Translating Data Driven Language Learning into French

1
Translating Data Driven Language Learning into
French

• Tom Cobb
• Dép. de Linguistique
• Université du Québec à Montréal

2
Peut-on augmenter le rythme dacquisition
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

3
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

4
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

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The idea of shortcuts to L2

• It has long been known that the time available
  for LL through experience is inadequate in most
  cases
• Learners time is short
• Database is dispersed
• Much time is needed to expose patterns in data

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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

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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

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

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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

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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

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Problems with list-driven learning

• Needed frequency information seems unavailable
  except in English
• List is not everyones cup of tea
• So, another idea
• Adapt computational tools to the less structured
  context of extensive reading

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Introducing R-READ Reading Extended Authentic
Documents with Resources of a kind that are
increasingly capable of Internet delivery
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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

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R-READ as a middle way

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

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Personal Anecdote

• Me, 1980, French reading test looming
• Method read one book, several times, aided by a
  language consultant
• Voltaires Candide
• Francophone girlfriend
• Look into every word deconstruct every structure
• Repeat pronunciations
• Stick-on concordances
• Little notebooks
• Stick-ons removed, fewer look-ups
• First Hurdle clear in about a week

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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?

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An electronic language consultant?
Go online
VLC
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User lexicon
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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)

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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)

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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)

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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

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Pilot Test of de Maupassants 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)

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Rs reading of German novella (Horst, 2000)

• R motivated adult intermediate learner
• German novella
• 9500 words
• 300 unique targets (132)
• 45 rated unknown at pretest
• 20 rated known at pretest
• Treatment 3 readings
• Av. 3 hrs / reading (3167 wds/hr)

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Js reading of Boule de Suif

• J motivated adult intermediate learner
• Boule de Suif
• 13,400 words
• 400 unique targets (133)
• 45 rated unknown at pretest
• 27 rated known at pretest
• Treatment 3 readings
• Av. 4.6 hrs/reading(2913 wds/hr)

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Rs German novella vs. Js Boule de Suif

• R motivated adult intermediate learner
• German novella
• 9500 words
• 300 unique targets (132)
• 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 (133)
• 45 rated unknown at pretest
• 27 rated known at pretest
• Treatment 3 readings
• Av. 4.6 hrs/reading(2913 wds/hr)

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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

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Results
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Js word knowledge ratings before reading and
after each of three readings (resource assisted)
Summary Unknown reduced from 180 to 128 Known
increased from 78 to 202
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Comparison to baseline
Percentage of targets in each category at outset
and after three readings, unassisted and
assisted
32
Comparison to baseline
Rs results typical of many acquisition-from-readi
ng studiesJ 250 greater in known category.
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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)

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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)

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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

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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?

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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/fifi
affiche.htm Frequency List Association des
Bibliophiles Universels (ABU), De Maupassant,
CEDRIC/CNAM, Paris