Content uploaded by Henriette Offer-Boljahn
Author content
All content in this area was uploaded by Henriette Offer-Boljahn on Feb 08, 2022
Content may be subject to copyright.
Journal of Pedagogical Research
Volume 6 , Issue 1, 2022
https://dx.doi.org/10.33902/JPR.20221175398
Research Article
Learning behaviors, executive functions, and
social skills: A meta-analysis on the factors
influencing learning development in the
transition from kindergarten to elementary school
Henriette Offer-Boljahn
1, Dennis Christian Hövel
2 and Thomas Hennemann
3
1
1University of Cologne, Faculty of Human Sciences, Germany (ORCID: 0000-0002-7736-4924)
2University of Applied Sciences in Special Needs Education (HfH), Switzerland (ORCID: 0000-0003-0933-2661)
3University of Cologne, Faculty of Human Sciences, Germany (ORCID: 0000-0003-4961-8680)
The linguistic, mathematical, social-emotional, and cognitive precursor competencies are important
predictors of learning success already at kindergarten age. A systematic analysis of the state of research on
the actual interrelationships of the focused precursor competencies brings together results of a meta-
analysis. The literature search yielded 653 hits, which were reduced to 94 hits after applying exclusion
criteria. It was possible to perform 271 correlation tests in 39 pathways. Thus, the sample (
[ ]) with an average age of 5.2 years ( years) is balanced in gender ratio of
48.5 % male and 48.9 % female (2.6 % not specified). The largest correlation between literacy and
mathematical precursor skills is . With a correlation between and , executive
functions significantly influence the development of literacy and mathematical skills and learning
behaviors. Learning behaviors are most strongly related to social skills, with . Parental influence
turns out to be smaller, with correlations ranging from to . The effect of individual pathways is
also small, and peer group was not included in the search term combination. Successful educational
processes in day care centers can be improved with a focus on the promotion of executive functions and
learning behaviors.
Keywords: Pre-primary education; Executive functions; Learning behaviors; Social-emotional learning;
Predictors
Article History: Submitted 4 September 2021; Revised 11 January 2022; Published online 8 February 2022
1. Introduction
In order to support learning success at an early age, learning at kindergarten age is increasingly
becoming the focus of educational research. Findings of previous research show that negative
learning experiences at kindergarten age can inhibit the motivation and effort of prospective
school-aged children (Viljaranta et al., 2009). Kindergarten age refers to children between the ages
of four and six years, as most OECD countries provide early education services in this age group in
Address of Corresponding Author
Henriette Offer-Boljahn, University of Cologne, Faculty of Human Sciences, Department of Health Education and Rehabilitation,
Lehrstuhl für Erziehungshilfe und sozial-emotionale Entwicklungsförderung, Klosterstrasse 79 c, Brieffach 9, 50931 Köln, Germany.
h.offer-boljahn@uni-koeln.de
How to cite: Offer-Boljahn, H., Hövel, D. C., & Hennemann, T. (2022). Learning behaviors, executive functions, and social skills: A meta-
analysis on the factors influencing learning development in the transition from kindergarten to elementary school. Journal of Pedagogical
Research, 6(1), 1-17. https://dx.doi.org/10.33902/JPR.20221175398
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 2
the context of early childcare (OECD, 2021). So-called precursor skills in literacy and early math
skills among kindergarten-aged children are discussed in numerous studies as important
predictors regarding the children's later literacy and math skills and can therefore counteract
negative learning experiences (e.g., Duncan et al., 2007; Hohm et al., 2017; Lonnemann &
Hasselhorn, 2018; Puolakanaho et al., 2008). Other than obvious learning related domains, the
OECD Professionals Survey (Bader et al., 2019) has highlighted that social-emotional skills and
their promotion are highly valued by parents and professionals in childcare settings. 87 % of the
respondents stated that they consider the promotion of cooperation skills to be particularly
important, which even exceeds the assessment of the importance of language skills at around 83 %
(Bader et al., 2019). Professionals in the eight OECD countries participating in the survey report
having particularly frequent conversations with children about their emotions. Thus, the need for
early promotion of social-emotional competencies is extremely high, as, for example, positive
learning experiences support motivation to engage with learning content (Blewitt, et al., 2018;
Viljaranta et al., 2009).
Several other studies also emphasize the importance of basic cognitive skills and well-
developed working memory in context of preparation for elementary school, (e.g., by relieving the
available knowledge in learning and problem solving from working memory capacities, thus
creating resources for newly acquired knowledge) and therefore should be considered when
discussing early learning at kindergarten age (Faust et al., 2012; Hasselhorn & Grube, 2008). For
practical reasons, it can be a real challenge to incorporate all these developmental areas into an
educational design simultaneously and in a quality manner. A multifactorial orientation of early
education in day care centers can help to implement high quality pre-elementary education to
prepare for a successful transition to elementary school.
1.1. The Interrelationships of Learning Areas at Kindergarten Age
Various studies (including Gasteiger-Klicpera et al., 2006; Gold, 2018; Mähler et al., 2017; Reinelt et
al., 2019) illustrate that learning domains are mutually dependent regarding school learning
success, although the respective cause-effect relationships cannot be fully clarified (Schuchardt &
Kuhn, 2019). For example, Duncan et al. (2007) point out the importance of early school readiness
skills, such as prior knowledge, attention, social-emotional skills, and mathematical precursor
skills; they note that these precursor skills can have a lasting impact on later learning success with
respect to students' numeracy and literacy. Cognitive skills, such as working memory skills, have
also been found to be significant precursor skills for learning and school success (Bredel, 2016;
Hasselhorn & Grube, 2008; Marx, 2006; Marx & Keller, 2010; Schulze & Kuhl, 2019). Mähler et al.
(2017) illustrate the links between cognitive and social-emotional competencies in terms of
wellbeing and school success, while Reinelt et al. (2019) depict problematic externalizing behaviors
in early infancy or in kindergarten in relation to later poor school performance and reduced
success in forming relationships with others. Gasteiger-Klicpera et al. (2006) point to a link
between reading and spelling difficulties and behavioral difficulties, which may have a
corresponding negative impact on children's academic learning success.
Discussing the importance of the domains of literacy, numerical, cognitive, and social-
emotional competences lead to the question how to install high quality education in a daily routine
at childcare centers. In this context, Offer-Boljahn et al. (2019) applied the concept of cross-domain
support of developmental domains and list combined support programs that were suitable for the
preschool year and tested for their effectiveness. It could be mapped that in these combined
programs especially educational elements of social-emotional competencies (i.e., emotion
knowledge or prosocial behavior) was implemented in all six programs in combination with
academic learning content (e.g., language promotion, basic mathematical skills, and thinking
strategies). Despite the wide range of evidence on the impact of the discussed early domain-
specific competences, there is no current overview of the correlations in pre-primary education.
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 3
1.2. Aims
Pre-primary education concerns more than just reading and writing. The results of the studies
discussed in the above underline the importance of literacy, numerical as well as socio-emotional
and cognitive education to support children in their learning at an early age. Prior knowledge can
contribute to the relief of working memory capacities to help successful information processing
and the expansion of knowledge. In addition, early linguistic competencies can lead to an
understanding of knowledge, and the formation of early social-emotional competencies can
contribute to the shaping of relationships and participation. Furthermore, these competencies
enable the recall of cognitive functions and thus shape social interaction in a sustainable way.
Thus, a systematic analysis on the actual interrelationships of the developmental areas of literacy
and mathematical, as well as social-emotional and cognitive competencies in kindergarten-aged
children is not yet available. The study examined the following question: How do the
developmental domains of literacy, numerical precursor skills, cognitive and socio-emotional
competences correlate at the age of four- to six-year-olds? Insights gained from this research could
yield important clues for the pedagogical practice of pre-primary education with a cross-domain
approach in the context of the transition to elementary school. This article presents the results of a
systematic literature search and tries to fill gaps in the research with the help of a meta-analytical
approach.
