read 2 articles, then write 250 words response paper

After you read the assigned articles, I want you to write a minimum 1 page response paper (250 words) to the articles including reflecting on your childhood and how this information would be relevant for current/ future work goals (i.e if you are currently working or plan to work in a childcare setting).

PS62CH18-Phillips ARI 22 November 2010 8:34

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Early Care, Education,
and Child Development
Deborah A

Phillips1 and Amy E. Lowenstein2
1 Department of Psychology, Georgetown University, Washington, DC 20057;
email: deborah.dap4@gmail.com
2 Institute of Human Development and Social Change, New York University, New York,
New York 10003; email: amy.lowenstein@nyu.edu

Annu. Rev. Psychol. 2011. 62:483–500

First published online as a Review in Advance on
September 3, 2010

The Annual Review of Psychology is online at
psych.annualreviews.org

This article’s doi:
10.1146/annurev.psych.031809.130707

0066-4308/11/0110-0483$20.00

Key Words

child care, Head Start, prekindergarten, public policy, developmental
outcomes, ecological framework

Abstract

Children growing up in the United States today typically spend a sub-
stantial portion of their early childhood years in early care and education
(ECE) settings. These settings are thus an essential element of any ef-
fort to understand the ecology of early development. Research aimed at
identifying the short- and long-term impacts of ECE experiences has a
long history, the results of which now point to three key conclusions.
(a) Although parents are the most important influence on children’s de-
velopment, ECE experiences have both short- and long-term impacts
on a wide range of developmental outcomes that are best understood
in interaction with family effects. (b) The quality of adult-child inter-
actions in ECE settings is the most potent source of variation in child
outcomes, although the amount of exposure to these settings also plays
a role, perhaps especially with regard to social-emotional development.
(c) Some children, notably those growing up in poverty, appear to be
more vulnerable to variation in the quality of ECE settings than do
other children. The frontiers of ECE research are addressing individ-
ual differences in children’s responses to child care and approaching
these settings both as sites for intervention research and as part of a
wider web of important settings in young children’s lives.

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Contents

EARLY CARE AND EDUCATION
IN THE UNITED STATES . . . . . . 484

THE CURRENT LANDSCAPE OF
EARLY CARE AND
EDUCATION . . . . . . . . . . . . . . . . . . . . 485

CHILDREN’S EXPERIENCE OF
EARLY CARE AND
EDUCATION . . . . . . . . . . . . . . . . . . . . 486
Time in Care . . . . . . . . . . . . . . . . . . . . . . 486
Quality of Care . . . . . . . . . . . . . . . . . . . . 486

EVOLUTION OF RESEARCH ON
EARLY CARE AND
EDUCATION . . . . . . . . . . . . . . . . . . . . 488
Maternal Deprivation Framework . . 489
Ecological Framework . . . . . . . . . . . . . 489
Compensatory Education

Framework . . . . . . . . . . . . . . . . . . . . . 490
THE EFFECTS OF EARLY CARE

AND EDUCATION . . . . . . . . . . . . . . 491
Child Care: Implications for the

Parent-Child Relationship . . . . . . . 491
Developmental Effects of Child

Care . . . . . . . . . . . . . . . . . . . . . . . . . . . 491
Developmental Effects of Head

Start . . . . . . . . . . . . . . . . . . . . . . . . . . . 492
Developmental Effects of Preschool

and State-Funded Pre-K . . . . . . . . 494
CONCLUSIONS AND

FUTURE DIRECTIONS . . . . . . . . . 494

EARLY CARE AND EDUCATION
IN THE UNITED STATES

Second only to the immediate family, early care
and education (ECE) settings are the context
in which early development unfolds, starting in
infancy and continuing through school entry
for the vast majority of children in the United
States. In 2005, 11.3 million children under the
age of 5 were in some child care or early educa-
tion arrangement while their mothers worked,
including 1.9 million infants under the age of
1 (U.S. Bur. Census 2008). Although children
have spent time outside the care of their moth-

ers throughout history (Lancaster & Lancaster
1987, Weisner & Gallimore 1977), the addition
of paid ECE arrangements with nonrelatives to
the repertoire of who cares for young children is
a relatively new phenomenon. Parents and rela-
tives continue to provide vast amounts of early
child care, but 51% of preschoolers and 30%
of infants and toddlers, irrespective of maternal
employment status, are now in other arrange-
ments (U.S. Bur. Census 2008).

The appropriately labeled “mixed delivery
system” of ECE services and programs in the
United States consists of a haphazard array
of formal and informal arrangements, pro-
grams, and funding streams. The services and
programs span the dual purposes of enabling
parents to work or engage in other activi-
ties and to protect and foster the development
and education of children. These purposes
map onto distinctions between “day care” and
“early education” or “preschool” that endure
despite ample evidence that beneficial out-
comes for children are associated with settings
that provide both sensitive, nurturing adult-
child interactions and support for early learning
and development (Shonkoff & Phillips 2000).

This is not an accident but rather a result of
multiple and conflicting values that have pro-
vided the context within which ECE services
have evolved in the United States. Of particu-
lar significance for understanding the contem-
porary landscape of ECE is the longstanding
conflict between values that urge early interven-
tion, as expressed in the Early Head Start and
Head Start programs, and those that view child-
rearing as a private family matter to be pro-
tected from government intervention (Phillips
1984). Dating back to the mid-twentieth cen-
tury, publicly provided care outside the family
unit has been largely restricted to children of
immigrant, impoverished, and minority fami-
lies (Cohen 2001, Fein & Clarke-Stewart 1973,
Gormley 1995, Steinfels 1973) and, on a tem-
porary basis, during times of national crisis. In
fact, the first federal expenditure on child care
services occurred during World War II, when
President Roosevelt agreed to use funds from
the Lanham Act to help states pay for child care

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centers so that an economy depleted of male
workers, yet in dire need of a wartime work-
force, could employ mothers. By 1948, how-
ever, only California continued to support the
worksite child care programs that had prolifer-
ated as a result of these funds, resulting in the
closure of over 2,000 centers. It was not until
1962, in the context of a welfare law, that the
federal government again earmarked funds for
child care, soon to be followed by the enactment
of the Head Start program in 1965.

The legacy of these early actions is reflected
in the lack of a coherent national ECE policy
in the United States. Public policy for ECE is
highly dispersed across:

� tax benefits, which support a small share
of the child care costs of nonpoor families;

� income support policy in the form of sub-
sidies for welfare-eligible families moving
into the workforce;

� early education policy, which emphasizes
early intervention and school readiness
for low-income children;

� special education policy, which provides
ECE services as part of early intervention
services for children with special needs;

� higher education funds that support the
postsecondary training and education of
ECE providers/teachers; and

� defense policy, through the U.S. Mili-
tary Child Care Act, which provides rela-
tively high-quality child care for military
families.

