Translating Data Driven Language Learning into French

Tom Cobb

Dép. de Linguistique Université du Québec à Montréal

Peut-on augmenter le rythme d’acquisition lexicale par la lecture ?

Une expérience de lecture en français appuyée sur une série de ressources en ligne.

Tom Cobb, Université du Québec à Montréal

Can the rate of lexical acquisition from reading be increased?

An experiment in reading French with a suite of on-line resources.

Tom Cobb, Université du Québec à Montréal

Background: Data-Driven Language Learning On-line

 Discovery learning  Learner-as-linguist  Alternatives to rules & definitions  Concordancing 

Grammar Safari



Concordancing



Concordancing on-line



Concordancing on-line in French

The idea of shortcuts to L2

 It has long been known that the time available for LL through experience is inadequate in most cases  Learner’s time is short  Database is dispersed  Much time is needed to expose patterns in data

The traditional shortcut to L2: Explicit declarative knowledge

  ‘Rules’ in grammar ‘Definitions’ in vocabulary  Never all that successful  Linguistic computing makes another kind of shortcut possible  Data aggregation & compression  Rapid pattern exposure

‘Rules’ in grammar



Error: * This is one of the biggest car in the world



Solution: We tell students the rule: “After one of the comes a plural noun”

Or, tell them to go check the data 10 of 396 examples in Brown Corpus…

Advantages of data based learning

     Learners initiate search themselves Patterns are large, crystal clear Linguistic authenticity is assured Learners have positive role to play: they are linguists (Cobb, 1999)  Cf. negative ‘mistake maker’ role in traditional approach Technology is used in a non-gaming context  And used well, since concordances can not be generated by any other means

Building a second lexicon - big need for data aggregation

 Contextual inference problematic   On learner-side (inferences generally unsuccessful; Laufer, Haynes et al studies) On data-side (poor contexts, vast distances between)  Dictionary information hard to use by those who need it  Direct instruction runs up against task-size problem

Can computer data-aggregation help build a second lexicon?

Two ideas:

1. List-driven learning: Corpus and concordance linked to frequency lists  Frequency based testing to find level  Make yourself a dictionary at the level where you are weak  Example:

Lexical Tutor

Problems with list-driven learning:

1. Needed frequency information seems unavailable except in English 2.

List is not everyone’s cup of tea So, another idea: Adapt computational tools to the less structured context of extensive reading

Introducing R-READ

R

eading

E

xtended

A

uthentic

D

ocuments with

R

esources …of a kind that are increasingly capable of Internet delivery

Brief History of Computer Assisted L2 Reading

 Pre-Internet Age: Skills based, no proof of transfer, “too little to read”  Internet Age: Too much to read, reading reduced to scanning

R-READ as a middle way

 that uses Internet resources to  make extensive authentic documents readable, and  target specific learning

Personal Anecdote

   

Me, 1980, French reading test looming… Method: read one book, several times, aided by a ‘language consultant’

 Voltaire’s

Candide

 Francophone girlfriend

Look into every word; deconstruct every structure

 Repeat pronunciations  Stick-on concordances  Little notebooks

Stick on’s removed, fewer look-ups

 First Hurdle clear in about a week

Equity problem:



Not everyone can find a personal language consultant



Question: Would it be possible to itemise what the consultant was doing and reproduce these services universally?

An electronic language consultant?

Go online VLC

User lexicon

Research Base (1)

 Listen & read  Draper & Moeller, 1971; Stanovich, 1896. Lightbown,1992 

Concordance: computer aided contextual inference

 Huckin, Haynes & Coady, 1991; Cobb, 1999; Zahar, Cobb, & Spada, in press 

Database as take-home learning outcome

  Minimal time-off-task (Cobb, 1997) Collaborative (Horst & Cobb, in prep)

Research Base (2)

 Dictionary  Can disrupt reading, cause misconception (Noblitt et al, 1990)  Useful pair with context if it

follows

effort to infer (Fraser, 1990)  Click-on interface  Even if useful, dictionary will not be used if effortful (Hulsteijn et al, 1996)

Research Base (3)

 R-READ as

middle position

between stark choices of the past on extensive reading 

Alternative 1:

Natural extensive reading is an adequate source of vocabulary growth in L1 (Krashen, 1989) or L2 (Nagy, 1997) 

Alternative 2:

Vocabulary growth will not happen if conditions are not in place; assure they are in place by pre-teaching wordlists, out of context if necessary (Nation & Waring, 1997)

Middle approach made possible through ‘NTIC’

 Vocabulary enhanced reading (Hulstijn, Holander, & Greidanus, 1996)  Learners make their own way through roughly tuned texts with support of resources  In-context feature preserved  But is it useful?