2. Method
2.1. Data Collection
Studies met inclusion criteria if (1) they included children aged 4 to 6 years as the OECD countries
apply pre-primary education at this age and most children can be reached for high-quality
educational means in childcare centers at this age; (2) at least two of the four domains of language
and mathematical precursor skills, social-emotional skills, and cognitive skills had to be
considered in relation to one another; (3) they assessed data from non-selective samples; (4) they
used a quantitative design; (4) they were available in German or English; and (5) they were
published in peer-reviewed scientific journals between January 2002 and December 2019 and were
retrievable. The time period was chosen because around the early 2000’s obligatory educational
plans for pre-primary education were published by committees in the OECD countries and a
corresponding thematization in the literature has been assumed.
To identify relevant studies, a systematic search was conducted using the Academic Search
Complete, ERIC, PsychARTICLES, PsychINFO, and PSYNDEX databases. This search involved
combining search terms that pertained to the topic area of interest. The combination of terms
included four search term groups related to preschool developmental domains (literacy: reading,
writing, literacy, linguistic, speech, phonological awareness, and listening; mathematics:
mathematics, numerical, numerous, counting, estimates, calculation, and mathematical ability;
social-emotional: emotion understanding, social-emotional, prosocial behavior, executive
functioning, self-regulation, social skills, externalizing, internalizing, and behavior; and cognition:
working memory, concentration, attention, and cognitive development). Two further groups of
search terms referred to the type of competencies (predictors: prediction, precursor, forecasting,
model, and projection) and the relationship to learning success (learning: academic achievement,
comprehension, successful learning, motivation, academic aptitude, and ability). The target group
was included in the combination of terms using relevant terms to describe the age group and
educational institution (i.e., preschool, kindergarten, school transition, primary/elementary school,
school age, school readiness, early childhood education, and preschool age). The process of data
selection is illustrated in Figure 1. All information was conducted in accordance with the Preferred
Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline
(Liberati et al., 2009). The titles and abstracts of the hit set were screened and filtered using the
above criteria. The search yielded 653 hits, which were reduced to 572 after duplicates were
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 4
removed by EBSCOhost. After reviewing the titles and abstracts, 359 publications were excluded
because they mostly addressed older age groups (k = 79) or included differing variables of
development (k = 149). Another k = 126 studies included selective samples, e.g. children with
ADHD or specific diseases. A total of k = 4 studies did not refer to a quantitative study design and
k = 1 study had not been readable due to language. The remaining k = 213 manuscripts were
screened, and an additional k = 119 studies were excluded for similar reasons: A total of k = 35 did
not meet the age criterion, k = 46 included differing variables in their data selection, k = 21 studies
revealed selective samples and k = 17 manuscripts were excluded because of differing criteria
regarding the respective study design. The systematic search ultimately identified 94 papers that
fit the criteria. The results of the literature search were reviewed by two independent experts to
check the study selection and to control the compliance with the inclusion and exclusion criteria. In
this way, it was checked that there was no reviewer bias.
Figure 1
Flow chart showing the results of the literature search
2.2. Data Analysis
According to Döring and Bortz (2016), the overall effect and the test of homogeneity of the total of
34 variables were formed. Subsequently, the effect size measure () was calculated to obtain a
consistent measure of effect size. This measure was then Z-transformed (Z), and the variance
within studies () as well as the weighting factors () for the studies were calculated. The overall
effect size () was then formed and tested for significance. These steps are necessary to account for
unequal effect sizes, study samples, and varying methodological quality across the studies of the
hit list by including weighted effect measures in the meta-analysis (Sedlmeier & Renkewitz, 2013).
For the final effect size the respective 95 % confidence interval and the correlation r were
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 5
calculated. With the fixed-effects model, the variables are assumed to be homogeneous, which was
tested (Q).
The assignment of the variables concerning the executive functions was done according to the
three dimensions of working memory—the visuo-spatial notepad (VS), the phonological loop (PL),
and the central executive (EF)—according to Baddeley's (1986) model. This approach was chosen
because the identified studies of this work maintain a different terminology in the field of
executive functions, and no uniform use of the conceptualization is apparent. Therefore, the
assignment was made via the respective description of the measurement levels.
3. Results
3.1. Description of Publications
The 94 studies in the analysis included 271 tests of correlation. These were summarized in 40
pathways, which are compiled in Table 1. Six additional variables (i.e., parents' numeracy
expectations, parent-child numeracy activities, parental education, feelings about school, home
learning environment, and classroom quality) were included alongside the core developmental
domains and tested for their associations because they showed practical relevance in the studies in
the hit list. The data are based on a total sample size of N = 141,508 children (M = 1.505;
SD = 3.930) aged 5.2 years (SD = 0.10 years). With a total distribution of 48.5 % male, 48.9 %
female, and 2.6 % no data, the sex ratio is balanced.
Of the studies, 59 % were conducted in the United States, 6.8 % were from Germany, 5.7 % from
Finland, and 4.6 % from the Netherlands. The remaining studies are distributed among Australia,
Belgium, Canada, China, Greece, Hong Kong, Israel, Spain, Switzerland, and the United Kingdom.
In the period from 2015 to 2019, 52.3 % of the studies were published; another 35.2 % were
published between 2010 and 2014, 12.5 % were published between 2005 and 2009. On average, four
correlations (SD = 2) were tested in each study; the maximum of tested correlations is 14, and the
minimum is one. The most frequent collection method is the test, with 62.7 % of all surveys; 19.2%
of the data were collected by teacher rating and16.6 % by parents rating, while 1.5 % of the
collection method is not specifically named.
Table 1
Results of the fixed-effect model of correlations
Variables
k
n
r
Z
95% CI
Working Memory and Learning Behaviors
Working Memory (WM) - LB
1
157
.40*
<0.05*
0.24
0.56
Executive functions (WM) - LB
2
18.459
.60***
12.11***
0.49
0.71
Visuospatial sketchpad (WM) - LB
2
18.459
.30***
3.75***
0.14
0.46
Phonological loop (WM) - LB
3
34.229
.17**
2.87**
0.05
0.29
Learning behaviors - Social competences
4
14.006
.57***
8.29***
0.51
0.82
Learning behaviors - Aggression
3
16.271
.47***
6.28***
0.35
0.66
Working Memory (WM) - Numeracy
12
18.196
.34***
6.05***
0.24
0.47
Executive functions (WM) - Numeracy
21
67.243
.48***
14.26***
0.45
0.59
Visuospatial sketchpad (WM) - Numeracy
27
32.142
.42***
10.58***
0.37
0.53
Phonological loop (WM) - Numeracy
19
26.655
.23***
4.62***
0.13
0.32
Working Memory (WM) - Literacy
14
18.120
.27***
4.72***
0.16
0.39
Executive functions (WM) - Literacy
27
50.234
.38***
10.39***
0.32
0.47
Visuospatial sketchpad (WM) - Literacy
30
49.167
.23***
6.36***
0.16
0.31
Phonological loop (WM) - Literacy
25
28.697
.28***
6.54***
0.20
0.38
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 6
Table 1 continued
Variables
k
n
r
Z
95% CI
Parents and Academic Achievement
Parents' numeracy expectations - Numeracy
2
149
.55**
2.5**
0.13
1.00
Parent-child numeracy activities - Numeracy
2
149
.44*
1.94*
0.00
0.95
Parental education - Numeracy
12
9.313
.39***
6.90***
0.30
0.53
Feelings about school - Numeracy
1
467
.34*
<0.05*
0.24
0.44
Parents' numeracy expectations - Literacy
2
149
.34
1.46
0.00
0.83
Parental education - Literacy
11
9.146
.32***
5.45***
0.21
0.45
Feelings about school - Literacy
1
467
.32*
<0.05*
0.23
0.41
Home Learning Environment - Literacy
5
5.868
.26***
3.35***
0.11
0.42
Parent-child numeracy activities - Literacy
3
270
.22
1.20
0.00
0.60
Home Learning Environment - Numeracy
4
2.618
.19*
1.89*
-0.01
0.38
Literacy and Numeracy
Literacy - Numeracy
52
39.385
.65***
20.77***
0.59
0.71
Classroom quality - Literacy
2
1.437
.53***
4.25***
0.32
0.86
Grammatical ability - Numeracy
3
279
.49**
2.84**
0.17
0.91
IQ - Numeracy
23
9.196
.44***
9.29***
0.37
0.57
IQ - Literacy
21
7.445
.43***
8.25***
0.34
0.56
Grammatical ability - Literacy
3
279
.42**
2.35**
0.07
0.82
Behavior and Academic Achievement
Hyperactivity/Inattention - Numeracy
7
17.785
.37***
6.17***
0.27
0.52
Hyperactivity/Inattention - Literacy
3
13.710
.29***
3.54***
0.13
0.47
Social-emotional skills - Numeracy
7
36.440
.28***
5.48***
0.18
0.38
Social-emotional skills - Literacy
10
37.004
.25***
5.23***
0.16
0.35
Self-regulation - Numeracy
6
47.837
.23***
4.65***
0.13
0.32
Self-regulation - Literacy
7
47.943
.21***
4.32***
0.11
0.30
Externalizing problem behaviors - Numeracy
4
40.644
.05
-0.98
0.00
0.05
Externalizing problem behaviors - Literacy
6
44.238
.08
-1.73
0.00
0.01
Internalizing problem behaviors - Numeracy
3
40.319
.04
-0.73
0.00
0.07
Internalizing problem behaviors - Literacy
4
40.663
.03
-0.64
0.00
0.07
Note. Total sample size: , ( ); k = total number of studies per variable,
n = total number of participants per variable, LB = learning behaviors; *** , ** , * .