ECE policy in the United States is also
highly decentralized, with the federal gov-
ernment’s role largely restricted to providing
funds, thus leaving most decisions regarding the
structure and quality of ECE services, eligibility
for these services, and funding priorities (e.g.,
to upgrade quality or serve more families) up
to the states, in effect creating 50 ECE systems.
Both federal and state support remain almost
exclusively directed to families living in or near
poverty, a notably distinct feature of child care
in the United States compared to many other
industrialized countries, where universal ECE
is available starting at age 3 (Kamerman & Kahn
1995, Waldfogel 2006).

THE CURRENT LANDSCAPE OF
EARLY CARE AND EDUCATION

This dispersal of responsibility for ECE across
policy domains and levels of government is
readily apparent in the contemporary landscape
of ECE services. It is scattered with programs
that are identified as providing early education,
school readiness, and intervention services for
children living in poverty, such as Early Head
Start, Head Start, and the growing number of
state prekindergarten (pre-K) programs for 4-
year-olds; those that provide similar services
to children with special needs through the In-
dividuals with Disabilities Education Act; and
those that are linked primarily to supporting
maternal employment within the welfare sys-
tem and thus focus on ensuring an adequate
supply of arrangements with little regard to
the quality of children’s experiences. The sem-
blance of a delivery system is now emerging for
low-income 4-year-olds, consisting of a mix of
Head Start programs and state-funded pre-K
programs, which now exist in 38 states and serve
more children than Head Start serves (Barnett
et al. 2008a). Three-year-olds are beginning to
be embraced by this system as well. The re-
maining, immense challenge concerns the lack
of any comparable ECE delivery system for in-
fants and toddlers.

It remains the case, nevertheless, that most
families with young children, who are neither
poor nor have a child with special needs, ar-
range for the early care and education of their
children privately and with little or no public
assistance in paying for this care. On a regu-
lar basis starting in 1985, the U.S. Census Bu-
reau (2005a,b; 2008) has attempted to capture
“Who’s Minding the Kids?”—the apt title of
its recent reports on ECE arrangements. These
reports make it evident that most families rely
on a patchwork of arrangements over the early
childhood period. These arrangements include
every conceivable combination of care by moth-
ers and fathers, who juggle work and nonwork
schedules to maintain parental care; care by rel-
atives; organized ECE in centers and preschool
settings; and care by nonrelatives, including

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PS62CH18-Phillips ARI 22 November 2010 8:34

nannies and family child care providers, who
care for small groups of children in their homes.
Figure 1 illustrates the regular ECE arrange-
ments that families with employed mothers
used in 2005. Importantly, 22% of parents of
infants and toddlers and 28% of families with
preschoolers relied on more than one arrange-
ment simultaneously to meet their needs. Not
illustrated are the arrangements used by the
29% of nonemployed mothers who also rely
on regular ECE arrangements for their young
children. Accurate portrayals of this patchwork
have eluded researchers, who typically focus on
the primary arrangement, knowing that this re-
sults in a tremendous loss of information on
children’s experiences.

CHILDREN’S EXPERIENCE OF
EARLY CARE AND EDUCATION

Research has directed attention to the amount
of time children spend in ECE settings and the
quality of these settings as the aspects that ac-
count for most of the variation in child impacts.

Time in Care

The most extensive data on the time young
children spend in ECE settings come from
the National Institute of Child Health and
Human Development (NICHD) Study of
Early Child Care and Youth Development
(SECCYD) (NICHD Early Child Care Res.
Netw. 2005a). This 10-site longitudinal study,
which followed 1,364 children born in 1991
from infancy through elementary school, had
as its primary purpose examining how variation
in nonmaternal care and early education
are related to children’s development. The
study protocol involved regularly interviewing
children’s parents about the arrangements they
were relying upon starting at birth. The results
have revealed a portrait of early, extensive, and
uninterrupted (yet with frequent changes in
arrangements) reliance on ECE arrangements.
At the point of entry into ECE (at 3.11 months
of age, on average), infants were enrolled for an
average of 29 hours per week [NICHD Early

Child Care Research Network (ECCRN)
1997a]. This pattern is largely driven by
maternal employment and particularly the
mother’s work hours and her contribution to
family income (NICHD ECCRN 1997b). A
child’s first entry into ECE typically initiates an
uninterrupted history of enrollment in various
arrangements right up to kindergarten entry.

By 3 years of age, children in ECE averaged
34.4 hours in care per week, with 52% spending
30 or more hours on a weekly basis in their ar-
rangement(s). This corresponds closely to the
most recent Census data on hours in care (U.S.
Bur. Census 2005), which indicate that chil-
dren under the age of 5 with employed mothers
spend an average of 36 hours per week in ECE
arrangements, while children whose mothers
are not employed spend an average of 18 hours
per week in such arrangements.

Young children with special needs stand out
from this pattern. They enter child care at older
ages, spend less time in child care, and tend to
be in informal arrangements with relatives to a
greater extent than do typically developing chil-
dren (Booth & Kelly 1998). This is likely the
outcome of a set of factors including the dif-
ficulty of working full time as the mother of a
child with special needs, perceptions that these
children are best cared for by their own par-
ents, and a scarcity of inclusive ECE settings
(Dinnebeil et al. 1998, Warfield & Hauser-
Cram 1996).

Quality of Care

Extensive scholarly and popular materials have
been written about the meaning of “quality” in
the ECE field. The ultimate definition of qual-
ity concerns those features of ECE that fos-
ter positive developmental outcomes. With this
as the criterion, there is fairly wide consensus
about the ingredients of high-quality ECE set-
tings. They are often divided into three tiers:
the child-adult relationship and interactions
(sometimes called “process” quality), structural
features of care, and the surrounding commu-
nity and policy context.

486 Phillips · Lowenstein

PS62CH18-Phillips ARI 22 November 2010 8:34

Adult-child relationship and interactions.
The most proximal and influential aspect of
quality is the nature of the relationship and
interactions that transpire between the adult
caregiver or teacher and the child. Across all
types of settings, young children whose care-
givers provide ample verbal and cognitive stim-
ulation, are sensitive and responsive, and give
children generous amounts of attention and
support are more advanced in all realms of de-
velopment compared to children who fail to re-
ceive these important inputs (Cost, Quality, &
Outcomes Team 1995; Lamb & Ahnert 2006;
NICHD ECCRN 1998a, 2000, 2001; Phillips
et al. 2006). Valid assessment of these facets
of quality entails on-site observations. Some
instruments assess multiple dimensions of the
immediate environment that children experi-
ence, as with the Early Childhood Environment
Rating Scale (Harms & Clifford 1980, 1984).
Some, such as the Classroom Assessment
Scoring System, focus on the overall emo-
tional and instructional climate of the setting
(Pianta et al. 2008). Others, such as the
Observational Record of the Caregiving En-
vironment (NICHD ECCRN 1996), focus on
interactions between the adults and children.

