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Translating Data Driven Language Learning into French
Tom CobbDép. 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 shortDatabase is dispersedMuch 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 & compressionRapid 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 levelMake yourself a dictionary at
the level where you are weakExample: 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
Reading Extended Authentic Documents with Resources
…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 scaleused 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
Pretest Posttest 1 Posttest 2 Posttest 3
0 (unknown) 180 wds 74 49 28
1,2 (unsure) 142 wds 189 165 170
3 (known) 78 wds 137 186 202
J’s word knowledge ratings before reading and after each of three readings (resource assisted)
Summary: Unknown reduced from 180 to 128Known increased from 78 to 202
Comparison to baseline
Results for R (unassisted)n=300 words
Results for J (R-READ)
n=400 words
Pretest 3rd posttest
Pretest 3rd posttest
0 (not known)
45% 38 45 7
1 or 2 (unsure)
28% 33 36 43
3 (known) 27% 29 20 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
Pretest 3rd posttest
Pretest 3rd posttest
0 (not known)
45% 38 45 7
1 or 2 (unsure)
28% 33 36 43
3 (known) 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
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