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Modeling Reading Development From First Grade Text

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Title: Modeling Reading Development From First Grade Text


1
Modeling Reading Development From First Grade Text
  • Michael W. Harm, CMU
  • Mark S. Seidenberg, Wisconsin/Madison

2
Why Construct Computational Models?
  • Can complement empirical studies
  • Allow manipulation of multiple factors that would
    be prohibitively difficult in a classroom setting
  • We can look under the hood and see what kinds
    of internal representations are formed during
    learning

3
Computational Models of Reading
  • Have a long history
  • Rumelhart McClelland 1981, Seidenberg
    McClelland 1989, Plaut Hinton, 1991, Coltheart
    et. al, 1993, Plaut et. al 1996, Zorzi 1999, etc.
  • Historically, have been aimed at explaining
    adult performance
  • Architecture of the reading system
  • Acquired dyslexia (due to brain damage)
  • Have traditionally not examined issues of reading
    development

4
Modeling Development Was Difficult With Earlier
Models
  • Earlier models did not learn about phonology
  • But phonology is important in learning to read!
  • Sampled large corpus of (adult) text in toto
  • Did not allow for manipulation of actual sequence
    or nature of word exposures
  • Prohibited examination of effects of different
    basals, interventions

5
The Harm Seidenberg 1999 Model of Reading
Begin by modeling pre- literate phonological
knowledge that children have
Can vary the strength and consistency of this
knowledge
and simulate the different degrees of
phonological ability children bring to bear
learning to read
6
Reading Uses this Phonological Knowledge
The nature of the phono representations
influences what is learned during reading
Core result the phonologically impaired model
learns differently
7
Two New Applications
We have applied the Harm Seidenberg 1999 model
to two novel applications
  • Simulation of effects of different instructional
    basals
  • Simulation of an reading intervention scheme

8
Simulating Effect of Different Reading Basals
  • Some early reading texts are more tuned to
    overlap in spelling/sound
  • Others emphasize variety in text exposure to
    wider range of words

Q How does this interact with reading
impairments?
9
Results Nonword Reading
  • Basal 1 More tuned to spelling/sound
    correspondences
  • Basal 2 Less systematic text
  • For both normal and impaired model, Basal 1
    better than Basal 2.

10
Results Word Reading
  • For normal models, large effect of basal
  • For impaired models, floor effect smaller
    difference

11
Summary Simulating Effects of Basals
  • We can explore the impact of reading materials,
    and differential effects on normal and impaired
    reading
  • Holds promise for more sophisticated
    explorations/manipulations
  • and direct ties to more fine grained properties
    of basals (see other talks in this session)

12
Simulating Reading Interventions
  • There is extensive evidence that one cause of
    poor reading development is a phonological
    impairment
  • However, interventions targeted at auditory
    phonology generally are not very effective
  • Interventions aimed at spelling/sound
    representations have greater success

Why?
13
Analysis of the Model
Phonology
Spelling
So effective interventions must target the
relationship between spelling and sound
14
The McCandliss et al. Intervention
  • Use lessons based on the Beck word-building
    scheme
  • Break words apart when errors are made
  • Emphasizes componential structure of words
  • and its relation to components of sound

15
Performed Simulation of this Intervention
  • Used phonologically impaired simulation from Harm
    Seidenberg 1999
  • Simulated intervention using actual items from
    lessons
  • Brought about improvements in nonword reading
  • Analyzed internal representations of words

16
Normal and Impaired Simulation
17
Representations Cluster Better Due to Intervention
Remediated Simulation
18
Conclusions
  • Computational simulations are now poised to
    explore more detailed aspects of childrens
    experience
  • With reading basals
  • And with interventions
  • Opens up a promising new line of research linking
    behavioral experimentation with computational
    analysis

19
With thanks to...
Mark S. Seidenberg Bruce McCandliss
20
fin
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