Regardless of the measurement instrument
that is used, the overall portrait of proximal or
“process” quality in the United States is one
of extremely wide variation around a mean that
has been characterized as “mediocre” (Natl. Sci.
Counc. Dev. Child 2007, Shonkoff & Phillips
2000). For example, in the NICHD Study of
Early Child Care (NICHD ECCRN 1996), one
in four infant caregivers was moderately in-
sensitive, only 26% were moderately or highly
stimulating of cognitive development, and 19%
were moderately or highly detached. This is
especially troubling in light of emerging evi-
dence that larger gains in cognitive-academic
outcomes for children in ECE programs ac-
crue when they experience care of high quality,
and that improvements in ECE quality in the
moderate to high range may be needed to yield
long-term measurable impacts (Burchinal et al.
2010, Vandell et al. 2010).

In addition to the one-on-one relationship
that the caregiver/teacher establishes with each
child, her role in fostering positive social inter-
actions among children in group ECE settings
has received growing attention. This is the re-
sult of emerging evidence that the social oppor-
tunities and challenges posed by peer groups
play an important role in how young children
adapt and react to their ECE experiences (Fabes
et al. 2003, Phillips et al. 2010).

Another aspect of this proximal tier of qual-
ity concerns the stability of the ECE workforce.
More stable providers and teachers have been
found to engage in more appropriate, atten-
tive, and engaged interactions with the chil-
dren in their care (Helburn 1995, Howes et al.
1992). Unfortunately, stability is rare in this
field. Turnover rates among ECE providers and
teachers are among the highest of any profes-
sion that is tracked by the U.S. Department of
Labor (U.S. Bur. Labor Statistics 2008), hover-
ing at 30% per year. In the only available lon-
gitudinal study of the center-based ECE work-
force, three-quarters of ECE teachers had left
their jobs after four years (Whitebook & Sakai
2004). It is widely accepted that the low salaries
of this workforce are a major determinant of
high turnover (Whitebook & Bellm 1999). In
2008, for example, the average hourly wage
of a child care worker was $9.73, with an an-
nual wage of $19,264 (U.S. Dept. Labor 2009).
This falls below the hourly wage of animal and
pest control workers, amusement park atten-
dants, hairdressers, and janitors. It also falls be-
low the 2008 poverty threshold of $21,834 for
a family of four with two children. Preschool
teachers fare somewhat better, with an average
hourly wage of $16.19 and annual earnings of
$28,647, but they still earn barely half of what
kindergarten teachers earn ($33.54 hourly, on
average).

Structural features of ECE. The second tier
of quality consists of structural features of ECE
settings that are associated with sensitive and
stimulating adult-child interactions and thus
with improved child outcomes (Love et al. 1996,

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PS62CH18-Phillips ARI 22 November 2010 8:34

NICHD ECCRN 2002). The most commonly
studied features are (a) the experience and edu-
cational backgrounds (amount, degrees, areas of
specialization) of the caregivers/teachers, which
are assumed to capture their professional com-
petence and skill level (as well as their level of
career commitment), (b) the total number of
children in the setting (group size), and (c) the
ratio of children to adults, which captures the
demands on an individual caregiver’s time and
capacity to provide sensitive care and effective
early education. Extensive, but not entirely con-
sistent, evidence documents the role of each of
these features in supporting higher-quality care
for young children. Importantly, it appears that
fairly minor changes in ratios and group sizes
can affect the quality of care that children re-
ceive. For example, infants in centers with ratios
of three or fewer children per caregiver have
been found to receive significantly more sen-
sitive and appropriate care (Howes et al. 1992)
and to score a standard deviation above those in
centers with larger ratios on a measure of com-
munication skills (Burchinal et al. 1996). With
regard to experience and education, there is on-
going debate about the thresholds above which
these qualifications are consistently linked to
higher-quality care.

Community and policy context. The final
tier of quality consists of the broader commu-
nity and policy environment, or macrosystem,
in which ECE services operate in the United
States. Important elements of this environment
include the regulatory and financing structures
that bear on the delivery of ECE services. In
every state some, but by no means all, ECE
programs are required to comply with regu-
lations that establish a floor of quality below
which children’s safety and development are
presumably compromised. States vary widely in
both the stringency and enforcement of these
regulations (e.g., provider-child ratios for in-
fant care currently range from 1:3 to 1:12; most
states permit infants and toddlers to be cared for
by staff who have not completed high school),
and there are no national standards to ensure
even a modicum of consistency (Marsland et al.

2003, Phillips et al. 1990). Yet, children who
attend ECE programs that meet recommended
standards for components that are regulated
(e.g., ratios, group size, provider education)
have higher cognitive and language test scores
(Clarke-Stewart et al. 2002, NICHD ECCRN
2001).

With regard to financing, the majority of
families in the United States do not receive as-
sistance with payments for ECE services de-
spite relatively high costs. As of 2005, the av-
erage family paid $129 weekly (which annual-
izes to $6,708) for all children under 5 years
of age. These payments amount to 29% of
family income for those living in poverty and
6.1% of family income for those living above
the poverty line (U.S. Bur. Census 2008). The
relation between the immediate costs of ECE
and the quality of the services that are pro-
vided is enormously difficult to estimate given
significant contributions of subsidies, in-kind
services, and geographic variation, for exam-
ple. Available data do, however, suggest that
there is a positive, albeit weak, association be-
tween cost and quality and that—at least for
preschool-age ECE—there may be a thresh-
old at a fairly low level of quality above which
costs are higher than they are below this thresh-
old (Levin & Schwartz 2007, Marshall et al.
2004).

EVOLUTION OF RESEARCH ON
EARLY CARE AND EDUCATION

Research on ECE has evolved over time in par-
allel fashion to the evolution of national policy
and debates on this topic. The salience of con-
cerns about maternal deprivation in an initial
wave of research on ECE gave way to a more
ecological approach that increasingly embed-
ded efforts to understand the effects of ECE in
the context of family influences on child devel-
opment. At the same time, a parallel research
literature, grounded in theory about compen-
satory education, focused on the capacity of
ECE programs and services to alter the de-
velopmental trajectories of children living in
poverty.

488 Phillips · Lowenstein

PS62CH18-Phillips ARI 22 November 2010 8:34

Maternal Deprivation Framework

Initially, research on child care was framed in
the context of prior research on maternal de-
privation (McCartney & Phillips 1988, Natl.
Res. Counc. 1990). The central question, in
effect, was whether child care—because it in-
volved maternal separation—constituted a mild
form of orphanage care. The basic methodol-
ogy involved comparing young children who
regularly spent time away from their mothers
in an ECE arrangement to those who did not.
Outcome measurement focused on mother-
child attachment and problem behavior. In
summarizing this era of research, the National
Research Council (1990) concluded that child
care was found to be neither inherently harmful
nor beneficial. The important legacies of this
initial era of research were to direct a second
wave of ECE research beyond simple compar-
isons of children in and not in the full-time care
of their mothers to variation in the ECE envi-
ronments that young children experience, and
to call attention to the serious issue of selection
effects, namely, effects ascribed to ECE that
actually arise from the fact that families select
ECE environments for their children (implying
that “ECE effects” perhaps should be ascribed,
at least in part, to family factors).

