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Learning Potential in high IQ children: The contribution of dynamic assessment to
the identiﬁcation of gifted children
M. Dolores Calero ⁎, García-Martin M. Belen, M. Auxiliadora Robles
University of Granada, Spain
Received 13 September 2009
Received in revised form 24 November 2010
Accepted 30 November 2010
In recent years, models of giftedness have incorporated personal and social variables which inﬂuence IQ,
rather than taking IQ into account exclusively. Among the various options presented in this context, authors
have proposed dynamic assessment techniques as a method for revealing the potential capacity in different
groups, independently of the IQ they present. The aim of the present study was to investigate, in two samples
of Spanish children from the urban middle class previously identiﬁed as gifted and of normal intelligence,
three basic assumptions common to studies in this line of research: (1) that there are signiﬁcant differences in
Learning Potential between gifted children and children with average IQ; (2) that the differences are apparent
in diverse tasks, and (3) that Learning Potential signiﬁcantly predicts the high/average status of the subjects.
127 children from 6 to 11 years old (64 high-IQ and 63 average-IQ) were evaluated using different dynamic
tests. Signiﬁcant intergroup differences were obtained and the tests were shown to have high predictive
Published by Elsevier Inc.
When Terman introduced the concept of giftedness in 1916
(Terman, 1925), the criterion for its deﬁnition was purely normative
(IQ score), and this shaped the reductionist concept of high capacities
which, in practice, is still prevalent today (Borland, 2005). With
Sternberg and Gardner's contributions (Gardner, 1983; Sternberg,
1985), the most recent models of giftedness establish a network of
interrelations between different types of construct and modulating
variables (Coleman, 1995; Mönks & Katzko, 2005). Thus, Gagné
(2003), Sternberg's WICS model (Sternberg, 2005; Sternberg &
Grigorenko, 2002) and the MMG Munich Model of Giftedness (Heller,
Perleth, & Lim, 2005) conceive giftedness as a multifactor construct of
abilities with social and meta-cognitive modulating parameters, as
well as “luck”.
Most of these approaches support the view that giftedness
involves the existence of aptitudes which, in conjunction with certain
personality characteristics and a favourable environment, induce in
individuals the need and capacity to learn rapidly and efﬁciently by
themselves in different ﬁelds (Calero, García-Martín, & Gómez, 2007;
Coleman & Cross, 2001; Freeman, 2005; Jeltova & Grigorenko, 2005).
Underlying these new conceptualizations is a paradigm of
identiﬁcation of gifted children in which high capacity is acknowl-
edged to show in different ways and to require more varied and
reliable forms of assessment (Van Tassel-Baska, Feng, & Evans, 2007).
This view also derives from the perception that diverse minorities are
underrepresented in giftedness programs in countries of the devel-
oped world. According to such authors, actual and potential execution
should be distinguished (Cross & Coleman, 2005) and attention
should be paid to concepts such as ‘emergent giftedness’(Rea, 2001),
potential giftedness (Babayeva & Voiskunovsky, 2003; Leitis, 2000), or
high-potential children (Lohman, 2005). The implication of this
approach is that intelligence and/or creativity are regulated by other
abilities, such as ﬂexibility and self-regulation, and/or by speciﬁc
socio-environmental variables which may help to optimize these
qualities or conversely, maintain them at normal or low levels of
functioning. While some authors have developed performance-based
instruments (Van Tassel-Baska, 2005; Van Tassel-Baska, Feng, & de
Brux, 2007), others defend the use of dynamic assessment to identify
children with high capacities in underrepresented communities. This
is based on the assumption that individuals who show poor
performance for cultural or environmental reasons may be detected
if their performance is shown to improve signiﬁcantly after intensive
training on the task concerned (Joseph & Ford, 2006; Laing & Kahmi,
2002; Naglieri & Ford, 2005; Noel & Edmunds, 2007; Peña, Gillam,
Malek, & Ruiz-Felter, 2006; Stormont, Stebbins, & Holliday, 2001;
Strong & Delgado, 2005; Swanson, 2006).