3.2. Working Memory and Learning Behaviors
Results are presented with correlation coefficient r. This is a measure of strength and direction of a
linear relationship of two variables. According to Cohen (1988) it is interpreted as small from 0.1,
as moderate from 0.3 and as large from 0.5. A total of eight comparisons were made in the areas of
working memory and learning behaviors (Table 1). The learning domains are related with a
medium correlation. A single study is represented in the work of Sasser et al. (2015), which forms
an overall score for working memory and enters the study with a moderate correlation: r = .40,
p < .05, 95 % CI for r (0.24, 0.56). When looking at the individual facets of working memory, a more
heterogeneous picture of the results emerges. Phonological loop (r = .17, p < .01, 95 % CI for p [0.05,
0.29]) and visuospatial notepad (r = .30, p < .001, 95 % CI for r [0.14, 0.46]) are related with a small
to moderate correlation with learning behaviors. At r = .60, p < .001, 95% CI for r (0.49, 0.71),
executive functions are related to learning behaviors with a large correlation. The relationship
between social skills and learning behaviors was examined by k = 4 studies and showed a large
relationship overall (r = .57, p < .001, 95 % CI for r [0.51, 0.82]). Between learning behaviors and
aggressive behaviors, k = 3 papers identify a moderate relationship (r = .47, p < .001, 95 % CI for r
[0.35, 0.66]). The three dimensions of working memory (EF, VS, PL) were examined most
frequently across the hit list in terms of their associations with both mathematical precursor skills
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 7
(k = 78) and literacy (k = 95). Considered together, all identified correlations fall within the range
of moderate correlations. Executive functions achieved the strongest correlations in each of the two
skill areas: r = .48, p < .001, 95 % CI for r (0.45, 0.59) and r = .38, p < .001, 95% CI for r (0.32, 0.47).
The visual-spatial notepad has a moderate correlation with mathematical precursor skills: r = .42,
p < .001, 95 % CI for r (0.37, 0.53). In contrast, the correlation of working memory with literacy is
small: r = .23, p < .001, 95 % CI for r (0.16, 0.31). The phonological loop has a small correlation with
respect to both skill areas: r = .23, p < .001, 95 % CI for r (0.13, 0.32) and r = .28, p < .001, 95 % CI for
r (0.20, 0.38). Compared to the results regarding learning behaviors, the correlations with
antecedent competencies in math skills and literacy are lower, with small correlations.
3.3. Parents and Academic Achievement
Parental expectation of the child's mathematical skills is largely related to the child's surveyed
mathematical skills (k = 2, r = .55, p < .01, 95 % CI for r [0.13, 1.00], whereas these expectations are
not significantly related to literacy skills. Activities between the parent and child, e.g., counting
together or playing counting games together in daily life, are moderately related to mathematical
antecedent skills (k = 2, r = .44, p < .05, 95 % CI for r [0.00, 0.95]). The relationship to the literacy
learning domain is not statistically relevant. Parental education is related to both learning domains
of literacy and math prerequisite skills with moderate correlations of r = .39 (k = 12, p < .001, 95 %
CI for r [0.30, 0.53] and r = .32 (k = 11, p < .001, 95 % CI for r [0.21, 0.45]). The variable of feelings
about school is related to mathematical precursor skills with a moderate correlation (r = .34, k = 1,
p < .05, 95 % CI for r [0.24, 0.44]). In relation to literacy, this relationship is slightly smaller, but still
interpreted as a moderate correlation (k = 1, r = .32, p < .05, 95 % CI for r [0.23, 0.41]).
3.4. Literacy and Numeracy
The developmental domains of literacy and math antecedent skills were examined with k = 52
studies and achieved a large correlation: r = .65, p < .001, 95 % CI for r (0.59, 0.71). With k = 2
papers, the relationship between classroom quality and literacy (r = .53, p < .001, 95 % CI for r [0.32,
0.86]) was examined, and a large correlation was found. Regarding grammatical ability and the
developmental domains of mathematical precursor skills and literacy, moderate correlations were
found in each k = 3, r = .49, p < .01, 95 % CI for r (0.17, 0.91) and r = .42, p < .01, 95 % CI for r (0.07,
0.82). IQ was considered in relation to antecedent competencies in mathematics (k = 23, r = .44,
p < .001, 95 % CI for r [0.37, 0.57]) and literacy (k = 21, r = .43, p < .001, 95 % CI for r [0.34, 0.56]),
depicting a moderate correlation in both cases.
3.5. Behavior and Academic Achievement
Regarding the correlation tests, behaviorally relevant variables and the learning domains of
mathematical precursor skills and literacy were examined with the largest samples shown here
(n = 366,583). The correlations found are small to moderate. Hyperactivity and inattention have a
small correlation with preschool mathematical precursor skills: r = .37 (k = 7, p < .001, 95 % CI for r
[0.27, 0.52]). In contrast, language skills have a smaller correlation: r = .29 (k = 3, p < .001, 95 % CI
for r [0.13, 0.47]). Social-emotional competencies and the developmental domains of mathematical
precursor skills and literacy are related with a small to moderate correlation (k = 7, r = .28, p < .001,
95 % CI for r [0.18, 0.38] and k = 10, r = .25, p < .001, 95 % CI for r [0.16, 0.35]). As part of the
executive functions, which were mapped separately in the studies, the relationship between self-
regulation and literacy and mathematical antecedent skills is estimated to be small (k = 6, r = .23,
p < .001, 95 % CI for r [0.13, 0.32] and k = 7, r = .21, p < .001, 95 % CI for r [0.11, 0.30]). There is no
statistically relevant relationship between externalizing and internalizing problem behaviors with
respect to the literacy and mathematical precursor skills learning domains.
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 8
4. Discussion and Conclusion
The meta-analytic summary of the international study results shows a large correlation between
the competence areas of literacy and the mathematical precursor competencies, with r = .65. These
variables can also be seen in the study results of Duncan et al. (2007) as important predictors for
the later learning success of kindergarten-aged children. It is also obvious that the early learning
areas are of high importance regarding learning, especially as they represent the central cultural
techniques of early school education. Therefore, they are listed and considered in educational
curricula and support measures in early childhood, as the assessment of curricula in OECD
countries makes clear (OECD, 2021). Here, language learning content is specified and tracked in
98 % of existing education plans. Mathematical precursor competencies are listed in 93 % of the
curricula. In these, literacy competencies are rated as especially important by educational
professionals (Bader et al., 2019). Mathematical precursor competencies, in contrast, are rated less
significant or are less represented in educational curricula (OECD, 2021). However, the results of
the present work suggest that these learning areas should be considered equally important and
considered in the realization of support services, as they are also highly significant in terms of
learning success for school careers (Mähler et al., 2017). Kleemans et al. (2011) see the need for
grammatical skills in preparation for linguistic skills as well as early mathematical skills. Similar to
the results presented by Offer-Boljahn et al. (2019), two remedial programs could be found that,
included both language and mathematical precursor competencies in their curricula. After
implementation of the KiDZ program, the competency profiles of the sample expanded in the
intervention study (Rossbach et al., 2010). Another important area is working memory. With
correlations between r = .17 and r = .60, executive functions, verbal WM, and visuo-spatial WM are
significant factors that influence the development of written language and mathematical
competencies as well as learning behaviors. Following the meta-analysis of Peijnenborgh et al.