Ecological Framework

Not coincidentally, these new foci of ECE
research emerged as ecological models were
becoming prominent within developmental
psychology (Bronfenbrenner 1979, Bronfen-
brenner & Morris 1998). Bronfenbrenner
proposed that the developing child sits at the
center of an ecological web, starting with the
child’s immediate setting, the microsystem, and
working outward in a series of nested contexts
that influence human development in complex
ways. As growing numbers of young children
were beginning to straddle two microsystems—
the family and ECE settings—researchers be-
gan to pay attention to the fact that children’s
ECE experiences are the result of a multitude
of decisions on the part of parents, including

whether to use nonparental care, and if so, the
age at which their children will enter ECE, the
type of ECE setting used, and the amount of
time spent in care. These decisions, in turn, are
a function of a bundle of “selection” factors in-
cluding parental attitudes and values regarding
ECE, family circumstances (such as maternal
employment status and family income), and
demographic characteristics. In an attempt
to understand these selection factors, the
second wave of research on ECE focused on
interrelationships between the family and the
ECE setting, also known as the mesosystem.
Rounding out the ecological model are the
exosystem and macrosystem, the outermost
layers that capture the parents’ worksites,
governmental systems, broad patterns of
ideology, and public policies that influence the
ECE setting and children’s experiences in it.

Among the most important selection factors
is family income. Children from higher-income
families spend more time in nonparental care
than do those from lower-income families
(Capizzano & Main 2005) and are more
likely to be cared for in center-based settings
than are their low-income peers (35% versus
26%) (Capizzano et al. 2000). In contrast,
low-income children are more likely to be in
relative care (28% versus 20%) or parent care
(28% versus 21%) (Capizzano et al. 2000).
Family income is also related to ECE quality,
such that lower income is associated with
lower quality in all types of arrangements
except center-based programs (Galinsky
et al. 1994, NICHD ECCRN 1997b, Phillips
et al. 1994). In center-based arrangements,
lower- and middle-income families receive
lower-quality care than do families living in
poverty or those with high incomes, probably
as a result of access for children living in
poverty (but not others) to public subsidies
and programs such as Early Head Start, Head
Start, and state pre-K programs ( Johnson &
Brooks-Gunn 2010, NICHD ECCRN 1997b,
Ryan et al. 2010). These programs are targeted
to children growing up in poverty and tend to
offer relatively higher-quality care and educa-
tion than other programs that are available and

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PS62CH18-Phillips ARI 22 November 2010 8:34

financially accessible to these children. There
are also racial/ethnic differences in the type
of ECE that parents select for their children,
although these selection effects become quite
small when other demographic characteristics
are controlled (Singer et al. 1998). Black
children are more likely than white or His-
panic children to have center-based care as
their primary child care arrangement (44%),
Hispanic children are least likely to be in
center-based care (20%), and white children
fall in the middle (32%) (Capizzano et al.
2006). Hispanic parents tend to prefer relative
care for their young children.

Initially, scientists attempted to address
the role of selection effects by statistically
controlling for family variables when modeling
the effects of ECE on children’s developmental
outcomes (McCartney 2006). More recently, a
third wave of research on ECE has attempted
to understand, rather than control for, family
factors as they interact with children’s experi-
ences in ECE to affect development. Efforts to
assess family moderators of child care effects
exemplify this third wave of research. Using
an ecological approach to characterize niches
defined by family and child care risk, recent
findings have indicated that a high-quality,
naturally occurring child care or preschool
setting can protect young children from the
negative behavioral (Watamura et al. 2011)
and academic (Burchinal et al. 2010) impacts
of a low-quality home environment (labeled a
compensatory effect). Watamura et al. (2011)
further reported that a high-quality home
environment was consistently protective, but
to a greater extent when children were also in
high-quality child care, reflecting the benefit of
“double protection.” Similarly, Votruba-Drzal
et al. (2004) found that high-quality child
care benefited low-income children’s reading
skill development, but only when coupled
with a stimulating home environment. It is
now widely accepted that understanding child
development involves capturing dynamic inter-
actions, cumulative impacts, and compensatory
mechanisms as they operate across the salient
environments in young children’s lives, of

which the home and ECE settings are typically
the most prominent and influential.

Compensatory Education Framework

Running alongside these waves of research on
child care, a sister literature has examined the
effects of early intervention programs for chil-
dren in poverty, guided by prevailing views
about compensatory education. Interest in the
effects of early intervention in the lives of low-
income children in the United States dates
back to the late 1950s and early 1960s, when
new research suggested that early environ-
mental deprivation led to suboptimal cogni-
tive development (Zigler & Hall 2000, Zigler
& Muenchow 1992) and that early enrichment
programs could counter these negative effects
(Bloom 1964, Hunt 1961). Amid the national
optimism and fiscal prosperity that character-
ized the United States in the early 1960s, early
intervention came to be seen as a means of per-
manently enhancing the development of low-
income children and possibly even wiping out
poverty itself (Zigler & Hall 2000). In con-
trast to child care in the United States, where
the emphasis has historically been on providing
custodial care to children while their parents
work, compensatory early intervention empha-
sizes school readiness and the provision of high-
quality care in an attempt to compensate for
suboptimal home environments.

The Head Start program is perhaps the
greatest legacy of this framework. Begun in
1965, Head Start is a federally funded com-
prehensive child development program whose
goal is to promote the school readiness of
low-income children by providing them with
comprehensive services, including preschool
education; medical, dental, and mental
health care; nutrition services; and parent-
involvement efforts (U.S. Dept. Health Human
Serv. 2010a). Together with Early Head Start,
created in 1994 to serve pregnant mothers
and children from birth to age 3, Head Start
provides comprehensive ECE services to more
than one million low-income children and their
families per year (Natl. Head Start Assoc. 2009).

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Two other model demonstrations of the
compensatory education approach are the
Perry Preschool Project and the Abecedarian
Project, begun in 1962 and 1972, respectively.
These programs were designed as randomized
experiments to assess the impacts of support-
ing the cognitive and socio-emotional devel-
opment of young, at-risk children through the
provision of intensive, high-quality early edu-
cation and family support services. Participants
were followed through adulthood. The posi-
tive, long-term impacts yielded by these pro-
grams have been championed as evidence that
high-quality early education programs can have
long-lasting beneficial effects on low-income
children’s cognitive, academic, and social and
emotional functioning (Campbell et al. 2002,
Schweinhart 2004). However, these carefully
constructed, high-quality, and expensive pro-
grams do not reflect the assortment of scaled-
up ECE programs available to the majority of
low-income families with young children to-
day. Furthermore, demographic changes and
changes in the ECE landscape over the past
40 years—notably, ongoing increases in mater-
nal employment and the growing number of
state pre-K programs focused on low-income
children—have rendered the counterfactuals
for these programs, which consisted largely of
children at home with their mothers, increas-
ingly irrelevant.

THE EFFECTS OF EARLY CARE
AND EDUCATION

Empirical research examining the effects of
ECE experiences on children has developed
along three relatively unconnected strands fo-
cused on child care, Head Start, and state-
funded pre-K programs. Research on child care
has been guided by concerns about whether the
mother-infant relationship will be harmed or
diminished in significance or will have reduced
impacts on children’s social and cognitive de-
velopment. Research on Head Start, in con-
trast, has been guided by efforts to document
the positive impacts of this early intervention
program on children’s development, broadly

defined. The newest line of research on state
pre-K programs shares more in common with
Head Start than with child care research.

