Simplifying reading: Implications for instruction

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Simplifying reading Implications for instruction

• Janet Vousden
• University of Warwick

Michelle Ellefson, Nick Chater, Jonathan Solity
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Overview

• English spelling-to-sound inconsistency and
  reading
• rational analysis of English reading
• applying the simplicity principle
• analysis of some common reading programmes

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Spelling-to-sound mappings

• spelling-to-sound mappings in English are not
  transparent at sub-lexical level

• some spellings are consistent
• ck duck - /d?k/, mock - /mok/, etc
• and a simple grapheme-phoneme rule will suffice
• ck - /k/

• others are not
• ea beach - /bi?t?/, real - /r??l/, great -
  /gre?t/, or head - /h?d/

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• most obvious at the grapheme level - ou
  grapheme is credited with having 10 different
  pronunciations (Gontijo, Gontijo, Shillcock,
  2003)

• e.g., round, group, should, four, country,
  tenuous, soul, journal, cough, pompous

• overall measure of (in)consistency in a language
  is its orthographic depth average number of
  pronunciations per grapheme

• for English, orthographic depth estimates
• 2.1 - 2.4 (Berndt, Reggia, Mitchum, 1987
  Gontijo, Gontijo, Shillcock, 2003) polysyllabic
  text
• 1.7 (Vousden, 2008) monosyllabic text

• compare e.g. Serbo-Croat which has OD of 1

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• how do literacy levels in English compare with
  other languages?

• can differences in consistency account for the
  difficulty in learning to read English?

• yes - inconsistency clearly increases difficulty
  of learning to read compared with more consistent
  languages (Frith, Wimmer Landerl, 1998)

• Data correct reading scores (adapted from
  Seymour, Aro, Erskine, 2003).

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• lag in performance persists through school years

Data non-word reading accuracy (reproduced from
Frith, Wimmer, Landerl, 1998)
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• Most often, vowel graphemes are inconsistent, but
  can use immediate context to resolve ambiguity
• C V C - C V or V C

• ambiguity can be resolved by considering the
  following consonant (a rime unit) rather than the
  previous consonant (Treiman et al., 1995)

• ea
• pronounced to rhyme with breath when followed by
  d 80
• pronounced to rhyme with meat when followed by
  p 100

• also, rime units are more consistent than
  graphemes
• 23 graphemes inconsistent
• 15 rimes inconsistent

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Choosing spelling-to-sound mappings

• influences from developmental literature (do
  rimes or gpcs predict reading ability?)

• variety of approaches from reading schemes
  (Rhymeworld, THRASS, etc)

• so many to choose from,
• 2000 rime mappings
• 300 grapheme mappings

• and many are inconsistent
• 15 rimes, 23 graphemes

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Rational analysis

• Attempt to explain behaviour in terms of
  adaptation to environment, independent of details
  of cognitive architecture

• Solution adopted by cognitive architecture should
  reflect structure of environment

• e.g., Anderson Schooler (1991) showed that the
  probability that a memory will be needed over
  time matches the availability of human memories
• same factors that predict memory performance also
  predict the odds that an item will be needed
• i.e. reliable effects of recency and frequency

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• factors that affect performance of skilled
  readers should be reflected in the statistical
  structure of the language, e.g. frequency and
  consistency

• effects of word frequency in naming and lexical
  decision
• effects of rime frequency on word-likeness
  judgements and pronunciation
• effects of grapheme frequency in letter search
  and word priming experiments

• by examining linguistic factors that skilled
  readers have adapted to, could the input be more
  optimally structured for learners?

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Analyses of spelling-to-sound mappings

• rational analysis predicts the most frequent and
  consistent mappings best predict pronunciation

• interested in the frequency consistency of
  mappings at level of words, rimes, and graphemes,
  and their ability to predict correct pronunciation

• CELEX database 7,297 different monosyllabic
  words, 10,924,491 words in total

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Words
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Onsets and rimes

• Exclude 100 most frequent words
• 7,197 diffrent words, total of 2,263,264 words

• Create table of onset and rime mapping
  frequencies, remove all but most frequent of
  inconsistent mappings

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Onsets
Rimes
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GPCs

• exclude 100 most frequent words
• 7197 diffrent words, total of 2,263,264 words

• create table of GPC mapping frequencies, remove
  all but most frequent of inconsistent mappings

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GPCs
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Summary

• some words much more frequent than others,
  therefore sight vocabulary very effective for
  small number of words, up to 100

• sub-lexical units also have skewed frequency
  distribution, and learning the most frequent
  mappings predicts high potential outcome

• high initial gains with GPCs, greater overall
  gain with rimes in the long run

• What is the optimal size unit to learn?