Although several dynamic tests focus on speciﬁc aspects, partic-
ularly of the educational curriculum, the techniques used in this
context generally involve non-verbal tasks based on inductive
reasoning, probably with the aim of establishing cognitive modiﬁ-
ability as a general capacity that each person possesses, as claimed by
Learning and Individual Differences 21 (2011) 176–181
⁎Corresponding author. Campus of Cartuja, University of Granada, 18071 Granada,
Spain. Tel.: +34 58243754.
E-mail address: firstname.lastname@example.org (M.D. Calero).
1041-6080/$ –see front matter. Published by Elsevier Inc.
Contents lists available at ScienceDirect
Learning and Individual Differences
journal homepage: www.elsevier.com/locate/lindif
Author's personal copy
Feuerstein (Feuerstein, Feuerstein, Falik, & Rand, 2002; Feuerstein,
Rand, & Hoffman, 1979). The standard methodological procedure is
pre-test–training–post-test. Training uses feedback about implemen-
tation, contingent reinforcement and verbal signs, and effects of the
training have been shown to be task-speciﬁc(Brown & Campione,
1984; Fernández-Ballesteros & Calero, 2001). It should be noted that
while some studies show that pretest scores correlate with IQ, gain
scores (measure of Learning Potential) in some samples (low
performance) do not correlate with IQ (Lidz & Van der Aalsvoort,
2005; Resing, De Jong, Bosma, & Tunteler, 2009).
Two types of analyses may be carried out on the results of these
tests: quantitative (gain or transfer score), basically consisting of the
difference between post-test and pre-test, or a typological approach
(Budoff, 1987). This involves classifying participants as Non-gainers,
Gainers and High Scorers, thus differentiating between signiﬁcant and
non-signiﬁcant gains. This type of statistical calculation has been used
frequently (see Budoff, 1987 or Schöttke, Bartram, & Wield, 1993)to
establish prognostic groups in populations with learning difﬁculties,
and has been shown to be effective and reliable in other groups, such
as old people and patients with schizophrenia (Waldorf, Wiedl, &
The use of dynamic assessment in the context of giftedness
originates with studies by Boling and Day (1993) and Passow and
Frasier (1996). Their approach was based on the fact that dynamic
tests had proved to be valid for the identiﬁcation of children of low
intellectual level and/or with learning difﬁculties, and for planning
subsequent intervention (Strong & Delgado, 2005; Swanson, 1995).
Studies also indicated that training considerably improved the
performance levels of different groups of subjects (e.g. Hickson &
Skuy, 1990; for a review see the meta-analysis by Swanson & Lussier,
2001). As a result, the methodology is increasingly applied in
countries with established attention programs for gifted children
(e.g. the USA). For instance, Borland and Wright (1994), Calvo (2004),
Stanley (1995), Lidz and Macrine (2001), and Matthews and Foster
(2005) used dynamic assessment to identify gifted children (from
minority groups) for participation in gifted programs. The published
research is scarce and focuses on traditionally underrepresented
populations (primarily ethnic minorities). However, the results show
that the methodology was successful at identifying children who
passed unnoticed through traditional intelligence tests.
Kanevsky's (2000) studyofpre-schoolchildrenwithanIQ
between 110 and 150 demonstrated that gifted children possessed a
as well as faster learning capacity and higher
generalization from such learning. Moreover, the learning demon-
strated by the children was associated with high levels of motivation,
meta-cognition, self-regulation and ﬂexibility, a ﬁnding which has
been conﬁrmed in other studies (Calero, García-Martín, Jiménez,
Kazén, & Araque, 2007). Subsequently, Kanevsky and Geake (2004)
found signiﬁcant qualitative and quantitative differences in Learning
Potential between gifted and non-gifted children. However, these
results were not very conclusive due to the small sample size (5
gifted/20 non-gifted). Other authors have proposed applying perfor-
mance tests involving problem-solving processes or based on learning
acquisition, but always as a method for detecting children with high
potential in disadvantaged groups (Van Tassel-Baska, 2005; Van
Tassel-Baska, Feng, & de Brux, 2007; Van Tassel-Baska, Feng, & Evans,
Two assumptions underlie the dynamic assessment studies to
date: ﬁrst, gifted children are those who obtain the highest gain as a
result of the training, independently of their IQ. Second, the potential
“learning capacity”measured by the studies is a global capacity
(manifested in different tasks) and may subsequently be used as a
global indicator of high capacity. The aim of the present study was to
investigate the validity of both these assumptions in a sample of
Spanish children. The children, who were not socially disadvantaged,
had been previously identiﬁed as gifted, and were compared with
children of average intelligence.