Child Care: Implications for the
Parent-Child Relationship

One of the oldest questions about the effects of
child care on children concerns whether time
spent in nonparental care attenuates the effects
of the family on children’s development. The
evidence on this matter is clear: Associations
between family factors and child outcomes do
not differ between children in extensive child
care and those with little to no exposure to child
care (Clark-Stewart et al. 1994, NICHD EC-
CRN 1998b). In response to longstanding con-
cerns about whether time spent in child care
(and, therefore, apart from the mother) might
interfere with mother-child attachment, the ev-
idence is similarly consistent. There is no sig-
nificant, direct effect of child care experience
(quantity, quality, or type) on children’s at-
tachment security at either 15 or 36 months
(NICHD ECCRN 1997c, 2001). Regardless
of early care experience, maternal sensitivity
is the strongest predictor of preschool attach-
ment classification. Significant interactions fur-
ther reveal that infants are less likely to be se-
cure when low maternal sensitivity is combined
with poor-quality child care, extensive hours in
care, or more than one child care arrangement.
These findings support a dual-risk model of de-
velopment (Werner & Smith 1992).

Developmental Effects of Child Care

Efforts to identify the conditions of care that af-
fect children’s development have led to a focus
on the relation between child care quality
and children’s cognitive and language out-
comes, and on the relation between quan-
tity of care and children’s social-emotional
development, especially problem behavior.
With regard to the first question, the Cost,
Quality, and Outcomes (CQO) study of 418
children nested within 176 child care centers
in four states found that high-quality care was

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PS62CH18-Phillips ARI 22 November 2010 8:34

associated with a range of cognitive and lan-
guage outcomes, even after controlling for
family background characteristics such as so-
cial class (Cost, Quality, & Outcomes Team
1995). Similar findings have emerged from the
NICHD Study of Early Child Care.

Quality of child care was consistently but
modestly related to cognitive (e.g., memory,
problem-solving, letter identification, num-
ber/counting) and language outcomes at 15,
24, 36, and 54 months, even after control-
ling for multiple child and family characteris-
tics (NICHD ECCRN 2000, 2002; NICHD
& Duncan 2003). Longer-term outcomes from
both the NICHD and CQO studies provide ev-
idence that child care quality has modest long-
term effects on children’s language ability, math
ability, memory, and attention skills through
kindergarten, and in some cases through the
later elementary and middle-school grades
(Belsky et al. 2007, NICHD ECCRN 2005b,
Peisner-Feinberg et al. 2001, Vandell et al.
2010). Stronger positive effects of child care
quality have sometimes been found for chil-
dren from more at-risk backgrounds (Peisner-
Feinberg et al. 2001). Evidence from studies of
home-based care suggests that variation in the
quality of these settings is also associated with
variation in cognitive and language develop-
ment at 15, 24, and 36 months (Clarke-Stewart
et al. 2002).

The literature on naturally occurring pat-
terns of child care use and children’s socio-
emotional development is characterized by two
conflicting stories. On the one hand, a large
body of research suggests that child care is
detrimental to children’s social development.
On the other hand, there is growing evidence
that child care programs can benefit aspects
of children’s socio-emotional adjustment, espe-
cially when program quality is high, and espe-
cially among children from low-income fami-
lies. The first “negative” pattern of findings has
been borne out most consistently in results of
the NICHD SECCYD. Results from this and
other studies indicate that the more hours chil-
dren spend in nonmaternal care, the more be-
havior problems and conflict with adults they

show at age 2, age 4-1/2, in kindergarten, and
in both elementary and middle school (Belsky
et al. 2007; Côté et al. 2008; Loeb et al. 2007;
NICHD ECCRN 1998a, 2002, 2003, 2005a;
Vandell & Corasaniti 1990; Vandell et al. 2010).

In most cases, the effects remain even af-
ter controlling for child care quality. However,
there is emerging evidence from the NICHD
study that child care quality may moderate the
effect of hours in care on children’s externaliz-
ing behavior. Specifically, child care hours were
found to be more strongly related to external-
izing behavior when children were in low- ver-
sus high-quality care (McCartney et al. 2010).
Recent work using the NICHD sample has
also identified a specific link between the num-
ber of hours spent in center-based care dur-
ing the first 4-1/2 years of life and children’s
teacher-reported behavior problems through
sixth grade (Belsky et al. 2007), although this
finding was not replicated at age 15 (Vandell
et al. 2010). This pattern of findings is con-
sistent with other evidence that long hours in
care are more strongly related to externaliz-
ing behavior when children are in care with
large groups of peers (McCartney et al. 2010).
Children from low-income families show a
somewhat different pattern of findings in which
no negative behavioral effects of center-based
care are found when quality of care is controlled
(Loeb et al. 2004, Votruba-Drzal et al. 2004).
Furthermore, when quality is high, spending
more hours in nonmaternal care actually leads
to decreases in low-income children’s behavior
problems (Votruba-Drzal et al. 2004).

Developmental Effects of Head Start

Since its creation, Head Start has been the sub-
ject of hundreds of studies. These have gener-
ally found that the program has small, short-
term positive effects on children’s cognitive
and social development (e.g., Lee et al. 1990,
Love et al. 2006, McKey et al. 1985, Zill et al.
2003). Only in recent years have social scien-
tists been able to design studies of Head Start
and Early Head Start that can more credibly
identify the programs’ causal impacts on child

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development. Results of the randomized exper-
imental Head Start Impact Study suggest that
the program benefits low-income children’s
cognitive and social development in the short
term but has few longer-term effects. Benefits
to cognitive development extend to assessments
of vocabulary, letter-word identification, pre-
academic skills, and parent-reported emergent
literacy at the end of the program year (U.S.
Dept. Health Human Serv. 2005). Children
who entered as 3-year-olds showed somewhat
stronger impacts, which also included pre-math
skills, than did children who entered as 4-year-
olds. Program participation was also related to
reductions in parent-reported overall problem
behaviors and hyperactivity for 3-year-olds but
not 4-year-olds. Significant effects were quite
modest in size, albeit consistent with other ev-
idence on high-quality programs. However, by
the end of first grade, there were few signifi-
cant impacts of Head Start participation. Chil-
dren who participated in the program as 4-
year-olds displayed significantly higher vocab-
ulary scores than children in the control group,
and those who participated as 3-year-olds per-
formed better on a standardized test of oral
comprehension (U.S. Dept. Health Human
Serv. 2010b). With regard to socio-emotional
outcomes, there was some evidence that the 3-
year-old cohort had closer and more positive re-
lationships with their parents by the end of first
grade, but findings for 4-year-olds were incon-
sistent. Importantly, however, dual-language
learners and children with special needs ben-
efited more from Head Start participation than
did other groups, with significant first-grade
impacts documented for math skills, language
skills (dual-language learners only), and social
skills (children with special needs only).