(2015), there is reason to believe that WM can be improved by appropriate school-based training,
with small to medium effect sizes (g = 0.36 to 0.63). The Tools of the Mind program (Blair & Raver,
2014), which is also listed in the review paper by Offer-Boljahn et al. (2019), enhances working
memory as early as kindergarten age.
The variable concerning learning behaviors is strongly related to social skills, with r = .57.
Neuenschwander et al. (2012) describe executive functions as abilities that enable learning despite
fatigue, distraction, or low motivation in children. Nguyen and Duncan (2018) report a statistically
significant relationship between executive functions and learning behavior (r = .79, p < .001). They
blame content overlap of the inhibitory control and learning behavior variables for the large
association and therefore believe further research using multidimensional survey methods is
necessary. Sasser et al. (2015) consider executive functions to be fundamental in establishing
learning behaviors. They further state that the dimensions of learning behaviors—such as
motivation, cooperation, attention, and persistence—support work in social contact, social
participation, and positive play experiences. Thus, social skills and learning behaviors make an
important contribution to a child's success in school, i.e., learning to read and facilitate acting on
instruction in elementary school mathematics classrooms (Sasser et al., 2015). Accordingly, well-
developed learning behaviors can strengthen social interaction by providing positive play
experiences and establishing and maintaining positive relationships with peers. Similar to the
review of Hagarty and Morgan (2020), some evidence for improving the social skills of children
and adolescents with learning disabilities is also found for interventions based on social-emotional
learning (SEL) theory. In their longitudinal study with a randomized experimental-control group
design (n = 1,634), McCormick et al. (2019) examined the effects of SEL over a five-year period. The
authors provide evidence of the significant effect of SEL on the need for special education needs
assignments (i.e., students who participated in SEL showed a lower rate of special education
needs). SEL can therefore be seen as an important field of action in instructional and support
planning for learning difficulties. The present results do not point to a direct connection between
internalizing or externalizing behavior and literacy or mathematical precursor competencies. It can
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 9
therefore be assumed that problems do not initially lead to deficient development, but deficient
developmental trajectories can lead to problems. Gasteiger-Klicpera et al. (2006) come to a similar
conclusion in the context of behavioral problems and reading alongside with spelling difficulties.
Morgan et al. (2018) state that cognitive flexibility, as part of executive functions, has a particularly
strong effect. They consider it to be responsible for the regulation of undesirable behaviors in
group contexts, as well as inattention. Nevertheless, cognitive flexibility can be useful for blocking
out irrelevant information and following instructions, thus facilitating learning in the domains of
mathematics and literacy. Miller-Cotto and Byrnes (2019) also suggest that working memory
performance and domain-specific skills reinforce each other. Higher performance in the math or
literacy skill domains leads to higher working memory performance, which in turn enables higher
cognitive processing of new content in the domains. Simultaneously, learning behaviors and
aggressive behaviors were collected in the analysis and showed a moderate correlation. Based on
parental perceptions of stress, it negatively affects children's learning behaviors and thus leads to
negative or undesirable behaviors in a group context, according to Smith-Adcock et al. (2019),
which Sasser et al. (2015) also found. In their study, Hassinger-Das et al. (2014) evaluated the
relationship of Hyperactivity and inattention in correlation to mathematical and linguistic
precursor skills finding out, that low attention may lead to learning delays in early school age. It is
evident that maladaptive behavior is often undesired in learning contexts and interferes with
learning, whether through the lack of attentiveness or through increased potentially negative
awareness by teachers.
Segers et al. (2015) state that the relationship with the child's competency profile is not an
outlook on learning development. Nevertheless, the relationship is significant since it is important
with respect to the home environment, as positive influences on learning development may be at
work here (Ferretti & Bub, 2017). Parents' learning activities with their children have a small to
moderate relationship with their children's ability profiles. With correlations ranging from r = .19
to .55, the home environment, parental expectations of the child, parental educational level, and
active practice and operation with numbers between parent and child are low in their influence on
the child's kindergarten-aged ability profile. These results highlight the significance of the
educational mission of the pedagogical care facility with its resource of pedagogical specialists.
With r = .53, they can already positively support learning with the help of good classroom
management. According to the results of Wustmann Seiler et al. (2017), good spatial and material
equipment of the childcare facility and a high quality of interaction can also have a positive effect
on children's behavior and dampen any family risk conditions. In terms of support opportunities,
the childcare facility therefore offers a viable setting, particularly regarding the promotion of
executive functions and learning behavior as well as social skills, which the home environment is
less able to provide in this way.
5. Limitations and Implications
The individual correlation checks shown here are only thinly represented, which is due to the
small number of studies found and to the widely varying sample sizes. In the composition of the
search term combination, children, educational professionals, and parents were included.
However, the term section on peer group behavior is missing, which may represent another
component in the development of competencies, especially regarding SEL.
Fortunately, the competency profiles in the included studies regarding the learning domains of
literacy and mathematics precursor competencies could be established using proximal elicitation
methods. To further extend the competencies of school learning, school performance could have
been another dimension of children's competencies and learning success. However, these were not
included in the included studies, which may be due to the age of the children. Performance
assessments in the form of grades have not yet been introduced internationally in the age group.
Regarding the terminology, in some cases, strongly different conceptualizations appear in the
international comparison. For example, working memory as part of the executive functions has not
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 10
always been clearly identified as such internationally. This is due to the different references to the
conceptualizations. The use of the conceptualizations alone does not represent an identical
interpretation of them and must be thoroughly examined to avoid ambiguities and confusions.
Here, a fundamental clarification is needed against the background that internationally
transferable terms and concepts cannot be confused with a country-specific reference model. Thus,
the variable of self-regulation is also listed as an individual one in the present results, although it is
counted among the executive functions. Therefore, this variable was listed under the behavioral
contextual tests.
For the area of early language development, the term literacy was chosen. The individual facets
of this term must be traced in the studies in the hit list. A higher operationalization of this learning
area meant a disproportionate reduction in the number of cases, which would not have been
conducive to the research question of this paper.
Do parents matter? Are they the ones who make the difference in the child's learning success?
The results of the meta-analysis have shown that executive functions have an important influence
on the learning behavior of children of kindergarten age and can also support the formation of
social skills. This finding is highly relevant for practical work in pre-elementary education, as this
content, coupled with language and mathematical educational goals, should be specifically
included, and focused on in educational programs. Therefore, the results emphasize the
importance of the day care center as an educational institution. Competencies of executive
functions should be disseminated by trained professionals, as the individual home environment
cannot offer this kind of support.
References
Note. References marked with an asterisk indicate studies included in the meta-analysis.
*Ahmed, S. F., Tang, S., Waters, N. E., & Davis-Kean, P. (2018). Executive function and academic
achievement: Longitudinal relations from early childhood to adolescence. Journal of Educational
Psychology, 111(3), 446–458. https://doi.org/10.1037/edu0000296
*Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in
academic attainment. Journal of Experimental Child Psychology, 106(1), 20–29.
https://doi.org/10.1016/j.jecp.2009.11.003
*Alloway, T. P., Gathercole, S. E., Adams, A.-M., Willies, C., Eaglen, R., & Lamont, E. (2005). Working
memory and phonological awareness as predictors of progress towards early learning goals at school
entry. British Journal of Developmental Psychology, 23(3), 417–426.
https://doi.org/10.1348/026151005X26804
*Anders, Y., Rossbach, H.-G., Weintert, S., Ebert, S., Kuger, S., Lehrl, S., & Maurice, J. (2012). Home and
preschool learning environments and their relations to the development of early numeracy skills. Early
Childhood Research Quarterly, 27(2), 231–244. https://doi.org/10.1016/j.ecresq.2011.08.003
*Aragón, E., Navarro, J., Aguilar, M., Cerda, G., & García-Sedeño, M. (2016). Predictive model for early math
skills based on structural equations. Scandinavian Journal of Psychology, 57(6), 489–494.
https://doi.org/10.1111/sjop.12317
*Arnold, D. H., Kupersmidt, J. B., Voegler-Lee, M. E., & Marshall, N. A. (2012). The association between
preschool children’s social functioning and their emergent academic skills. Early Childhood Research
Quarterly, 27(3), 376–386. https://doi.org/10.1016/j.ecresq.2011.12.009
*Austin, A. M., & Blevins-Knabe, B. (2012). Early mathematics and phonological awareness in two child care
settings. Early Child Development and Care, 183(9), 1–18. https://doi.org/10.1080/03004430.2012.716832
Baddeley, A. (1986). Working memory. Clarendon Press.
