The Efficacy of Minds in Motion on Children’s Development of Executive Function,
Visuo-spatial and Math Skills
David Grissmer1, Andrew Mashburn2, Elizabeth Cottone1, Laura Brock3,
William Murrah1, Julie Blodgett1, Claire Cameron1
1
2
University of Virginia
Portland State University
3
College of Charleston
SREE Fall 2013 Conference Abstract Template
Abstract Body
Limit 4 pages single-spaced.
Background / Context:
Description of prior research and its intellectual context.
This paper presents the result from the randomized controlled trial of the Minds In
Motion intervention described in the previous paper.
Purpose / Objective / Research Question / Focus of Study:
Description of the focus of the research.
The primary research question that is addressed in this paper is: To what extent does MIM
impact children’s development of visuospatial processing, executive functioning, sensorimotor
processing, and math skills?
Setting:
Description of the research location.
See Paper 3 in the Symposium
Population / Participants / Subjects:
Description of the participants in the study: who, how many, key features, or characteristics.
See Paper 3 in the Symposium
Intervention / Program / Practice:
Description of the intervention, program, or practice, including details of administration and
duration.
See Paper 3 in the Symposium
Research Design:
Description of the research design.
The study utilized an experimental design in which 87 kindergartners and 1st graders attending
the WINGS afterschool program in three elementary schools during the 2010-2011 school year
were randomly assigned to one of two study conditions: 1) the Minds in Motion (MIM)
Curriculum, in which children participated in MIM intervention activities in small groups, and
2) a Control condition in which children participated in their regular after-school WINGS
Choice Time activities. Example Choice Time activities included theater, soccer, Girl Scouts,
and cooking, and their activities would be typical of students not attending WINGS.
Assignments to study condition used a within-school and within-grade block design, such that
randomization occurred separately within the three schools and within the two recruited grades
(kindergarten and 1st grade). Random assignment resulted in 45children in the MIM condition
and 42 children in the control condition.
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We hypothesize that MIM will have direct, positive impacts on children’s development of each
skill area during the school year.
Data Collection and Analysis:
Description of the methods for collecting and analyzing data.
See Paper 3 in the Symposium
Findings / Results:
Description of the main findings with specific details.
Pretest, posttest, and gain scores on each measure for MIM and control groups are presented and
Table 1, and there were no significant differences across the study conditions at pretest. Table 2
summarizes the results testing impacts of MIM using gain scores (pretest minus posttest) as the
outcome, such that:
Gi = a + b*Ti
j * zij +€I
where Gi is the score gain for student i, Ti is a binary variable equaling 1 for treatment and 0 for
control children. zij is the value of covariate j for student i, €i is the assumed normally distributed
error. a, b, and cj are estimated regression coefficients. Covariates included gender, race, and
grade. Estimates from an alternative specification in which posttest scores are expressed as a
function of pretest scores, covariates and group assignment is shown in Table 3. The results
presented in Table 2 and 3 show impact estimates that are nearly identical in magnitude.
Figure 1 summarizes the results using the gain score method from Table 2. Results show
statistically significant gains for the composite EF (effect size = .73, p < .001) and composite
visuo-spatial measure (effect size = .55, p < .01). Effect size estimates were computed by
dividing the size of the treatment effect divided by the pooled standard deviation for the
treatment and control groups. There were significant impacts of MIM on two EF sub-tests—
visual attention (effect size = .71, p < .01), and auditory attention (effect size = .61, p < .01).—
and on one visuo-spatial subtest design copying (effect size = .54, p < .05). For EF and visuospatial skills, no gender or grade interactions were significant.
As a follow up analyses, we estimated impacts of MIM separately for kindergartners and
1st graders, and results showed a significant positive effect of MIM on math outcomes for 1st
graders. Table 4 presents results from analysis using the gain score method for 1st graders.
Figure 2 summarizes the 1st grade results for five math measures: Woodcock- Johnson Applied
Problems (effect size = .56, p < .05), TEMA (effect size = .12, NS), KEYMATH3-Numeration
(effect size = .54, p < .01), KEYMATH3-Measurement (effect size = -.03, NS), and
KEYMATH3-Geometry (effect size = -.13, NS). Sensitivity analysis showed that none of the
significant effects were unusually sensitive to outliers.