Working hypotheses were as follows:
(1) Children with high IQ will present a signiﬁcantly higher pre-
test score than those of average intelligence in each and all of
the dynamic tests used.
(2) Children with high IQ will present a signiﬁcantly higher
Learning Potential, measured through gain scores (post/pre-
test difference) in each and all of the dynamic tests used.
(3) The gain scores obtained in the different Learning Potential
tests employed will be signiﬁcantly predictive of the estab-
lished classiﬁcation status (high IQ vs. average IQ).
The sample comprised of 127 Spanish middle-class urban-
dwelling children divided into two groups (high-IQ vs. average IQ).
In the high-IQ group, N = 64 (41 female and 23 male, Mage =8.18 -
years (SD= 1.859); age range: 7–11 years). IQ scores range from 136
to 160 (M IQ= 144.59; SD = 8.01) measured by the K-BIT test
(Kaufman & Kaufman, 1997). In the average IQ group, N = 63 (34
female and 29 male, Mage= 8.25 years (SD=1.859); age range: 7–
11 years). IQ scores range from 90 to 120 (MIQ=101.96, SD = 9.29).
2.2. Materials and procedure
The “Brief Intelligence Test (K-BIT)”(Kaufman & Kaufman, 1997)
consists of two subtests, Vocabulary and Matrices. Used to evaluate an
age range from 4 to 90 years, the test provides typical scores for each
subtest and a Global Composite IQ. The original version has high
reliability and validity ratings. In the Spanish adaptation, coefﬁcients
of reliability for Vocabulary range from 0.76 to 0.94 in the test age-
range (4–90 years); from 0.74 to 0.93 for Matrices and from 0.82 to
0.96 for the Composite IQ. With regard to validity, the K-BIT
Composite IQ has a correlation of 0.80 with the WISC-R global IQ,
and of 0.75 with the WAIS-R.
Learning Potential (L.P.) was evaluated using the following three
tests from the LPAD (Feuerstein et al., 1979), one of the few dynamic
assessment techniques adapted to the Spanish population.
“Positions Test”(Calero & Navarro, 2003). This is a version of the
Position Fixation Test (Rey, 1968) designed to evaluate visual–spatial
memory, adapted by Feuerstein et al. (1979) and adapted again for
the Spanish population with a dynamic training-within-test format by
Calero and Navarro (2003). The examiner presents ﬁve crosses drawn
on a grid with 25 squares and the subject attempts to reproduce the
positions by marking them on a blank grid. After each failed the
trainer provides more assistance with increasingly precise strategic
clues. The gain score (equal to posttest) is based on how much
assistance the child does not require to resolve each model.
“The Organizer Test”(Feuerstein et al., 1979). This test evaluates
the ability to use given information, resolve logic problems through
inferential processes, and deduce connections through analysis of
complex verbal information. Tasks consist of closed logical systems,
with a series of assertions or premises in each item. The test has a pre-
test, training and post-test, with a parallel structure of 20 items in
each phase. During the training, the assessor directs the child towards
ways of compiling data using memory and organization. Three types
of scores are obtained: pre-test, post-test and gain scores.
“Stencil Design Test”(Feuerstein et al., 1979). This perceptual
structuration task involves the analysis and synthesis of a series of
Zone of Proximal Development (Vygotsky, 1978).