A number of explanations have been offered
for the lack of long-term effects of participation
in Head Start (Natl. Forum Early Child. Policy
Prog. 2010). First, it appears that the children
in the control group caught up to their peers
in the Head Start treatment group during the
first two years of school, suggesting that chil-
dren’s experiences in school might have con-
tributed to the absence of program impacts at

the end of first grade. Second, the ECE experi-
ences of the children in the treatment and con-
trol groups were much more similar than the
treatment and control conditions in most ran-
domized experiments. About half of 4-year-olds
and 40% of 3-year-olds in the control group
were enrolled in center-based ECE soon after
the study began. Furthermore, one year later,
some of the 3-year-olds in the control group
enrolled in Head Start, which they were free
to do after the initial program year. The more
similar the experiences of children in the treat-
ment and control groups, the less likely it is that
the two groups will differ in their outcomes.
Finally, the quality of Head Start programs in
the study was variable, with fewer than 5% of
4-year-olds in programs that received an “excel-
lent” quality rating. More research is needed to
understand which features of Head Start pro-
grams and classrooms are related to children’s
positive developmental outcomes and how to
improve the quality of these features.

Findings from the equally rigorous, ran-
domized experimental Early Head Start (EHS)
Impact Study suggest that the program has both
short- and longer-term effects on low-income
children’s development. In the short term, EHS
children performed better on measures of cog-
nition, language, and socio-emotional func-
tioning at age 3 than did their peers who did
not receive EHS (Admin. Child. Fam. 2006).
Longer-term results from the age-5 follow-up
reveal that children who participated in formal
ECE programs (i.e., center-based child care,
Head Start, or pre-K) after age 3 showed better
early reading-related skills but also increased
levels of parent-reported aggressive behavior.
However, those who attended EHS as infants
and toddlers before entering formal care dis-
played significantly lower levels of aggression
than did those who did not attend EHS (Admin.
Child. Fam. 2006). In short, children who expe-
rienced both EHS and formal ECE programs
after age 3 received the benefits of EHS and the
improved reading-related skills associated with
formal programs, without the increase in ag-
gressive behavior. Taken together, the research
on Head Start and Early Head Start suggests

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that earlier enrollment in and/or greater expo-
sure to these programs across the early child-
hood years reaps greater benefits.

Developmental Effects of Preschool
and State-Funded Pre-K

A small but burgeoning body of research has
focused on the effects of participation in state-
funded pre-K programs on children’s develop-
mental outcomes and has found a mixture of
positive and negative effects. Each of the studies
discussed below used advanced statistical meth-
ods to address the problem of selection bias.

Gormley et al. (2005) examined the effects
of participation in Tulsa, Oklahoma’s high-
quality, universal pre-K program on children’s
cognitive development by comparing “young”
kindergarten children who just completed pre-
K to “old” pre-K children just beginning pre-
K. Large impacts exceeding those reported for
other state-funded pre-K programs and high-
quality child care programs were found on
standardized tests of early literacy and pre-
math learning. The program benefited chil-
dren from all racial/ethnic groups as well as
children from diverse income brackets. Gorm-
ley and colleagues have also reported positive
impacts of pre-K participation on children’s
social-emotional development in the form of
reduced timidity and enhanced attentiveness in
the classroom (Gormley et al. 2010). In a sepa-
rate analysis focused solely on low-income chil-
dren, as defined by their eligibility for either a
free or reduced-price lunch, Lowenstein et al.
(2009) also found that participation in pre-K
was associated with lower levels of timidity and
higher levels of attentiveness at kindergarten
entry.

Using a sample of more than 5,000 chil-
dren enrolled in state-funded pre-K pro-
grams and the same methodological ap-
proach used by Gormley et al. (2005),
Barnett et al. (2007) estimated the ef-
fects of pre-K participation on children’s
learning at kindergarten entry. They found
evidence of positive effects on language, liter-
acy, and math skills. Effects on print aware-

ness were particularly large, followed by gains
in math and language skills. There was also evi-
dence of state-level variation in program effects.

Evidence from analyses of a nationally rep-
resentative dataset, the Early Childhood Lon-
gitudinal Study-Kindergarten Cohort (Magnu-
son et al. 2007), indicates that participation in
both pre-K and other types of center-based care
(“preschool”), as defined by parents, was asso-
ciated with higher reading and math skills at
school entry, but also increased aggression and
decreased self-control. By the spring of first
grade, the effects on academic skills had largely
disappeared, but the negative behavioral effects
persisted. As in the child care literature, larger
and longer-lasting effects on academic gains
were found for economically disadvantaged
children. Magnuson et al. (2007) also found no
negative socio-emotional effects among public
school children whose pre-K and kindergarten
classrooms were located in the same school (as is
generally the case in Tulsa), a finding that sug-
gests that pre-K programs located in the pub-
lic schools may generate the greatest return on
public investment in early education.

CONCLUSIONS AND
FUTURE DIRECTIONS

Research on the developmental effects of chil-
dren’s experiences with ECE, as provided in
the United States, offers an excellent exam-
ple of a cumulative line of inquiry that has
evolved relatively systematically over a period
of decades. The legacy of this research is clear
documentation that sometimes ECE environ-
ments pose risks to young children, sometimes
they confer benefits, but their impacts are best
understood in conjunction with other potent
influences—notably family resources and the
quality of parental care—on early development.
The driving questions for the vast majority
of this research have been whether and under
what conditions ECE confers risk or protec-
tion. Both the amount of exposure and the qual-
ity of the instructional and social transactions
that form young children’s ECE experiences af-
fect the trajectories they will follow when they

494 Phillips · Lowenstein

PS62CH18-Phillips ARI 22 November 2010 8:34

encounter formal schooling and move into the
middle-childhood years.

Relatively neglected until very recently have
been questions about for whom ECE confers
risk or protection. The frontiers of science in
this area are directed at the following ques-
tions, each of which carries the potential to
advance understanding of the mechanisms by
which ECE experience affects the paths that
children follow toward problematic or promis-
ing futures:

� How do individual differences in neuro-
biologically based responses to stress af-
fect children’s experiences in and devel-
opmental impacts of exposure to ECE
settings? Neurobiological studies have
implicated child care experiences in the
early development of physiological pro-
cesses that govern the regulation of stress
(Geoffroy et al. 2006, Gunnar et al. 2010,
Vermeer & van IJzendoorn 2006). Such
findings point to the importance of con-
sidering physiologically based processes
as mediators of ECE impacts.

� How do individual differences in temper-
ament affect children’s short- and longer-
term responses to variation in ECE ex-
periences? Using notions of biological
sensitivity to context, temperament re-
searchers working in collaboration with
ECE researchers are beginning to iden-
tify children with highly inhibited, so-
cially reticent temperaments as a group
for whom variation in the quality of ECE
settings matters more than it does for
children with other temperamental styles
(Phillips et al. 2010, Pluess & Belsky
2010).