Bader, S., Bloem, S., Riedel, B., Seybel, C., & Turani, D. (Eds.). (2019). Kita-Praxis im internationalen Vergleich.
Ergebnisse der OECD-Fachkräftebefragung 2018 [Daycare Practices in International Comparison. Results of
the OECD Teacher Survey 2018]. https://www.dji.de/fileadmin/user_upload/bibs2019/28314_Kita-
Praxis_im_internationalen_Vergleich.pdf
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 11
Blair, C., & Raver, C. C. (2014). Closing the achievement gap through modification of neurocognitive and
neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to
the education of children in kindergarten. PLoS ONE, 9(11), 1–13.
https://doi.org/10.1371/journal.pone.0112393
Blewitt, C., Fuller-Tyszkiewicz, M., Nolan, A., Bergmeier, H., Vicary, D., Huang, T., McCabe, P., McKay, T.,
& Skouteris, H. (2018). Social and emotional learning associated with universal curriculum-based
interventions in early childhood education and care centers. A systematic review and meta-analysis.
JAMA Network Open, 1, 1-19. https://doi.org/10.1001/jamanetworkopen.2018.5727
Bredel, U. (2016). Schriftspracherwerb. In U. Domahs, & B. Rimus (Eds.), Handbuch Laut, Gebärde, Buchstabe
[Handbook Sound, Gesture, Letter] (pp. 436-454). de Gruyter.
*Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive
functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years.
Developmental Neuropsychology, 33(3), 205–228. https://doi.org/10.1080/87565640801982312
*Bull, R., Espy, K. A., Wiebe, S. A., Sheffield, T. D., & Nelson, J. M. (2011). Using confirmatory factor analysis
to understand executive control in preschool children: Sources of variation in emergent mathematic
achievement. Developmental Science, 14(4), 679–692. https://doi.org/10.1111/j.1467-7687.2010.01012.x
*Bulotsky-Shearer, R. J., Fernandez, V., Dominguez, X., & Rouse, H. L. (2011). Behavior problems in learning
activities and social interactions in head start classrooms and early reading, mathematics, and approaches
to learning. School Psychology Review, 40(1), 39–56. https://doi.org/10.1080/02796015.2011.12087727
*Byrnes, J. P., Wang, A., & Miller-Cotto, D. (2019). Children as mediators of their own cognitive development
in kindergarten. Cognitive Development, 50, 80–97. https://doi.org/10.1016/j.cogdev.2019.03.003
*Chrysochoou, E., & Bablekou, Z. (2011). Phonological loop and central executive contributions to oral
comprehension skills of 5.5 to 9.5 years old children. Applied Cognitive Psychology, 25(4), 576–583.
https://doi.org/10.1002/acp.1723
*Cirino, P. T. (2011). The interrelationships of mathematical precursors in kindergarten. Journal of
Experimental Child Psychology, 108(4), 713–733. https://doi.org/10.1016/j.jecp.2010.11.004
*Clark, C. A., Pritchard, V. E., & Woodward, L. J. (2010). Preschool executive functioning abilities predict
early mathematics achievement. Developmental Psychology, 46(5), 1176–1191.
https://doi.org/10.1037/a0019672
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Döring, N., & Bortz, J. (2016). Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften
[Research Methods and Evaluation in Social and Human Sciences] (5th ed.). Springer.
https://doi.org/10.1007/978-3-642-41089-5
*Davidse, N. J., De Jong, M. T., & Bus, A. G. (2014). Explaining common variance shared by early numeracy
and literacy. Reading and Writing, 27, 631–648. https://doi.org/10.1007/s11145-013-9465-0
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., Pagani, L. S.,
Feinstein, L., Engel, M., Brooks-Gunn, J., Sexton, H., Duckworth, K., & Japel, C. (2007). School readiness
and later achievement. Developmental Psychology, 43(6), 1428–1446. https://doi.org/10.1037/0012-
1649.43.6.1428
*Elliott, L. (2019). Profiles of academic, cognitive, and behavioral development from kindergarten to third
grade. Learning and Individual Differences, 70, 109–120. https://doi.org/10.1016/j.lindif.2019.01.010
Faust, G., Kratzmann, J., & Wehner, F. (2012). School Entry – a Risk for Schoolbeginners? Zeitschrift für
Pädagogische Psychologie, 26(3), 197–212. https://doi.org/10.1024/1010-0652/a000069
*Ferretti, L., & Bub, K. L. (2017). Family routines and school readiness during the transition to kindergarten.
Early Education and Development, 28(1), 59–77. https://doi.org/10.1080/10409289.2016.1195671
*Forget-Dubois, N., Lemelin, J.-P., Boivin, M., Dionne, G., Séguin, J. R., Vitaro, F., & Tremblay, R. E. (2007).
Predicting early school achievement with the EDI: A longitudinal population-based study. Early
Education and Development, 18(3), 405–426. https://doi.org/10.1080/10409280701610796
*Foster, M. E., Anthony, J. L., Clements, D. H., & Sarama, J. H. (2015). Processes in the development of
mathematics in kindergarten children from Title 1 schools. Journal of Experimental Child Psychology, 140,
56–73. https://doi.org/10.1016/j.jecp.2015.07.004
*Friedman, S. L., Scholnick, E. K., Bender, R. H., Vandergrift, N., Spieker, S., Hirsh Pasek, K., Keating, D. P.,
& Park, Y. (2014). Planning in middle childhood: Early predictors and later outcomes. Child Development,
85(4), 1446–1460. https://doi.org/10.1111/cdev.12221
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 12
*Fuchs, L. S., Geary, D. C., Compton, D. L., Fuchs, D., Hamlett, C. L., & Bryant, J. D. (2010). The contributions
of numerosity and domain-general abilities to school readiness. Child Development, 81(5), 1520–1533.
https://doi.org/10.1111/j.1467-8624.2010.01489.x
*Fuhs, M. W. (2011). Verbal ability and executive functioning development in preschoolers at Head Start.
Developmental Psychology, 47(2), 404–416. https://doi.org/10.1037/a0021065
*Fuhs, M. W., Farran, D. C., & Nesbitt, K. T. (2015). Prekindergarten children’s executive functioning skills
and achievement gains: The utility of direct assessments and teacher ratings. Journal of Educational
Psychology, 107(1), 207–221. https://doi.org/10.1037/a0037366
*Fuhs, M. W., Nesbitt, K. T., Farran, D. C., & Dong, N. (2014). Longitudinal associations between executive
functioning and academic skills across content areas. Developmental Psychology, 50(1), 1698–1709.
https://doi.org/10.1037/a0036633
Gasteiger-Klicpera, B., Klicpera, C., & Schabmann, A. (2006). Relationship between reading, spelling and
behavior disorders: A longitudinal study from kindergarten to the fourth grade of elementary school.
Kindheit und Entwicklung, 15(1), 55–67. https://doi.org/10.1026/0942-5403.15.1.55
*Geary, D. C., & vanMarle, K. (2016). Young children’s core symbolic and nonsymbolic quantitative
knowledge in the prediction of later mathematics achievement. Developmental Psychology, 52(12), 2130–
2144. https://doi.org/10.1037/dev0000214
*Georgiou, G. K., Tziraki, N., Manolitsis, G., & Fella, A. (2013). Is rapid automatized naming related to
reading and mathematics for the same reason(s)? A follow-up study from kindergarten to Grade 1.
Journal of Experimental Child Psychology, 115(3), 481–496. https://doi.org/10.1016/j.jecp.2013.01.004
*Georgiou, G. K., Wei, W., Inoue, T., Das, J. P., & Deng, C. (2019). Cultural influences on the relation between
executive functions and academic achievement. Reading and Writing, 33, 991–1013.
https://doi.org/10.1007/s11145-019-09961-8
Gold, A. (2018). Lernschwierigkeiten. Ursachen, Diagnostik, Intervention [Learning Disorders. Causes,
Diagnostics, Interventions] (2nd ed.). Stuttgart: Kohlhammer.