177M.D. Calero et al. / Learning and Individual Differences 21 (2011) 176–181
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stimuli through the superimposition of patterns of different colors and
shapes. Using a total of 20 items, children are required to construct a
design identical to the one in the colored model, by means of
representation rather than manually. Five items are used for the pre-
test and 15 for the training and post-test. The training focuses on
visual transport and internal transformation of the stimulus. The gain
score is calculated according to the degree of help required to solve
The reliability of the three Learning Potential tests has been
assessed as part of Feuerstein's LPAD. Internal consistency and test–
retest reliability ranged from .70 to .95 (For more details on scoring
and application techniques, see Feuerstein et al., 1979; Feuerstein
et al., 2002).
High-IQ children were recruited through a local Association of
Spanish Parents of Gifted Children. Three of the children attended
accelerated learning programs. The others only received complemen-
tary tuition from their usual teacher. The children had been previously
identiﬁed as gifted by educational orientation teams using instru-
ments approved by the Autonomous Government of Andalusia
(Spain) (interviews with parents, WISC-R (Weschler, 1994), Raven's
Progressive Matrices (Rave, 1995) and school aptitude tests).
Average-IQ children were randomly selected in various schools
from students with an IQ of between 90 and 110 according to the
government orientation teams, using the WISC-R. Participation was
voluntary and subject to parents' informed consent. Children
presenting learning problems, hyperactivity or other psychological
conditions were excluded from both groups in order to control the
effects of other variables on the results.
All participants carried out the K-BIT and the dynamic tests in
three individual sessions lasting 50 min each. Presentation of the
different tests was counterbalanced to control learning effects.
The study followed a correlational two-group study design. The
independent variable was performance in the K-BIT; dependent
variables were pretest and gain scores in the L. P. tests.
In addition to the pre-test and gain scores, the results were
analyzed typologically. In this case, in line with previous studies
(Fernández-Ballesteros & Calero, 1993; Fernández-Ballesteros &
Calero, 1995), the calculation for the group of Gainers was based on
an improvement score of more than 1.5 SD with respect to the group
mean pre-test score. Calculation for the group of High Scorers was
based on the maximum range score with less than 1.5 SD with respect
to the group mean pre-test score.
The following statistical analyses were carried out: t-test, Chi-
square for classifying groups and Discriminant Analysis. All analyses
were performed using the statistics pack SPSS 15.1.
As stated earlier, the objective of the study was to establish
differences in the Learning Potential between high-IQ children and
those of average intelligence in the three different L. P. tests.
Accordingly, we initially carried out an independent group mean
comparison using the t- test analysis. Fig. 1 shows pre and post-test
scores obtained by each group. As may be seen, the high-IQ children
present a signiﬁcantly higher initial score (pre-test) and ﬁnal score
(post-test) in each and all of the tests. Differences in the Positions
Test are apparent only in the post-test score, since the training-
within-test format does not involve a pre-test. Results of the t-test
statistical analysis for independent samples were as follows: Positions
=18.82; p = .0001); Organizer Pre: (t
p= .0001); Organizer Post:(t
=26.36; p b= .0001); Stencil Design
=1.28; p = 0.0001); Stencil Design Post, (t
Table 1 shows inter-group mean differences in gain scores for each
of the L. P. tests. Again, the high-IQ children achieve signiﬁcantly
higher gain scores in all three tests. Moreover, while the size of effect
for each score in each test is signiﬁcantly high for both groups, the size
of effect for the high-IQ group is considerably superior in all cases.
Table 2 shows group distribution according to the established
categories for each dynamic test. In this case, the high-IQ children
appear as Gainers or High Scorers in the three dynamic assessment
tests. By contrast, children of average IQ are classiﬁed as Non-gainers
in some of the tests, and greater variability exists in the more difﬁcult
tests, that is, Organiser and Stencil Design. The Chi-square statistical
analysis is signiﬁcant for each classiﬁcation performed.