� In light of evidence that children liv-
ing in poverty benefit more than others
from high-quality ECE settings, what can
be learned about the sensitivity of other
vulnerable groups, such as children with
special needs or those who are En-
glish language learners, to these settings?
A logical and important next step will

involve examining the role that more bi-
ologically based indicators of risk (e.g.,
stress reactivity, temperament, special
needs status) play in conjunction with
more environmentally based risk factors
to affect the developmental impacts of
ECE (Phillips et al. 2010).

The evolving landscape of ECE for
preschoolers, and especially the rapid growth
in both state pre-K programs and knowledge
about effective instructional approaches for
young children, has opened up an additional
set of new scientific questions:

� As growing numbers of 4-year-olds spend
time in formal ECE settings that are
explicitly designed to prepare them for
kindergarten entry, a language around is-
sues of “alignment” has emerged in the
ECE field. There is a pressing need to op-
erationalize this construct and assess the
variety of ways in which explicit bridges
can be built across pre-K and kinder-
garten settings to support children’s suc-
cessful transition to school and help them
maintain the academic and social gains
made in pre-K (Bogard & Takanishi
2005).

� Along the same lines, there is a press-
ing need to consider ECE environments
as part of a broader matrix of important
settings in young children’s lives, includ-
ing child welfare agencies and health and
mental health care systems.

� Both Head Start and pre-K programs
have become popular “laboratories” for
implementing and evaluating a new gen-
eration of early intervention strategies.
These initiatives emphasize the integra-
tion of early literacy and social-emotional
curricula as well as support for early self-
regulatory skills (Barnett et al. 2008b,
Bierman et al. 2008, Diamond et al.
2007). New findings in this area shed light
on promising avenues for future research
and effective program design (e.g., Raver
et al. 2009).

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SUMMARY POINTS

1. There is no coherent system of early care and education in the United States, but rather
a mixed delivery system in which different options ranging from next-door neighbors
to school-based pre-K programs are available to and preferred by families with differing
needs and resources, children of different ages, and groups defined by race, ethnicity,
and culture.

2. The typical young child in the United States enters ECE within the first few months of
life, spends the better part of most days there, and changes arrangements fairly frequently
prior to school entry—a portrait that is driven largely by mothers’ employment patterns.

3. The quality of ECE arrangements varies tremendously around an average that has been
repeatedly characterized and documented to be mediocre with regard to their capacity
to promote positive developmental outcomes.

4. Research on child care has focused on how various features, notably the timing and
amount of exposure, type and stability of care, and level of quality, affect the typical
course of development.

5. Results suggest that sometimes ECE experiences pose risks to young children, sometimes
they confer benefits, but most often they play a less powerful—albeit significant and
cumulative—role in the context of family influences. Indeed, it is now widely recognized
that ECE effects are most appropriately studied in interaction with family effects on early
development.

6. Future research on ECE should explore the contribution of individual differences among
children (e.g., in temperamental styles, stress reactivity, special needs status), include
measurement of regulatory capacities and stress responses, include children with spe-
cial needs, examine ECE-elementary school alignment, and continue work that embeds
promising early childhood interventions within a range of ECE settings.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.

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500 Phillips · Lowenstein

PS62CH18-Phillips ARI 22 November 2010 8:34

Figure 1
Early care and education arrangements of children of employed mothers (2005). Totals sum to more than
100% due to inclusion of children in more than one arrangement. Charts do not include children who are
not in any regular arrangement (12% of infants and toddlers and 10.7% of preschoolers). Source: U.S.
Bureau of the Census. 2008. Who’s Minding the Kids? Child Care Arrangements: Spring 2005 Detailed Tables.
Washington, DC: U.S. Dept. Commerce.

www.annualreviews.org • Early Care and Education C-1

PS62-FrontMatter ARI 15 November 2010 17:50

Annual Review of
Psychology

Volume 62, 2011 Contents

Prefatory

The Development of Problem Solving in Young Children:
A Critical Cognitive Skill
Rachel Keen � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Decision Making

The Neuroscience of Social Decision-Making
James K. Rilling and Alan G. Sanfey � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �23

Speech Perception

Speech Perception
Arthur G. Samuel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Attention and Performance

A Taxonomy of External and Internal Attention
Marvin M. Chun, Julie D. Golomb, and Nicholas B. Turk-Browne � � � � � � � � � � � � � � � � � � � � � �73

Language Processing

The Neural Bases of Social Cognition and Story Comprehension
Raymond A. Mar � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 103

Reasoning and Problem Solving

Causal Learning and Inference as a Rational Process:
The New Synthesis
Keith J. Holyoak and Patricia W. Cheng � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 135

Emotional, Social, and Personality Development

Development in the Early Years: Socialization, Motor Development,
and Consciousness
Claire B. Kopp � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 165

Peer Contagion in Child and Adolescent Social
and Emotional Development
Thomas J. Dishion and Jessica M. Tipsord � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 189

PS62-FrontMatter ARI 15 November 2010 17:50

Adulthood and Aging

Psychological Wisdom Research: Commonalities and Differences in a
Growing Field
Ursula M. Staudinger and Judith Glück � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 215

Development in the Family

Socialization Processes in the Family: Social and
Emotional Development
Joan E. Grusec � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 243

Psychopathology

Delusional Belief
Max Coltheart, Robyn Langdon, and Ryan McKay � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 271

Therapy for Specific Problems

Long-Term Impact of Prevention Programs to Promote Effective
Parenting: Lasting Effects but Uncertain Processes
Irwin N. Sandler, Erin N. Schoenfelder, Sharlene A. Wolchik,

and David P. MacKinnon � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 299

Self and Identity

Do Conscious Thoughts Cause Behavior?
Roy F. Baumeister, E.J. Masicampo, and Kathleen D. Vohs � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 331

Neuroscience of Self and Self-Regulation
Todd F. Heatherton � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 363

Attitude Change and Persuasion

Attitudes and Attitude Change
Gerd Bohner and Nina Dickel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 391

Cross-Country or Regional Comparisons

Culture, Mind, and the Brain: Current Evidence and Future Directions
Shinobu Kitayama and Ayse K. Uskul � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 419

Cognition in Organizations

Heuristic Decision Making
Gerd Gigerenzer and Wolfgang Gaissmaier � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 451

Structures and Goals of Educational Settings

Early Care, Education, and Child Development
Deborah A. Phillips and Amy E. Lowenstein � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 483

Contents vii

PS62-FrontMatter ARI 3 November 2010 10:34

Psychophysiological Disorders and Psychological Dimensions
on Medical Disorders

Psychological Perspectives on Pathways Linking Socioeconomic Status
and Physical Health
Karen A. Matthews and Linda C. Gallo � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 501

Psychological Science on Pregnancy: Stress Processes, Biopsychosocial
Models, and Emerging Research Issues
Christine Dunkel Schetter � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 531

Research Methodology

The Development of Autobiographical Memory
Robyn Fivush � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 559

The Disaggregation of Within-Person and Between-Person Effects in
Longitudinal Models of Change
Patrick J. Curran and Daniel J. Bauer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 583

Thirty Years and Counting: Finding Meaning in the N400
Component of the Event-Related Brain Potential (ERP)
Marta Kutas and Kara D. Federmeier � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 621