*Graziano, P. A., Reavis, R. D., Keane, S. P., & Calkins, S. D. (2007). The role of emotion regulation and
children's early academic success. Journal of School Psychology, 45(1), 3–19.
https://doi.org/10.1016/j.jsp.2006.09.002
Hagarty, I., & Morgan, G. (2020). Social-emotional learning for children with learning disabilities: A
systematic review. Educational Psychology in Practice, 36(2), 208–222.
https://doi.org/10.1080/02667363.2020.1742096
Hasselhorn, M., & Grube, D. (2008). Individual Preconditions and Developmental Constraints of Children’s
Learning during the Preschool and Early School Years. Empirische Pädagogik, 22, 113–126.
*Hassinger-Das, B., Jordan, N. C., Glutting, J., Irwin, C., & Dyson, N. (2014). Domain general mediators of
the relation between kindergarten number sense and first-grade mathematics achievement. Journal of
Experimental Child Psychology, 118, 78–92. https://doi.org/10.1016/j.jecp.2013.09.008
*Hirsch, S., Lambert, K., Coppens, K., & Moeller, K. (2018). Basic numerical competences in large-scale
assessment data: Structure and long-term relevance. Journal of Experimental Child Psychology, 167, 32–48.
https://doi.org/10.1016/j.jecp.2017.09.015
Hohm, E., Laucht, M., Zohsel, K., Schmidt, M., Brandeis, G., & Banaschweski, T. (2017). Resilience and
resources during development: From early childhood to adulthood. Kindheit und Entwicklung, 26(4), 230–
239. https://doi.org/10.1026/0942-5403/a000236
*Hornburg, C. B., Schmitt, S. A., & Purpura, D. J. (2018). Relations between preschoolers’ mathematical
language understanding and specific numeracy skills. Journal of Experimental Child Psychology, 176, 84–
100. https://doi.org/10.1016/j.jecp.2018.07.005
*Hughes, J., & Kwok, O.-M. (2007). Influence of student-teacher and parent-teacher relationships on lower
achieving readers’ engagement and achievement in the primary grades. Journal of Educational Psychology,
99(1), 39–51. https://doi.org/10.1037/0022-0663.99.1.39
*Hughes, J., Luo, W., Kwok, O.-M., & Loyd, L. K. (2008). Teacher-student support, effortful engagement, and
achievement: A 3-year longitudinal study. Journal of Educational Psychology, 100(1), 1–14.
https://doi.org/10.1037/0022-0663.100.1.1
*Isbell, E., Calkins, S. D., Cole, V. T., Swingler, M. M., & Leerkes, E. M. (2019). Longitudinal associations
between conflict monitoring and emergent academic skills: An event‐related potentials study.
Developmental Psychobiology, 61(4), 495–512. https://doi.org/10.1002/dev.21809
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 13
*Kleemans, T., Peeters, M., Segers, E., & Verhoeven, L. (2012). Child and home predictors of early numeracy
skills in kindergarten. Early Childhood Research Quarterly, 27(3), 471–477.
https://doi.org/10.1016/j.ecresq.2011.12.004
*Kleemans, T., Segers, E., & Verhoeven, L. (2011). Cognitive and linguistic precursors to numeracy in
kindergarten: Evidence from first and second language learners. Learning and Individual Differences, 21(5),
555–561. https://doi.org/10.1016/j.lindif.2011.07.008
*Koponen, T., Aunola, K., Ahonen, T., & Nurmi, J.-E. (2007). Cognitive predictors of single-digit and
procedural calculation skills and their covariation with reading skill. Journal of Experimental Child
Psychology, 97(3), 220–241. https://doi.org/10.1016/j.jecp.2007.03.001
*Koponen, T., Salmi, P., Torppa, M., Eklund, K., Aro, T., Aro, M., Poikkeus, A.-M., Lerkkanen, M.-K., &
Nurmi, J.-E. (2016). Counting and rapid naming predict the fluency of arithmetic and reading skills.
Contemporary Educational Psychology, 44-45, 83–94. https://doi.org/10.1016/j.cedpsych.2016.02.004
*Krajewski, K., Schneider, W., & Nieding, G. (2008). Early Training of Quantity – Number Competencies in
Preschool. Psychologie in Erziehung und Unterricht, 55, 100–113.
*Kyttälä, M., Aunio, P., Lepola, J., & Hautamäki, J. (2013). The role of the working memory and language
skills in the prediction of word problem solving in 4- to 7-year-old children. Educational Psychology, 34(6),
674–696. https://doi.org/10.1080/01443410.2013.814192
*LeFevre, J.-A., Fast, L., Skwarchuk, S.-L., Smith-Chant, B., Bisanz, J., Kamawar, D., & Penner-Wilger, M.
(2010). Pathways to mathematics: Longitudinal predictors of performance. Child Development, 81(6), 1753–
1767. https://doi.org/10.1111/j.1467-8624.2010.01508.x
*Lepola, J., Niemi, P., Kuikka, M., & Hannula, M. M. (2005). Cognitive-linguistic skills and motivation as
longitudinal predictors of reading and arithmetic achievement: A follow-up study from kindergarten to
grade 2. International Journal of Educational Research, 43(4-5), 250–271.
https://doi.org/10.1016/j.ijer.2006.06.005
*Li, Y., Xu, L., Liu, L., Lv, Y., Wang, Y., & Huntsinger, C. S. (2016). Can preschool socioeconomic composition
moderate relationships between family environment and Chinese children's early academic and social
outcomes? Children and Youth Services Review, 60, 1–10. https://doi.org/10.1016/j.childyouth.2015.11.009
Liberati, A., Atman, D., Tetzlaff, J., Mulrow, C., Gøtzsche, P., Ioannidis, J., Clarke, M., Devereaux, P.,
Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-
analyses of studies that evaluate health care interventions: Explanation and elaboration. PLOS Medicine, 6,
1–28. https://doi.org/10.1371/journal.pmed.1000100
*Lockl, K., Ebert, S., & Weinert, S. (2017). Predicting school achievement from early theory of mind:
Differential effects on achievement tests and teacher ratings. Learning and Individual Differences, 53, 93–102.
https://doi.org/10.1016/j.lindif.2016.11.007
*Lombardi, C. M., Casey, B. M., Thomson, D., & Nguyen, H. N. (2017). Maternal support of young children’s
planning and spatial concept learning as predictors of later math (and reading) achievement. Early
Childhood Research Quarterly, 41, 114–125. https://doi.org/10.1016/j.ecresq.2017.07.004
Lonnemann, J., & Hasselhorn, M. (2018). Early mathematics education: Current research trends and
perspectives. Frühe Bildung, 7(3), 129–134. https://doi.org/10.1026/2191-9186/a000379
Mähler, C., Petermann, U., & Greve, W. (2017). Social-emotional and cognitive skills as regulation
competencies. Kindheit und Entwicklung, 26(1), 1–6. https://doi.org/10.1026/0942-5403/a000210
*Martin, R. B., Cirino, P. T., Sharp, C., & Barnes, M. (2014). Number and counting skills in kindergarten as
predictors of grade 1 mathematical skills. Learning and Individual Differences, 34, 12–23.
https://doi.org/10.1016/j.lindif.2014.05.006
Marx, E. (2006). Does a child’s language system benefit from other domains of cognitive development? A
study of the relationship between language acquisition and inductive reasoning. Zeitschrift für
Entwicklungspsychologie und Pädagogische Psychologie, 38(3), 139–145. https://doi.org/10.1026/0049-
8637.38.3.139
Marx, E., & Keller, K. (2010). Effects of an Inductive Reasoning Training on the Development of Language
and Intelligence in Preschoolers and First Graders from Socially Disadvantaged Districts. Zeitschrift für
Pädagogische Psychologie, 24(2), 139–146. https://doi.org/10.1024/1010-0652/a000011
*McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., & Morrisson, F. J. ( 2007).
Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills.
Developmental Psychology, 43(4), 947–959. https://doi.org/10.1037/0012-1649.43.4.947
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 14
*McClelland, M. M., Cameron, C. E., Duncan, R., Bowles, P. P., Acock, A. C., Miao, A., & Pratt, M. E. (2014).