Finally, Table 3 shows the results of the Step-by-Step Discriminant
Analysis to determine the predictive capacity of each test with regard
to the initial classiﬁcation of each subject as Gifted or Non-gifted. As
may be seen, the three tests show predictive capacity, although Wilks
Lambda statistics for Step 3 are relatively low (.184, .165 and .150). In
view of these results, the most reliable predictor would be to take the
three tests together.
As speciﬁed earlier, in addition to determining if there are
signiﬁcant differences between high and average IQ subjects in gain
scores in Dynamic tests, the study aimed to establish if such
differences occurred in diverse types of tasks and if the tests
discriminate between the Gifted and Non-gifted status.
Regarding the ﬁrst objective, the high-IQ children started with a
signiﬁcantly higher performance level in each of the tests. Addition-
ally, they showed a signiﬁcantly higher improvement than those of
HIGH IQ AVERAGE IQ HIGH IQ AVERAGE IQ HIGH IQ AVERAGE IQ
Fig. 1. Pre and post-score differences between high-IQ children and children with average intelligence.
178 M.D. Calero et al. / Learning and Individual Differences 21 (2011) 176–181
Author's personal copy
average intelligence in all of the tests, on the basis of simple gain. This
is also conﬁrmed by the fact that if children are classiﬁed according to
their gain scores, no gifted child is assigned to the Non-gainers group
in any of the tests, in contrast to some normal-intelligence children.
Following Vygotsky's concept of the Zone of Proximal Develop-
ment (Vygotsky, 1978), which concentrates on what a child may
potentially become rather than what (s)he is, these results show that
high IQ children have a more extensive ZPD than average IQ children.
Accordingly, under the same training conditions, they achieve
markedly superior results in three different tests. It therefore appears
that dynamic assessment is a reliable method of establishing the
Learning Potential of such children, as was maintained in previous
studies (Coleman & Cross, 2001; Heller, 2004; Jeltova & Grigorenko,
2005; Van der Stel & Veenman, 2007; Veenman & Spaans, 2004).
These ﬁndings lend support to recent studies which argue that
intelligence implies capacity for learning and meta-cognition (Calero,
García-Martín, & Gómez, 2007; Calero, García-Martín, Jiménez, et al.,
2007; Kanevsky & Geake, 2004; Morris, 2005). High-IQ children not
only demonstrate high performance in all three tests, but also have a
high capacity to learn in each.
In this study we centered on a non-disadvantaged population in
order to show that children of high intelligence have a signiﬁcantly
higher and more general Learning Potential than children with
normal IQ. The results lead us to the view that, as proposed in earlier
studies (Borland & Wright, 1994; Lidz & Macrine, 2001; Matthews &
Foster, 2005), the methodology may be used to identify children
whose high potential is not manifested for environmental reasons,
and who present an average or even a low IQ. After training, such
children will show greater and more general improvement than
children with average capacity. Taken together with previous ﬁndings
(Calero, García-Martín, & Gómez, 2007; Calero, García-Martín,
Jiménez, et al., 2007; Morris, 2005), these results endorse the view
that the criteria for the identiﬁcation and differentiation of a gifted
child should not be based exclusively on the results of intelligence
tests, but should take other characteristics into account, particularly
when identifying children of potentially high intelligence but with
average or inadequate current performance (Borland, 2005; Brown
et al., 2005).
Regarding the second objective, our study also conﬁrms that
giftedness is a general capacity which is apparent in different contexts
(Gagné, 1985; McCoach & Siegle, 2003; Selby, Shaw, & Houtz, 2005).
In this respect, results show that high-IQ children learn more in each
and every one of the tasks assessed, representing different abilities:
memory of positions, verbal reasoning and perceptual structuration.
By contrast, the performance and Learning Potential of the average IQ
children varies from task to task. In our opinion, these results indicate
that high IQ children learn more than average IQ children in diverse
tasks. This may mean that the high performance of high IQ children in
certain areas is due to contextually-derived learning opportunities
(Sternberg, 2005; Winner, 2000).