In sum, children in our sample had initial skills that, on average, placed them between the
th
20 and 33th percentile nationally. The effect of the curriculum moved the sample of children
from around the 27th percentile to the 51st percentile on EF skills. Children’s visuo-spatial skills
were lifted from around the 33rd percentile to the 47th percentile, while the scores for 1st graders
on the Woodcock-Johnson Applied Problems and KEYMATH3-Numeration moved from around
the 32rd to 48th percentile.
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Conclusions:
Description of conclusions, recommendations, and limitations based on findings.
The impacts of MIM are practically significant on several levels. First, we have
demonstrated that a structured play-based, hands-on intervention conducted in after-school time
can increase children’s mathematics achievement. This is particularly valuable because it
indicates we can utilize time external to the school day to implement activities to improve
performance in an important academic domain without explicit mathematics instruction. Results
also confirmed that visuospatial skills, which have been linked to mathematics achievement
(Dehaene, 2011), can be improved using this intervention. Similarly, we have shown that the
intervention improves children’s EF. EF has been the target of numerous other interventions
(e.g., Tools of the Mind; (Diamond & Lee, 2011)), but ours may be unique in that it achieved
such a large effect size with EF as only one of several targeted skill areas. Because EF has been
linked to a wide range of children’s cognitive and academic outcomes from early childhood
through adolescence (Best, Miller, & Naglieri, 2011); (Vitaro, Brendgen, Larose, & Tremblay,
2005), the MIM intervention may also have far-reaching long-term impacts.
A few key contextual factors may have contributed to the success of the intervention in
improving these cognitive and achievement outcomes. First, the vast majority of study
participants were socio-demographically disadvantaged with low initial skill levels.
Approximately 90% of the children were Black, and almost all were eligible for free or reducedpriced lunches. Children in our study scored between the 20th and 33rd percentile on preintervention measures of visuospatial, EF, and sensorimotor processing, as well as mathematics.
Diamond and Lee (2011) found that EF intervention effects are often larger for children in higher
risk groups, and this may be the case for other skills as well.
Second, the dosage of intervention we were able to provide was very high. The 45-min-aday, 4-days-a-week, 28-week structure of the program allowed ample access to the participants,
and the rigorous attendance requirements of the WINGS program assured that children were
present at least 85% of the time. Additionally, the play-based nature of the intervention activities
likely contributed to increasing children’s engagement and time spent on task. Appropriate playbased activities can be intrinsically motivating for children, which leads to higher engagement
levels (Hirsh-Pasek, Golinkoff, Berk, & Singer, 2009). Lastly, the design of the intervention,
using activities that were variable in form (e.g., diverse, colorful, novel materials) but regular in
function or intent (e.g., design copy) and increasing in difficulty, allowed for both sustained
interest and consistent practice with a certain set of skill
The evidence from this and other studies suggests that incorporating certain types of play
into pre-school math curricula, the general pre-school curricula, or out of school programs can
significantly boost math skills for disadvantaged children. The study results here introduce the
possibility that one link between play and math may be through EF and visuo-spatial skills, and
the deficits in these skills for disadvantaged children may contribute to achievement score gaps.
These deficits may also explain the lack of progress in closing math achievement gaps from 25
years of K-12 school reform focusing on improving math instruction. Traditional direct math
instruction alone, no matter how early or intense its quality, may be unable to build these
foundational skills. However, once skill deficits are narrowed, our results would suggest that K12 school reform policies might be more effective because more children have the foundational
skills that facilitate learning math.
Building math skills by utilizing structured play overcomes a main challenge posed by
traditional direct math instruction in early schooling: sustaining a child’s engagement. Play,
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almost by definition, is an intrinsically motivated activity that can continuously engage and
sustain children’s attention. Taking advantage of structured, sustained play, whether during
school or out of school or embedded in math curricula, appears to be an important component in
improving math skills for disadvantaged children. Their lack of opportunity to engage in certain
kinds of play may be a major factor in lagging early math skills.