Predictors of early growth in academic achievement: The head-toes-knees-shoulders task. Frontiers in
Psychology, 5, 1–14. https://doi.org/10.3389/fpsyg.2014.00599
*McCormick, M. P., O'Connor, E. E., & Barnes, S. P. (2016). Mother–child attachment styles and math and
reading skills in middle childhood: The mediating role of children’s exploration and engagement. Early
Childhood Research Quarterly, 36, 295–306. https://doi.org/10.1016/j.ecresq.2016.01.011
McCormick, M. P., Neuhaus, R., Horn, E. P., O’Connor, E. E., White, H. I., Harding, S., Cappella, E., &
McClowry, S. (2019). Long-term effects of social-emotional learning on receipt of special education and
grade retention: Evidence from a randomized trial of INSIGHTS. AERA Open, 5(3), 1–21.
https://doi.org/10.1177/2332858419867290
*McLear, C., Trentacosta, C. J., & Smith-Darden, J. (2016). Child self-regulation, parental secure base scripts,
and at-risk kindergartners’ academic achievement. Early Education and Development, 27(4), 440–456.
https://doi.org/10.1080/10409289.2016.1091972
*Merz, E., Zucker, T. A., Landry, S. H., Williams, J. M., Assel, M., Taylor, H., Lonigan, C., Phillips, B. M.,
Clancy-Menchetti, J., Barnes, M. A., Eisenberg, N., & de Villiers, J. (2015). Parenting predictors of
cognitive skills and emotion knowledge in socioeconomically disadvantaged preschoolers. Journal of
Experimental Child Psychology, 132, 14–31. https://doi.org/10.1016/j.jecp.2014.11.010
*Miller-Cotto, D., & Byrnes, J. P. (2020). What’s the best way to characterize the relationship between
working memory and achievement? An initial examination of competing theories. Journal of Educational
Psychology, 112(5), 1074–1084. https://doi.org/10.1037/edu0000395
*Moll, K., Snowling, M. J., Göbel, S. M., & Hulme, C. (2015). Early language and executive skills predict
variations in number and arithmetic skills in children at family-risk of dyslexia and typically developing
controls. Learning and Instruction, 38, 53–62. https://doi.org/10.1016/j.learninstruc.2015.03.004
*Morgan, P. L., Farkas, G., Hillemeier, M., Pun, W. H., & Maczuga, S. (2019). Kindergarten children's
executive functions predict their second‐grade academic achievement and behavior. Child Development,
90(5), 1802–1816. https://doi.org/10.1111/cdev.13095
*Mou, Y., Bertletti, I., & Hyde, D. C. (2018). What counts in preschool number knowledge? A Bayes factor
analytic approach toward theoretical model development. Journal of Experimental Child Psychology, 166,
116–133. https://doi.org/10.1016/j.jecp.2017.07.016
*Nanu, C., McMullen, J., Munck, P., & Hannula-Sormunen, M. H. (2018). Spontaneous focusing on
numerosity in preschool as a predictor of mathematical skills and knowledge in the fifth grade. Journal of
Experimental Child Psychology, 169, 42–58. https://doi.org/10.1016/j.jecp.2017.12.011
*Neuenschwander, R., Röthlisberger, M., Cimeli, P., & Roebers, C. (2012). How do different aspects of self-
regulation predict successful adaptation to school? Journal of Experimental Child Psychology, 113(3), 353–
371. https://doi.org/10.1016/j.jecp.2012.07.004
*Nevo, E., & Breznitz, Z. (2011). Assessment of working memory components at 6 years of age as predictors
of reading achievements a year later. Journal of Experimental Child Psychology, 109(1), 73–90.
https://doi.org/10.1016/j.jecp.2010.09.010
*Nguyen, T., & Duncan, G. J. (2019). Kindergarten components of executive function and third grade
achievement: A national study. Early Childhood Research Quarterly, 46, 49–61.
https://doi.org/10.1016/j.ecresq.2018.05.006
*Nguyen, T., Duncan, R. J., & Bailey, D. (2019). Theoretical and methodological implications of associations
between executive function and mathematics in early childhood. Contemporary Educational Psychology, 58,
376–287. https://doi.org/10.1016/j.cedpsych.2019.04.002
*Niklas, F., & Schneider, W. (2014). Casting the die before the die is cast: the importance of the home
numeracy environment for preschool children. European Journal of Psychology of Education, 29, 327–345.
https://doi.org/10.1007/s10212-013-0201-6
*Niklas, F., & Schneider, W. (2017). Home learning environment and development of child competencies
from kindergarten until the end of elementary school. Contemporary Educational Psychology, 49, 263–274.
https://doi.org/10.1016/j.cedpsych.2017.03.006
*Niklas, F., & Tayler, C. (2018). Room quality and composition matters: Children's verbal and numeracy
abilities in Australian early childhood settings. Learning and Instruction, 54, 114–124.
https://doi.org/10.1016/j.learninstruc.2017.08.006
*Noël, M.-P. (2009). Counting on working memory when learning to count and to add: A preschool study.
Developmental Psychology, 45(6), 1630–1643. https://doi.org/10.1037/a0016224
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 15
OECD. (2021). Starting Strong IV. Supporting Meaningful Interactions in Early Childhood Education and Care (Vol.
Starting Strong). Paris: OECD Publishing. https://doi.org/10.1787/f47a06ae-en
Offer-Boljahn, H., Hövel, D., & Hennemann, T. (2019). Multicomponent interventions in early child
education: A systematic review. Psychoeducational Assessment, Intervention and Rehabilitation, 1(1), 31–44.
https://doi.org/10.30436/PAIR19-01
*Passolunghi, M. C., Lanfranchi, S., Altoè, G., & Sollazzo, N. (2015). Early numerical abilities and cognitive
skills in kindergarten children. Journal of Experimental Child Psychology, 135, 25–42.
https://doi.org/10.1016/j.jecp.2015.02.001
*Passolunghi, M. C., Vercelloni, B., & Schadee, H. (2007). The precursors of mathematics learning: Working
memory, phonological ability and numerical competence. Cognitive Development, 22(2), 165–184.
https://doi.org/10.1016/j.cogdev.2006.09.001
*Paul, J. M., Gray, S. A., Butterworth, B. L., & Reeve, R. A. (2019). Reading and math tests differentially
predict number transcoding and number fact speed longitudinally: A random intercept cross-lagged
panel approach. Journal of Educational Psychology, 111(2), 299–313. https://doi.org/10.1037/edu0000287
Peijnenborgh, J., Hurks, P., Aldenkamp, A., Vles, J., & Hendriksen, J. (2015). Efficacy of working memory
training in children and adolescents with learning disabilities: A review study and meta-analysis.
Neuropsychological Rehabilitation, 26(5-6), 645–672. https://doi.org/10.1080/09602011.2015.1026356
*Perry, K. E., Donohue, K. M., & Weinstein, R. S. (2007). Teaching practices and the promotion of
achievement and adjustment in first grade. Journal of School Psychology, 45(3), 269–292.
https://doi.org/10.1016/j.jsp.2007.02.005
*Preßler, A.-L., Könen, T., Hasselhorn, M., & Krajewski, K. (2014). Cognitive preconditions of early reading
and spelling: a latent-variable approach with longitudinal data. Reading and Writing, 27, 383–406.
https://doi.org/10.1007/s11145-013-9449-0
*Puccioni, J., Baker, E. R., & Froiland, J. M. (2019). Academic socialization and the transition to kindergarten:
Parental beliefs about school readiness and involvement. Infant and Child Development, 28(6), 1–25.
https://doi.org/10.1002/icd.2154
Puolakanaho, A., Ahonen, T., Aro, M., Eklund, K., Leppänen, P., Poikkeus, A.-M., Tolvanen, A., Torppa, M.,
& Lyytinen, H. (2008). Developmental links of very early phonological and language skills to second
grade reading outcomes. Strong to accuracy but only minor to fluency. Journal of Learning Disabilities,
41(4), 353–370. https://doi.org/10.1177/0022219407311747
*Purpura, D. J., & Logan, J. A. (2015). The nonlinear relations of the approximate number system and
mathematical language to early mathematics development. Developmental Psychology, 51(12), 1717–1724.
