30. • Comprehension / production according to age: keyness (vs. Other ages and Other directories) e.g., + likes and dislikes / commands (all since 3) + declaratives / questions (since 4) + numbers (5) / + colours (6) Negative: specific words (e.g., “suitcase” – age 3 / “dem” – age 5, etc) Possible applications for pedagogy Keyword type Year 3 Year 4 Year 5 Year 6 POSITIVE I [NAMES] A HE GOIN(G) [WHY] D(O) [YA] UH DIS MONKEY BUGS IS [THIS] DOSE THA DEY DE TIRE KNOCK [NAMES] I DE DIS MOMMY I’M [COULD] GRAIN DAT’S GONNA [INFINITIVES] NOW DESE NEED DERE CAN MOM [AUXILIARIES] HEY PAINT ALLIGATOR OLD LADY [BALANCE] FIVE YUP OK HOW [MHMH] [NUMBERS] ASK GIRAFFE [SOMETIMES] THINK GOP THIS SCHOOL GUTCHET NINE WE [PICK CARDS] PENGUIN BLUE WIN BACKWARDS PENGUINS GREEN CARDS I ROBBIE’S CANDYLAND GAME MOVED HATE STAY PURPLE PICKED THEY HEARTS [ORDER] NEGATIVE HMM BOOM YUM SUITCASE GAIN DOLLY BOOKS KNOWS TV FIT [MOTHER’S] ICE BATH BOOM WORD HAPPY TOY HOUSE SHAPE CHAIR WHO WHAT’S OKAY TAKES DEM FO BRIDGE REINDEER DAT MARBLE BREAK ‘T
    • 31. • Broader Contrastive View: Overall key items in English (vs. BNC sampler) and Spanish (vs. Written material—news, essays, ads—on the web) keyness : e.g., + questions (what / qué) + personal inclinations (I want / quiero...) + negation / dislikes / commands (don’t / no / ...) EFL content for Spanish learners American English Spain’s Spanish Word Keyness You 65.208,5 Dem 60.718,0 What 53.113,5 Do 38.399,9 Go 28.287,0 I 24.541,8 A 21.785,5 It 21.218,3 Zero 20.639,2 Not 18.937,5 Mommy 18.612,8 Want 16.661,1 No 16.515,1 That 16.035,6 Don't 15.150,2 Oh 14.576,8 Here 14.421,3 Huh 14.326,5 Put 14.182,9 See 13.663,4 Word Keyness Qué 7.722,3 No 6.660,0 Sí 6.572,3 Te 4.604,5 Mira 3.733,8 Aquí 3.411,6 Está 3.213,1 Mí 2.828,3 Me 2.240,5 Ver 2.202,0 Di 2.197,7 Eh 2.183,7 Ti 2.136,9 Ah 2.044,7 Ay 1.590,0 Yo 1.575,6 Ahí 1.555,9 Esto 1.292,1 Así 1.271,5 Ahora 1.244,9
    • 32. Nationality / Age comparison Interpersonal Declarative Markers American English 3 <> 4 ,4583 ,0057 ,0593 3 <> 5 ,0003 ,4923 ,5289 3 <> 6 ,4660 ,2085 ,0002 4 <>5 ,0000 ,0311 ,0968 4 <> 6 ,0252 ,0003 ,0000 5 <> 6 ,5989 ,0629 ,0062 Spain’s Spanish 3 <> 4 ,3617 ,1213 ,9714 3 <> 5 ,7595 ,0052 ,1917 3 <> 6 ,9027 ,0794 ,0398 4 <>5 ,4110 ,9072 ,2047 4 <> 6 ,3279 ,5768 ,0434 5 <> 6 ,7979 ,2432 ,4016
    • 33. • COLLABORATIVE PLAY / TASKS • DYNAMIC AND VISUAL • RECEIVE AND PRODUCE INFORMATION Resources for pedagogical aims in the children’s lessons
    • 34. 1. Interpersonal (i.e., use of first and second person pronouns, vocative words, commands); 2. Declarative (demonstrative pronouns and adjectives, third person statements, expression of preferences and dislikes); 3. Markers (discourse connectors, interjections, gambits) 4. Nouns (30 % English / 26 % Spanish); 14.6 % verbs / 7 % adjectives Linguistic-discursive priorities • 60 keywords at each age level > t-scores 1. Interpersonal = years 3 and 4 (E = S) 2. Declarative = years 4 and 3 (E); 3 (S) 3. Markers = years 4 and 5 (E); 5 and 6 (S) 4. Nouns = years 5 and 4 (E); 5 (S) *MOT: want to take it apart first ? [interpersonal question] *CHI: right here +... [marker / metadiscourse / production] *MOT: how do you get it out ? [interpersonal question] *MOT: how do you get the pieces out ? [interpersonal question / repetition] *MOT: like this ? [question / metadiscourse / repetition] *CHI: yeah . [answer / production] *MOT: ok . [answer / marker] *CHI: are ya gonna talk to it without the puzzles out of it ? [interpersonal question / production] *MOT: yeah . [answer] *MOT: <you can just put> [//] why don't you put a piece and then I'll put a piece . [command / question] *CHI: ok . [answer / marker / production] *MOT: this looks like Mickey's head . [declarative / naming] *MOT: is that his head ? [question / repetition] *CHI: yep . [answer / production] *MOT: ok . [answer / marker] *CHI: there . [metadiscourse / production] *MOT: now it's your turn . [marker / interpersonal prompt] *CHI: um . [pause / marker / production] *MOT: ok . [answer / marker] *CHI: there . [metadiscourse / production] *OBS: a ver # me dices como te llamas . [interpersonal question] *CRI: Cristina Perez Perez . [answer / production] *OBS: Cristina Perez Perez ? [question / repetition] *OBS: oye que estabas haciendo ahora en clase ? [marker / interpersonal question] CRI: estaba escribiendo y pintando . [answer / declarative / production] *OBS: y que estabas escribiendo y pintando ? [interpersonal question / repetition] *CRI: escribiendo en el cuaderno azul . [answer / declarative / production] *OBS: si # oye y que es el cuaderno azul ? [marker / interpersonal question / repetition] *CRI: uno que tiene cuadrados rojos y lo voy a terminar .[answer / declarative / production] *OBS: si y que te ha dicho la sor # que lo haces bien ? [marker / interpersonal question] *CRI: si . [answer / production] *OBS: y tambien pintas en ese ? [marker / metadiscourse / interpersonal question] *CRI: &=afirma . [answer / production] *OBS: y que pintas ? [marker / interpersonal question] *CRI: pin [/] pinto cuadros . [answer / production]
    • 35. lessons Concepts 3 4 5 Linguistic content 3 4 5 Colours X Like/ Dislike X X X Greetings / introductions X X X Prepositions X X Numbers X Commands (Imperative) X X X Sizes and shapes X X X To be X X X The weather X It is … X X X Feelings (love, hate …) and likes (I like/ I don´t like) X X X Are you ….? X X Specific Vocabulary X To have X X X Simple descriptions of objects, people ... X X X Personal and possessive pronouns X X X Vocabulary X X X X Naming of objects, people –simple definitions X X X Personal and possessive pronouns X X X Space /time orientation (up, down, near ...) X X X Can/Could Would you like … X Actions (read, jump, run) X X Adjectives Comparative and superlative X X Family X X These is/are X X Sensations, states of mind (happy, bored, I am cold…) X X Do/does Yes/no questions X X Daily routines (wash one’s hands, have breakfast…) and parts of the day X X X Wh/ open questions Interrogative pronouns X X
    • 36. • Self-access and group interactivity with key language at age (EFL and L1): – Adaptive for age / knowledge level (e.g., focus on common words, common structures, simple naming, defining ...) – Assessment by teachers + other professionals (child pedagogy / psychology / sociology counsellors...) • Animations / graphics / visual aspects > motivation in MULTIMODALITY (e.g., audiovisual references in metadiscourse, interpersonal addresses, etc) • Interaction via Computer & networking: learning and playing too Applications / Implications
    • 37. • Think about the fields / topics that are important for 12-15 year old teenagers: >What words are more important and why? Also think about how to best have students acess and exploit them…?
    • 38. The vocabulary / grammar component in speech? Determine what components can be observed (e.g., vocabulary, grammar, text, register…? Explain why) a)Dunno about that, maybe, I ain’t sure, maybe b)Had I known back then, then that’d’ve made some difference! c) Could you just shut up once and for all! d) Whatever he’s thinking, he sure chews it up e)How’re you doing? Fine, thanks f)Needless to say, need I say more! g)Just going for a stroll…!

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