Finally, we investigated the capacity of dynamic assessment to
predict the children's established status (Gifted/Non-gifted). In this
respect, our initial hypothesis was conﬁrmed, with the gain scores of
each and all of the tests signiﬁcantly predicting the classiﬁcation
status of the sample as determined by IQ. However, the opposite does
not occur, that is the IQ scores do not predict the gains obtained in the
dynamic tests (in line with previous studies by Kanevsky & Geake,
2004). Results demonstrate the signiﬁcantly high predictive power of
the Organizer, Stencil Design and Positions Test, in descending order.
These ﬁndings lend empirical support to the contention that dynamic
tests are capable of accurate identiﬁcation of gifted children.
To sum up, the results of our study indicate that high-IQ children
not only possess a high level of intelligence (measured by means of
standard tests), but that they learn more and more effectively in all or
most of the tasks undertaken, rather than in just one domain. Finally,
the dynamic assessment tests used in this study have proved to be
reliable instruments for discrimination between high and average IQ
All these results underline the usefulness of dynamic techniques
for assessing the potential of high-ability subjects, a ﬁnding which
may improve the process of identifying gifted children in general and
particularly –as other authors have suggested –of identifying
potential ability in children whose initial performance is low (e.g.
underrepresented groups) (Babayeva & Voiskunovsky, 2003;
Kanevsky & Geake, 2004; Leitis, 2000).
To carry out the Discriminant Analysis in this study, the IQ score
was used as indicative of high ability, and there was no attempt to
analyze other characteristics which determine giftedness. While this
may be viewed as a methodological limitation, in our view it was
necessary to use the IQ classiﬁcation as a starting point, in order to
show that dynamic assessment can help improve the identiﬁcation of
intellectually gifted children, as results have indicated. Although –
following Vigotsky –these tests evaluate a phenomenon distinct from
the ‘manifest’performance measured by IQ tests, they have proved
Mean differences in gain scores and effect size in high-IQ children and children with average intelligence.
Group Mean S.D. t (1/126) p d* t(1/63) p
Positions test Gain/effect size Average IQ 23.7 5.78 18.82 .0001 3.32 8.35 .0001
High IQ 38.11 2 4.88 10.81 .0001
Organizer test Gain/Effect Size Average IQ 3.85 1.93 19.74 .0001 2.49 14.08 .0001
High IQ 9.59 1.28 6.37 36.04 .0001
Stencil design test Gain/effect size Average IQ 9 5.67 18.34 .0001 1.54 8.68 .0001
High IQ 30.04 7.15 5.58 31.60 .0001
d*: effect sizes.
Contingency table of distribution between IQ level and gain status (with a typological
analysis) in the Learning Potential tests.
Positions test Average IQ 1 59 3 63 107.81 .0001
High IQ 0 2 62 64
Organizer test Average IQ 43 20 0 63 67.25 .0001
High IQ 0 59 5 64
Stencil design test Average IQ 24 39 0 63 37.12 .0001
High IQ 0 53 11 64
Step Introduced Tolerance F to exit Wilks
1 Gain scores organizer 1.000 389.67 389.67(1/126) .0001
2 Gain scores organizer .999 100.08 .271 351.62(2/125) .0001
Gain scores stencil
.999 76.92 .243
3 Gain scores organizer .969 52.34 .184 275.95(3/124) .0001
Gain scores stencil
.937 33.80 .165
Gain scores positions .909 19.53 .150
179M.D. Calero et al. / Learning and Individual Differences 21 (2011) 176–181
Author's personal copy
capable of predicting such a performance in children for whom a
favourable environment and/or other modulating variables have
made it possible to obtain high scores. The next step is to carry out
longitudinal studies to test the validity of these techniques for
identifying children of high ability who for various reasons do not
currently manifest such potential (Boling & Day, 1993; Borland &
Wright, 1994; Lidz & Macrine, 2001; Sibaya, 1996). In this respect,
future lines of research should include the replication of these results
in previously unidentiﬁed populations; that is, in the near future, the
tests should be used in the detection and follow-up of giftedness in
minority groups, and longitudinal studies should be undertaken to
monitor the evolution of children identiﬁed as Gifted or Non-gifted on
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