https://doi.org/10.1037/dev0000055
*Purpura, D. J., & Simms, V. (2018). Approximate Number System development in preschool: What factors
predict change? Cognitive Development, 45, 31–39. https://doi.org/10.1016/j.cogdev.2017.11.001
*Purpura, D. J., Logan, J. A., Hassinger-Das, B., & Napoli, A. R. (2017). Why do early mathematics skills
predict later reading? The role of mathematical language. Developmental Psychology, 53(9), 1633–1642.
https://doi.org/10.1037/dev0000375
*Rasmussen, C., & Bisanz, J. (2005). Representation and working memory in early arithmetic. Journal of
Experimental Child Psychology, 91(2), 137–157. https://doi.org/10.1016/j.jecp.2005.01.004
Reinelt, T., Samdan, G., Kiel, N., & Petermann, F. (2019). Predicting Externalizing Behavior Problems in Early
Childhood: Evidence From Longitudinal Studies. Kindheit und Entwicklung, 28(1), 19–32.
https://doi.org/10.1026/0942-5403/a000268
Rossbach, H.-G., Sechtig, J., & Freund, U. (2010). Empirische Evaluation des Modellversuchs "Kindergarten der
Zukunft - KiDZ". Ergebnisse der Kindergartenphase [Empirical Evaluation of the Pilot Project „Kindergarten
of the Future – KiDZ―. Results of the Kindergarten Years]. University of Bamberg Press.
*Sáez, L., Sidler Folsom, J., Al Otaiba, S., & Schatschneider, C. (2012). Relations among student attention
behaviors, teacher practices, and beginning word reading skill. Journal of Learning Disabilities, 45(5), 418–
432. https://doi.org/10.1177/0022219411431243
*Sasser, T. R., Bierman, K. L., & Heinrichs, B. (2015). Executive functioning and school adjustment: The
mediational role of pre-kindergarten learning-related behaviors. Early Childhood Research Quarterly, 30,
70–79. https://doi.org/10.1016/j.ecresq.2014.09.001
*Schmerold, K., Bock, A., Peterson, M., Leaf, B., Vennergrund, K., & Pasnak, R. (2017). The relations between
patterning, executive function, and mathematics. The Journal of Psychology, 151(2), 207–228.
https://doi.org/10.1080/00223980.2016.1252708
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 16
*Schmitt, S. A., Geldhof, J., Purpura, D., Duncan, R., & McClelland, M. M. (2017). Examining the relations
between executive function, math, and literacy during the transition to kindergarten: A multi-analytic
approach. Journal of Educational Psychology, 109(8), 1120–1140. https://doi.org/10.1037/edu0000193
Schuchardt, K., & Kuhn, J.-T. (2019). Komorbidität von Lernschwierigkeiten. Lernen und Lernstörungen, 8(1),
5–6. https://doi.org/10.1024/2235-0977/a000251
Schulze, S., & Kuhl, J. (2019). Mathematical learning support and integrated working memory training.
Lernen und Lernstörungen, 8(1), 47–59. https://doi.org/10.1024/2235-0977/a000229
*Schwarz, M., & Shaul, S. (2018). Towards a better understanding of the link between executive functions,
early literacy, and emergent mathematical abilities. Written Language & Literacy, 21(2), 238–268.
https://doi.org/10.1075/wll.00016.sch
Sedlmeier, P., & Renkewitz, F. (2013). Forschungsmethoden und Statistik für Psychologen und
Sozialwissenschaftler [Research Methods and Statistics for Psychologists and Social Scientists] (2nd ed.).
Pearson.
*Segers, E., Kleemans, T., & Verhoeven, L. (2015). Role of parent literacy and numeracy expectations and
activities in predicting early numeracy skills. Mathematical Thinking and Learning, 17(2-3), 219–236.
https://doi.org/10.1080/10986065.2015.1016819
*Sims, D. M., Purpura, D. J., & Lonigan, C. J. (2016). The relation between inattentive and
hyperactive/impulsive behaviors and early mathematics skills. Journal of Attention Disorders, 20(8), 704–
714. https://doi.org/10.1177/1087054712464390
*Skwarchuk, S.-L., Sowinski, C., & LeFevre, J.-A. (2014). Formal and informal home learning activities in
relation to children’s early numeracy and literacy skills: The development of a home numeracy model.
Journal of Experimental Child Psychology, 121, 63–84. https://doi.org/10.1016/j.jecp.2013.11.006
*Smith-Adcock, S., Leite, W., & Amatea, E. (2019). A model of parenting risk and resilience, social-emotional
readiness, and reading achievement in kindergarten children from low-income families model. Journal of
Child and Family Studies, 28, 2826–2841. https://doi.org/10.1007/s10826-019-01462-0
*Son, S.-H. C., Choi, J. Y., & Kwon, K.-A. (2019). Reciprocal associations between inhibitory control and early
academic skills: Evidence from a nationally representative sample of Head Start children. Early Education
and Development, 30(4), 456–477. https://doi.org/10.1080/10409289.2019.1572382
*Soto-Calvo, E., Simmons, F. R., Willis, C., & Adams, A.-M. (2015). Identifying the cognitive predictors of
early counting and calculation skills: Evidence from a longitudinal study. Journal of Experimental Child
Psychology, 140, 16–37. https://doi.org/10.1016/j.jecp.2015.06.011
*Stipek, D., & Valentino, R. A. (2015). Early childhood memory and attention as predictors of academic
growth trajectories. Journal of Educational Psychology, 107(3), 771–788.
https://doi.org/10.1037/edu0000004
*Tindal, G., Irvin, S., & Nese, J. F. (2015). Skills for children entering kindergarten. Educational Assessment,
20(4), 297–319. https://doi.org/10.1080/10627197.2015.1093929
*Trentacosta, C. J., & Izard, C. E. (2007). Kindergarten children’s emotion competence as a predictor of their
academic competence in first grade. Emotion, 7(1), 77–88. https://doi.org/10.1037/1528-3542.7.1.77
*Vandenbroucke, L., Verschueren, K., Desoete, A., Aunio, P., Ghesquière, P., & Baeyens, D. (2018). Crossing
the bridge to elementary school: The development of children’s working memory components in relation
to teacher-student relationships and academic achievement. Early Childhood Research Quarterly, 42, 1–10.
https://doi.org/10.1016/j.ecresq.2017.08.004
Viljaranta, J., Lerkkanen, L.-K., Poikkeus, A.-M., Aunola, K., & Nurmi, J.-E. (2009). Cross-lagged relations
between task motivation and performance in arithmetic and literacy in kindergarten. Learning and
Instruction, 19(4), 335–344. https://doi.org/10.1016/j.learninstruc.2008.06.011
*Viterbori, P., Usai, C., Traverso, L., & De Franchis, V. (2015). How preschool executive functioning predicts
several aspects of math achievement in Grades 1 and 3: A longitudinal study. Journal of Experimental Child
Psychology, 140, 38–55. https://doi.org/10.1016/j.jecp.2015.06.014
*Weiland, C., Ulvestad, K., Sachs, J., & Yoshikawa, H. (2013). Associations between classroom quality and
children’s vocabulary and executive function skills in an urban public prekindergarten program. Early
Childhood Research Quarterly, 28(2), 199–209. https://doi.org/10.1016/j.ecresq.2012.12.002
*Welsh, J., Nix, R., Blair, C., Bierman, K., & Nelson, K. (2010). The development of cognitive skills and gains
in academic school readiness for children from low-income families. Journal of Educational Psychology,
102(1), 43–53. https://doi.org/10.1037/a0016738
H. Offer-Boljahn et al. / Journal of Pedagogical Research, 6(1), 1-17 17
*Willoughby, M., Blair, C., Wirth, R. J., & Greenberg, M. (2012). The measurement of executive function at
age 5: Psychometric properties and relationship to academic achievement. Psychological Assessment, 24,
226–236. https://doi.org/10.1037/a0025361
Wustmann Seiler, C., Müller, E., & Simoni, H. (2017). The protective role of childcare quality for behavioral
adjustment in 3- to 5-year-old children. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie,
49, 1–10. https://doi.org/10.1026/0049-8637/a000162
*Zhang, X., & Lin, D. (2018). Cognitive precursors of word reading versus arithmetic competencies in young
Chinese children. Early Childhood Research Quarterly, 42, 55–65.
https://doi.org/10.1016/j.ecresq.2017.08.006