Math gaps may be caused as much by differences in what children experience before
school entry and experiences outside school rather than inside the K-12 school system. While
disadvantaged children have substantial math gaps at school entry, gains in math for advantaged
and disadvantaged children are similar during kindergarten and 1st grade, but math gaps increase
during summertime between kindergarten and 1st grade (Burkam, Ready, Lee, & LoGerfo,
2004). Almost all previous approaches to improve foundational or math skills for disadvantaged
children have focused on the time in pre-school or school. This study opens up intriguing out of
school possibilities such as incorporating structured play-based activities into after-school and
summertime programs and encouraging parents to engage in specific play activities with
children. Parents have responded to research and widespread publicity over the last 20 years
about the value of reading more to their children. Playing certain kinds of structured games with
children may be as important for building math skills as reading to children is for building
literacy skills. After school and summertime programs do not face the same bureaucratic
challenges that exist for implementing in-school interventions, and may be an important
supplementary strategy for closing math achievement gaps that is likely cost-effective because of
lower costs of instruction.
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Appendices
Not included in page count.
Appendix A. References
References are to be in APA version 6 format.
Best, J. R., Miller, P. H., & Naglieri, J. A. (2011). Relations between executive function and
academic achievement from ages 5 to 17 in a large, representative national sample. Learning and
Individual Differences, 21(4), 327-336. doi:10.1016/j.lindif.2011.01.007
Burkam, D. T., Ready, D. D., Lee, V. E., & LoGerfo, L. F. (2004). Social-class differences in
summer learning between kindergarten and first grade: Model specification and estimation.
Sociology of Education, 77(1), 1-31.
Dehaene, S. (2011). The number sense: How the mind creates mathematics (Revised and updated
edition ed.). New York, NY,: Oxford University Press.
Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in
children 4 to 12 years old. Science, 333(6045), 959-964. doi:10.1126/science.1204529
Hirsh-Pasek, K., Golinkoff, R. M., Berk, L. E., & Singer, D. G. (2009). A mandate for playful
learning in preschool: Presenting the evidence. New York, NY, US: Oxford University
Press.
Vitaro, F., Brendgen, M., Larose, S., & Tremblay, R. E. (2005). Kindergarten disruptive
behaviors, protective factors, and educational achievement by early adulthood. Journal of
Educational Psychology, 97(4), 617-629. doi:10.1037/0022-0663.97.4.617
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Appendix B. Tables and Figures
Table 1.
Comparison of Pretest and Posttest Measures, Gain Scores, and Effect Sizes
Pretest
Posttest
Outcome
T1
C
T
EF
Visuospatial
Visual Attention
Auditory Attention
Tower
Arrows
Design Copying
Applied Problems
TEMA
Numeracy
Measurement
Geometry
81.15
91.67
7.67
8.00
6.86
8.38
8.88
89.98
87.57
7.60
7.10
8.43
87.15
94.32
8.44
8.83
7.63
9.10
9.05
92.39
88.32
8.12
7.83
8.27
100.29
99.14
10.64
10.12
9.33
9.88
9.83
96.02
95.95
8.90
7.71
9.43
1
2
3
T = MIM group, C = control group
s.d. = pooled standard deviation
Effect Size = (Tgain - Cgain) / s.d.
C
Gain
T
96.22 19.14
95.07 7.48
9.66
2.98
9.68
2.12
9.15
2.48
9.39
1.50
9.00
0.95
96.00 6.05
95.20 8.38
8.63
1.31
8.44
0.62
9.51
1.00
C
9.07
0.76
1.22
0.85
1.51
0.29
-0.05
3.61
6.88
0.51
0.61
1.24
s.d.2 Effect Size3
14.69
11.96
2.51
2.56
2.78
2.15
2.70
11.45
14.56
3.00
2.37
2.60
0.69
0.56
0.70
0.49
0.35
0.56
0.37
0.21
0.10
0.27
0.00
-0.09
Table 2.
Regression Estimates of Treatment Effects using Gain Scores
EF1 VS
Intercept
treatment
grade
male
race:White
race:Other
R2
Adj. R2
*** p
1
Vis. Attn. Aud. Attn. Tower Arrows Design Copy Applied Prob. TEMA Num.
Meas.
Geom.