 What follows is a substantial test of this middle approach

Pilot Test of de Maupassant’s Boule de Suif with R-READ

 How do vocabulary learning results of reading with online lexical resources compare to results of reading without these tools?

 Baseline for comparison: Repeated reading case studies of lexical acquisition by Horst (2000)

R’s reading of German novella (Horst, 2000)

       

R

– motivated adult intermediate learner German novella 9500 words 300 unique targets (1:32) 45% rated unknown at pretest 20% rated known at pretest Treatment 3 readings Av. 3 hrs / reading (3167 wds/hr)

J’s reading of Boule de Suif

       

J

– motivated adult intermediate learner Boule de Suif 13,400 words 400 unique targets (1:33) 45% rated unknown at pretest 27% rated known at pretest Treatment 3 readings Av. 4.6 hrs/reading (2913 wds/hr)

R’s German novella vs. J’s Boule de Suif

       

R

– motivated adult intermediate learner German novella 9500 words 300 unique targets (1:32) 45% rated unknown at pretest 20% rated known at pretest Treatment 3 readings Av. 3 hrs / reading (3167 wds/hr)        

J

– motivated adult intermediate learner Boule de Suif 13,400 words 400 unique targets (1:33) 45% rated unknown at pretest 27% rated known at pretest Treatment 3 readings Av. 4.6 hrs/reading (2913 wds/hr)

Rating scale used at end of each reading

 0 = I don't know what this word means  1 = I am not sure what this word means  2 = I think I know what this word means  3 = I definitely know what this word means (Underlining added) Non-binary measure, Horst & Meara, 1999

Results

J’s word knowledge ratings before reading and after each of three readings (resource assisted)

0 (unknown) 1,2 (unsure) 3 (known) Pretest 180 wds 142 wds 78 wds Posttest 1 74 189 137 Posttest 2 49 165 186 Posttest 3 28 170 202

Summary: Unknown reduced from 180 to 128 Known increased from 78 to 202

Comparison to baseline

Results for R (

unassisted

) n=300 words Results for J (

R-READ

) n=400 words 0 (not known) 1 or 2 (unsure) 3 (known) Pretest 45% 28% 27% 3rd posttest 38 33 29 Pretest 45 36 20 3rd posttest 7 43 51

Percentage of targets in each category at outset and after three readings, unassisted and assisted

Comparison to baseline

Results for R (unassisted) n=300 words Results for J (R-READ) n=400 words 0 (not known) 1 or 2 (unsure) 3 (known) Pretest 45% 28% 3rd posttest 38 33 Pretest 45 36 3rd posttest 7 43 27%

29

20

51 R’s results typical of many acquisition-from-reading studies;J 250% greater in ‘known’ category.

Self-assessment check

 J (after 3 readings) and R (after 10 readings) asked for translations of words judged known  Js responses 94% accurate (Three readings with R-READ)   Rs responses 77% accurate (10 unassisted readings)

Conclusion (1)

 This is only a pilot study  Suggests significant learning increase for minor time increase  These are learning figures seen in previous research only for tiny word sets via ‘rich’ instruction (Beck, McKeown… 1982)

Conclusion (2)

 Suggests viablity of middle-way model of acquisition-through reading  Suggests that low-cost language consultants can be brought into wide-spread use

Conclusion (3)

J. B. Carroll (1964) expressed a wish that a way could be found to mimic the effects of natural contextual learning, except more efficiently....

 Maybe this ancient educational cul de-sac can be solved through the principled application of computer technology – how many others?

Acknowledgements This Web page incorporates the labours of many: The roman 'Boule de Suif'

Guy de Maupassant (1870)

Concordance program, true click-on hypertext

Chris Greaves, Virtual Language Centre, Polytechnic University, Hong Kong

French-English Dictionary

Neil Coffey http://www.french-linguistics.co.uk/dictionary/

Complete Corpus of de Maupassant oeuvre

Thierry de Selva, Laboratoire d'Informatique, Université de Franche-Compté, Besançon

Read-aloud of 'Boule de Suif'

Dominique Daguier, for «Le livre qui parle»

Perl scripting for User Lexicon

Mutassem Abdulahab & Monet, EZScripting.

Web formatting of 'Boule de Suif'

Carole Netter, Clicnet , Swarthmore College.

Historical Background

Luc et Eric Dodument, Skylink , Hombourg, Belgium.

Movie poster

http://perso.wanadoo.fr/lester/fifiaffiche.htm

Frequency List

Association des Bibliophiles Universels (ABU), De Maupassant, CEDRIC/CNAM, Paris