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• Potential benefits for reading outcome are larger
  for onset/rimes, but is this out-weighed by the
  cost of remembering many more mappings?

• Can we measure the potential benefit from, and
  cost of, remembering mappings for
• GPCs
• onset/rimes
• A combination of both ?

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The Simplicity Principle

• reading, like much high-level cognition, involves
  finding patterns in data, but many patterns are
  compatible with any finite set of data - so how
  does the cognitive system choose from the
  possibilities?

• Using the simplicity principle, choose the
  simplest explanation of the data - intuitively,
  has long history (Occams razor)

• can quantify simplicity by measuring (shortest)
  description from which data can be reconstructed
  - trade off brevity against goodness of fit
• cognition as compression

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• implement with minimum description length (MDL)
• more regularity more compression
• no regularity no compression, just reproduce
  data

• can measure compression with Shannons (1948)
  coding theorem - more probable events are
  assigned shorter code lengths
• length/bits log2(1/p)

• measure code length to specify
• hypothesis about data (mappings)
• data, given hypothesis (decoding accuracy, given
  mappings)

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Method

• determine mappings frequencies from
  monosyllabic corpus of childrens reading
  materials (Stuart et al., 2003), for mapping
  sizes
• words
• CV/C (head/coda)
• C/VC (onset/rime)
• GPCs

• determine code length to describe
• mappings
• decoding accuracy, given mappings
• for each mapping size

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Table 1. A list of reading schemes/series used by
over a third of schools in the survey Name of
scheme using scheme Included in database?
Ginn 360 74 Yes Storychest 58 Yes
Magic Circle 58 Yes 1 2 3 and Away 50
Yes Griffin Pirates 43 Yes Breakthrough to
Literacy 41 Bangers and Mash 40 Yes Wide
range readers 38 Yes Dragon Pirates 37
Yes Through the rainbow 34 Ladybird
read-it-yourself 33 Yes Humming birds 32
Thunder the dinosaur 29 Yes Link Up 29
Gay Way 27 Yes Monster 27 Yes Oxford
Reading Tree 27 Yes Once Upon a Time 26
Yes Trog 26
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Code length for mappings
length log2(1/p(w)) log2(1/p(i?))
log2(1/p(newline))
length log2(1/p(b)) log2(1/p(i))
log2(1/p(space)) log2(1/p(b)) log2(1/p(I))
log2(1/p(newline))
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Code length for decoding accuracy
apply letter-to-sound rules to produce a list of
pronunciations
bread breId brid br?d
arrange in rank order of most probable (computed
from letter-to-sound frequencies) note rank of
correct pronunciation
bread brid br?d breId
code length for data, given hypothesis
log2(1/p(rank2))
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Simulations

• overall comparison between different unit sizes
  for whole vocabulary

• how does code length vary as a function of size
  of vocabulary for each unit size?

• optimize number of mappings by removing those
  that reduce total code length

• compare different reading schemes

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Comparing different unit sizes for whole
vocabulary
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Code length as a function of vocabulary size
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Optimizing number of mappings
GPCs Description length reduced by removing
mainly inconsistent, low frequency mappings
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Comparing different reading schemes
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Decoding accuracy by scheme
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• ERR implemented as a reading intervention in 12
  Essex schools

Data from Shapiro Solity (2008)
increase in reading scores significantly greater
for ERR schools
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• Some conclusions

• small amount of sight vocabulary accounts for
  large proportion of text, but only small
  vocabularies most simply described by whole words
• Complements recent work by Treiman and colleagues
  that shows children learn better when association
  between sound and print is non-arbitrary

• As a homogenous set, GPCs provide a simpler
  explanation of the data

• choosing the best set could be important