0.79
(0.18)
0.73
(0.18)
0.14
(0.18)
0.34†
(0.18)
0.50
(0.38)
0.59
(0.38)
0.08
(0.18)
0.55
(0.19)
0.07
(0.19)
0.07
(0.19)
0.13
(0.40)
0.25
(0.40)
0.42
(0.26)
0.71
(0.26)
0.25
(0.26)
0.10
(0.26)
0.26
(0.55)
0.12
(0.55)
0.83
(0.20)
0.61
(0.21)
0.56
(0.21)
0.53
(0.21)
0.43
(0.44)
0.08
(0.43)
0.55
(0.21)
0.33
(0.21)
0.04
(0.21)
0.16
(0.21)
0.86†
(0.45)
0.96
(0.44)
0.09
(0.19)
0.37†
(0.19)
0.01
(0.19)
0.15
(0.19)
0.13
(0.40)
0.51
(0.40)
0.02
(0.23)
0.54
(0.24)
0.12
(0.24)
0.31
(0.24)
0.07
(0.50)
0.23
(0.50)
0.01
(0.15)
0.29†
(0.15)
0.37
(0.15)
0.10
(0.15)
0.75
(0.32)
0.46
(0.32)
0.70
(0.15)
0.10
(0.16)
0.53
(0.16)
0.16
(0.15)
0.04
(0.33)
0.01
(0.32)
0.15
(0.19)
0.34†
(0.20)
0.19
(0.20)
0.25
(0.20)
0.61
(0.42)
0.91
(0.42)
0.12
(0.19)
0.04
(0.20)
0.17
(0.20)
0.05
(0.20)
0.43
(0.42)
0.32
(0.42)
0.27
(0.18)
0.08
(0.19)
0.21
(0.19)
0.17
(0.19)
0.52
(0.40)
0.57
(0.39)
0.23
0.18
0.11
0.06
0.10
0.04
0.22
0.16
0.13
0.07
0.08
0.02
0.09
0.03
0.19
0.13
0.15
0.10
0.13
0.08
0.03
0.03
0.08
0.02
< 0.001, ** p < 0.01, * p < 0.05,
†p
< 0.1
EF = Executive Functions, VS = Visuospatial, Vis. Attn. = Visual Attention, Aud. Attn. = Auditory Attention, Applied Prob. = Woodcock-Johnson
Applied Problems, TEMA = Test of Early Mathematics Ability, Num. = KeyMath Numeration, Meas. = KeyMath Measurement, Geom. = KeyMath
Geometry
Table 3.
Regression Estimates of Treatment Effects for Outcomes Controlling for Pretest Scores.
EF
Intercept
treatment
grade
male
race:White
race:Other
pretest
R2
Adj. R2
Num. obs.
*** p
1
1
VS
Vis. Attn.
Aud. Attn.
Tower
Arrows
Design Copy
Applied Prob.
†
TEMA
Num.
Meas.
Geom.
0.14
(0.16)
0.60
(0.17)
0.08
(0.16)
0.29†
(0.16)
0.30
(0.35)
0.50
(0.34)
0.74
(0.08)
0.19
(0.17)
0.46
(0.17)
0.13
(0.17)
0.06
(0.17)
0.10
(0.36)
0.31
(0.35)
0.65
(0.08)
0.23
(0.20)
0.50
(0.20)
0.14
(0.20)
0.14
(0.20)
0.57
(0.43)
0.18
(0.42)
0.43
(0.10)
0.18
(0.19)
0.48
(0.20)
0.29
(0.21)
0.45
(0.20)
0.04
(0.43)
0.10
(0.42)
0.49
(0.11)
0.14
(0.17)
0.23
(0.17)
0.00
(0.17)
0.12
(0.17)
0.64†
(0.37)
0.74
(0.36)
0.66
(0.09)
0.02
(0.17)
0.36
(0.17)
0.13
(0.17)
0.19
(0.17)
0.08
(0.36)
0.51
(0.35)
0.62
(0.09)
8.91
(0.42)
0.71
(0.44)
0.18
(0.44)
0.83†
(0.43)
0.19
(0.91)
0.04
(0.91)
0.92
(0.22)
0.29
(0.15)
0.26†
(0.15)
0.26†
(0.16)
0.06
(0.15)
0.61†
(0.32)
0.46
(0.31)
0.81
(0.08)
0.30
(0.14)
0.11
(0.15)
0.69
(0.15)
0.01
(0.15)
0.05
(0.31)
0.07
(0.30)
0.69
