working memory deficits as they relate to academic growth of students with rd
DESCRIPTION
Working Memory Deficits as They Relate to Academic Growth of Students with RD. Olga Jerman, Ph.D. Director of Research Frostig Center, Pasadena, CA Minyi Shih , Ph.D. California State University, Los Angeles. PCRC 2010 San Diego, CA. Frostig Center. Abstract. - PowerPoint PPT PresentationTRANSCRIPT
Working Memory Deficits as They Relate to Academic Growth of Students with RD
Olga Jerman, Ph.D.Director of ResearchFrostig Center, Pasadena, CA
Minyi Shih, Ph.D.California State University, Los Angeles
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PCRC 2010San Diego, CA
AbstractThe study investigated whether (a) growth patterns related to cognitive processing (working memory, updating, inhibition) differed in subgroups of children with reading disabilities (RD), and (b) if growth in WM (executive processing) predicted growth in other cognitive areas, such as reading and math. 81 children (ages 7 to 17) categorized as poor decoders, poor comprehenders, or average readers were administered a battery of achievement and cognitive measures for three consecutive years. HLM showed that growth in executive processing (inhibition) in children with RD constrained growth in reading and math. The results support the notion that development in the executive system underlies performance on reading and math measures.
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Working Memory & Reading Working Memory is:
A system that simultaneously processes and stores information for a brief period of time
Responsible for a range of cognitive functions, such as maintaining attention, inhibiting irrelevant information, switching between different stimuli, and updating.
There are different models of working memory and different tasks to assess working memory
Dyslexia
Average intelligence; Below average score on standardized reading
measures; Scores on math tasks within average range.
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Baddeley’s model of WM
Central Executive
VisuospatialSketchpad Episodic
Buffer
PhonologicalLoop
Visual Semantics LTM Language
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Research Questions:
1. Does growth in the executive components of WM or WM span differ as a function of reading ability?
2. Do deficits in the executive components of WM (or WM span) constrain RD students’ growth on measures related to crystallized intelligence (reading and math)?
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Methods
Participants: 73 students
Gender: 24 girls and 49 boys
Mean age 12.20; range 7.8 – 17.0
SES: middle-upper to upper class
Ethnicity: 57 Caucasian; 6 African-
American; 5 Hispanic; 5 other
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Subgroups
1. Poor decoders (PD); N = 25
Reading fluency (WRAT-3) < 25th percentile
(90 SS)
Math (WRAT-3) > 80 SS
2. Poor comprehenders (PC); N = 23
Reading fluency (WRAT-3) > 25th percentile
Comprehension (WRMT) < 25th percentile
Math (WRAT-3) > 80 SS
3. Control group (C); N = 25
Reading fluency (WRAT-3) > 40th percentile
Comprehension (WRMT) > 40th percentile
Math (WRAT-3) > 80 SS
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Does Growth in WM Differ As a Function of Reading Ability?
WM growth rates among Poor decoders,
Poor comprehenders, and Average
readers are comparable for the younger
students
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Results of Multilevel Modelsfor Change in Sentence Span
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Parameter Unconditional means
Unconditional growth
Conditional means
Conditional growth
Fixed Effects
Initial status, π0i
Intercept γ00 1.3439*** 1.3027*** 1.4469*** 1.4498***
Group 1 (PD)
γ01
-0.1091 -0.1208
Group 2 (PC)
γ02
-0.3832* -0.3817*
Rate of change, π2i
Slope γ10
0.0104*** 0.0108*** 0.0137**
Group 1 (PD)
γ11
-0.0054
Group 2 (PC)
γ12
-0.0029
Variance components Goodness-of-fit
statistics
-2ln(L) 397.9 377.1 371.5 370.6
AIC 403.9 389.1 387.5 390.6
BIC 410.9 402.8 405.8 413.5
DC
0
1
2
3
4
AGE
90 114 138 162 186 210
Students’ Performance on Sentence Span
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Note: Age is given in months, WM performance presented in span scores
Younger and Older Students Across3 Waves on Sentence Span
Sentence span
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6
z-s
co
res
PD
PC
Control
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Do Deficits in WM Span or Executive System Constrain Growth on Measures Related to Reading and Math?
Growth in WM span did not explain any additional variance in students’ reading and math performance and did not account for the growth in these areas.
On the other hand, measures of Executive processing were found to have an important influence in children’s growth in math and reading.