(0.07)
0.03
(0.19)
0.26
(0.19)
0.02
(0.20)
0.23
(0.19)
0.19
(0.42)
0.71†
(0.41)
0.56
(0.10)
0.10
(0.19)
0.20
(0.19)
0.08
(0.19)
0.14
(0.19)
0.45
(0.40)
0.06
(0.40)
0.55
(0.10)
0.04
(0.19)
0.05
(0.19)
0.15
(0.20)
0.06
(0.19)
0.02
(0.41)
0.47
(0.39)
0.58
(0.10)
0.53
0.49
82
0.47
0.43
83
0.26
0.20
83
0.26
0.20
82
0.45
0.41
83
0.47
0.43
83
0.24
0.18
83
0.60
0.56
83
0.61
0.58
83
0.31
0.26
83
0.35
0.30
83
0.35
0.30
83
< 0.001, ** p < 0.01, * p < 0.05,
†p
< 0.1
EF = Executive Functions, VS = Visuospatial, Vis. Attn. = Visual Attention, Aud. Attn. = Auditory Attention, Applied Prob. = Woodcock-Johnson
Applied Problems, TEMA = Test of Early Mathematics Ability, Num. = KeyMath Numeration, Meas. = KeyMath Measurement, Geom. = KeyMath
Geometry.
Table 4.
Regression Estimates of Treatment Effects using Gain Scores for First Graders Only
EF
Intercept
treatment
male
race:White
race:Other
R2
Adj. R2
Num. obs.
*** p
1
1
VS
Vis. Attn.
Aud. Attn.
Tower
†
Arrows
Design Copy
Applied Prob.
TEMA
Num.
Meas.
Geom.
0.67
(0.25)
0.76
(0.28)
0.37
(0.27)
0.09
(0.47)
0.77
(0.47)
0.12
(0.20)
0.45
(0.22)
0.04
(0.22)
0.07
(0.37)
0.24
(0.37)
0.78
(0.33)
0.82
(0.36)
0.39
(0.36)
0.70
(0.62)
0.31
(0.62)
0.12
(0.26)
0.68
(0.28)
0.28
(0.28)
0.22
(0.48)
0.14
(0.48)
0.60
(0.31)
0.24
(0.34)
0.21
(0.33)
0.62
(0.58)
1.06†
(0.57)
0.14
(0.22)
0.27
(0.24)
0.18
(0.23)
0.17
(0.40)
0.39
(0.40)
0.03
(0.25)
0.50†
(0.27)
0.16
(0.27)
0.10
(0.46)
0.11
(0.46)
0.28
(0.21)
0.56
(0.23)
0.02
(0.23)
0.79†
(0.39)
0.61
(0.39)
0.12
(0.14)
0.12
(0.16)
0.17
(0.16)
0.02
(0.27)
0.19
(0.27)
0.18
(0.18)
0.54
(0.19)
0.17
(0.19)
0.72
(0.33)
1.01
(0.33)
0.27
(0.22)
0.03
(0.24)
0.07
(0.24)
0.48
(0.41)
0.34
(0.40)
0.40
(0.27)
0.13
(0.29)
0.28
(0.29)
0.08
(0.49)
0.65
(0.49)
0.21
0.13
43
0.13
0.04
43
0.14
0.05
43
0.16
0.07
43
0.12
0.03
43
0.09
0.00
43
0.10
0.00
43
0.22
0.14
43
0.06
0.04
43
0.33
0.26
43
0.05
0.05
43
0.08
0.02
43
< 0.001, ** p < 0.01, * p < 0.05,
†p
< 0.1
EF = Executive Functions, VS = Visuospatial, Vis. Attn. = Visual Attention, Aud. Attn. = Auditory Attention, Applied Prob. = Woodcock-Johnson Applied
Problems, TEMA = Test of Early Mathematics Ability, Num. = KeyMath Numeration, Meas. = KeyMath Measurement, Geom. = KeyMath Geometry
Figure 1. Comparison of Visuo-spatial and EF gains
Figure 2. Comparison of Math gains
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