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Growth Models for Reading(TOWRE Real Word and Non-word Reading)
Fixed Effects Parameter Unconditional
means Unconditional
growth Conditional
means Conditional
growth
Initial status, π0i
Intercept γ00 0.3865*** 0.3181*** 0.9380*** 0.9367***
Group 1 (PD)
γ01 -1.4966*** -1.5016***
Group 2 (PC)
γ02 -0.4577* -0.4423*
DC γ03 0.3472 p=.64
Rate of change, π2i
Slope γ10 0.01557*** 0.01268*** 0.01254***
Group 1 (PD)
γ11
Group 2 (PC)
γ12
DC γ13 -9.0633 p=.66
Variance components
Goodness-of-fit statistics
-2ln(L) 332.0 286.1 242.0 241.7
AIC 338.0 298.1 258.0 261.7
BIC 344.9 311.9 276.3 284.6
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Growth Models for Math(WISC-R Arithmetic Subtest)
Fixed Effects Parameter Unconditional means
Unconditional growth
Conditional means
Conditional growth
Initial status, π0i
Intercept γ00 0.4733*** 0.3344** 0.8151*** 0.8213***
Gr 1 (PD) γ01 -0.7582*** -0.778***
Gr 2 (PC) γ02 -0.7232*** -0.6878***
DC γ03 1.3970; p=.108
Rate of change, π2i
Slope γ10 0.0171*** 0.0174*** 0.0167***
Gr 1 (PD) γ11
Gr 2 (PC) γ12
DC γ13 -37.9112 p=.128
Variance components Goodness-of-fit statistics
-2ln(L) 449.2 409.0 394.4 391.8
AIC 455.2 421.0 410.4 411.8
BIC 462.2 434.8 428.7 434.7
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Growth Models with Executive Activity (EA) as a Predictor of Math
Fixed Effects Parameter Based on Conditional means models for math
Initial status, π0i
Intercept γ00 0.7433*** 0.7481*** 0.6897*** 0.6767***
Gr 1 (PD) γ01 -0.5863** -0.5913** -0.5406** -0.5572**
Gr 2 (PC) γ02 -0.6804** -0.65** -0.5794** -0.5902**
EA γ03 0.1927** 1.1119** 1.1933** 1.9283***
EA*Gr1 γ04 -0.1786 -2.685*
EA*Gr2 γ05 -0.2662*
-1.5262 p=.085
Rate of change, π2i
Slope γ10 0.0166*** 0.0159*** 0.0157*** 0.0167***
Gr 1 (PD) γ11
Gr 2 (PC) γ12
EA γ13 -80.1154* -74.7292* -140.36**
EA*Gr1 γ14 212.01*
EA*Gr2 γ15 111.11 p=.15
Variance components Goodness-of-fit statistics
-2ln(L) 385.5 380.1 375.8 370.2
AIC 403.5 400.1 339.8 398.2
BIC 424.1 423.0 427.3 430.2
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Growth Models with Executive Activity (EA) as a Predictor of Reading
Fixed Effects parameter Based on Conditional means models for Reading
Initial status, π0i
Intercept γ00 .8839*** .8627*** .8642*** .9311***
Gr 1 (PD) γ01 -1.3721*** -1.3715*** -1.2673*** -1.2818***
Gr 2 (PC) γ02 -.4094* -.3823* -.3923* -.5464**
EA γ03 .1424* .3482 .4672 1.0123**
EA*gr1 γ04 .1597 -2.0265*
EA*gr2 γ05 -.0896 -1.6763* Rate of change, π2i
Slope γ10 .01214*** .01209*** .01267*** .01414***
Gr 1 (PD) γ11
Gr 2 (PC) γ12
EA γ13 -17.8989 -27.573 -82.3519**
EA*gr1 γ14 197.55**
EA*gr2 γ15 147.28*
Variance components Goodness-of-fit statistics
-2ln(L) 235.4 236.0 233.6 222.9
AIC 253.4 254.0 255.6 250.9
BIC 274.0 274.6 280.8 283.0
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Conclusion
Functions of the Executive system of WM,
specifically inhibition and/or updating of the
new information, contribute significantly to
students’ reading and math growth.
Students with RD show deficits in these
areas, which constrain their ability to learn
new material, comprehend written text, and
problem-solve.
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Implications Theoretical implications:
Findings suggest that specific aspects of the Central Executive, rather than general WM impairments, are deficient in RD students.
Functions of the Central Executive are critical for successful learning in school. Deficits in executive functions result in developmental lag in reading and math acquisition.
Practical implications: Modification of classroom instruction and curricular
materials: Minimize the amount of irrelevant info; reduce switching
between tasks & activities; slower pace of introducing new info (updating).
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