Indexes

Cumulative Index of Contributing Authors, Volumes 52–62 � � � � � � � � � � � � � � � � � � � � � � � � � � � 000

Cumulative Index of Chapter Titles, Volumes 52–62 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 000

Errata

An online log of corrections to Annual Review of Psychology articles may be found at
http://psych.AnnualReviews.org/errata.shtml

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ANRV398-PS61-25 ARI 5 November 2009 14:23

The Psychology
of Academic Achievement
Philip H

Winne and John C. Nesbit
Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
email: winne@sfu.ca, jcnesbit@sfu.ca

Annu. Rev. Psychol. 2010. 61:653–78

First published online as a Review in Advance on
October 19, 2009

The Annual Review of Psychology is online at
psych.annualreviews.org

This article’s doi:
10.1146/annurev.psych.093008.100348

0066-4308/10/0110-0653$20.00

Key Words

school learning, educational psychology, motivation, metacognition,
experimental methodology, self-regulated learning

Abstract
Educational psychology has generated a prolific array of findings about
factors that influence and correlate with academic achievement. We re-
view select findings from this voluminous literature and identify two do-
mains of psychology: heuristics that describe generic relations between
instructional designs and learning, which we call the psychology of “the
way things are,” and findings about metacognition and self-regulated
learning that demonstrate learners selectively apply and change their
use of those heuristics, which we call the psychology of “the way learn-
ers make things.” Distinguishing these domains highlights a need to
marry two approaches to research methodology: the classical approach,
which we describe as snapshot, bookend, between-group experimen-
tation; and a microgenetic approach that traces proximal cause-effect
bonds over time to validate theoretical accounts of how learning gen-
erates achievements. We argue for fusing these methods to advance a
validated psychology of academic achievement.

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Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . 654
COGNITIVE FACTORS . . . . . . . . . . . . 655

The Example of Cognitive Load . . . . 655
METACOGNITIVE FACTORS . . . . . . 657
MOTIVATIONAL FACTORS . . . . . . . . 659

Achievement Goals . . . . . . . . . . . . . . . . 659
Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . 660
Epistemic Beliefs . . . . . . . . . . . . . . . . . . . 661

CONTEXT FACTORS . . . . . . . . . . . . . . 661
Peer-Supported Learning . . . . . . . . . . 661
Classrooms and Class Size . . . . . . . . . . 663
Homework . . . . . . . . . . . . . . . . . . . . . . . . 664
Socioeconomic Status . . . . . . . . . . . . . . 666

PERSISTENT DEBATES . . . . . . . . . . . . 666
Learning and Cognitive Styles . . . . . . 666
Discovery Learning . . . . . . . . . . . . . . . . 667

METHODOLOGICAL ISSUES
IN MODELING A
PSYCHOLOGY OF
ACADEMIC ACHIEVEMENT . . . 669
Paradigmatic Issues . . . . . . . . . . . . . . . . 669
A Revised Paradigm . . . . . . . . . . . . . . . . 671

SHAPES FOR FUTURE
RESEARCH . . . . . . . . . . . . . . . . . . . . . . 671

INTRODUCTION

“Extensive” significantly understates the scope
of research relevant to a psychology of academic
achievement. Not having examined all relevant
books, chapters, proceedings, and articles—a
task we estimate might require three decades
of full-time work—we nonetheless posit it is
possible to develop a unified account of why,
how, and under what conditions learners suc-
ceed or fail in school. That account could lead to
powerful theories about improving educational
practices. Advancing toward such a model is our
aim here although, necessarily, much has been
omitted from our review. Like all models, our
model will have limitations.

The model we sketch acknowledges two cat-
egories of psychological phenomena. The first
concerns a psychology of “the way things are.”
By this we mean psychological phenomena that,

in principle, are universal among learners and
across subject areas and are not likely under
learners’ control. One example is that cognition
can simultaneously manage only a limited num-
ber of tasks or chunks of information. Another is
that learners express biases that can be shaped
by information in their environment. This is
the framing effect. A third is that information
studied and then immediately restudied will be
recalled less completely and less accurately than
if restudying is delayed.

The second category concerns a psychology
of “the way learners make things.” In this cat-
egory we consider learners as agents. Agents
choose among tasks and among psychological
tools for working on tasks. An example is decid-
ing whether to prepare for an exam by massed
or spaced review. Another example is deciding
whether and how long to try retrieving infor-
mation when it can’t be found but there is a
feeling of knowing it. If learners have knowl-
edge of several mnemonic techniques for re-
calling information, they can choose among
those mnemonics. If a first choice fails but
strengthens the feeling of knowing, learners can
metacognitively monitor what they did to make
an informed choice about the next mnemonic
technique to try. They have the option to in-
terpret success and failure as due to effort or
ability. When these choices are made and acted
on, new information is created and feeds for-
ward. In this way, learners shape their learning
environment.

Is it important to distinguish between psy-
chologies of the way things are and the way
learners make things? In his recent review of
research on memory, Roediger (2008, p. 247)
wrote: “The aim of this review has been to re-
mind us of the quest for laws and the difficulty
in achieving them. . . . The most fundamental
principle of learning and memory, perhaps its
only sort of general law, is that in making any
generalization about memory one must add that
‘it depends.’” We suggest Roediger’s lament
may derive from failing to incorporate our dis-
tinction. While one significant source of vari-
ance in the psychology of academic achieve-
ment is due to the way things are, a second

654 Winne · Nesbit

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significant source of variance originates in the
psychology of the way learners make things. We
argue that a psychology of academic achieve-
ment must account for how each psychology
separately and jointly affects achievement.

Our account of the psychology of academic
achievement also borrows a view presented by
Borsboom et al. (2003). In brief, they argue
and we agree that both kinds of psychology
have been hampered, even misled, by failing to
address proximal psychological processes. We
consider questions about psychological pro-
cesses that are shaped and constrained by how
things are, and about processes that provide
tools with which learners make things. In our
account, we portray academic achievement as
the result of self-regulated learning and argue
that improving research entails rethinking con-
structs and the paradigm that guides experi-
mental research.

COGNITIVE FACTORS

Since the publication of Thorndike’s (1903)
classic book Educational Psychology, the field has
generated thousands of studies. Most investi-
gated how environmental factors can be de-
signed and how conditions within learners can
be arranged to promote learning facts, princi-
ples, skills, and schemas. Recently, a consortium
of approximately 35 eminent researchers (see
http://psyc.memphis.edu/learning/index.
shtml) summarized from this voluminous
library 25 empirically grounded heuristics for
instructional designs (see Table 1).

Intending no slight to the range of work con-
tributing to each heuristic, we choose cognitive
load theory to epitomize the category of a psy-
chology describing “the way things are.”

The Example of Cognitive Load

The construct of cognitive load has proven a
powerful explanatory device for spanning the
oft-cited gap between a science of learning
and the arts of teaching and instructional de-
sign. Sweller (1988) developed cognitive load
theory from models of working memory (e.g.,

Baddeley & Hitch 1974) that emphasized the
limited capacity of working memory as a fun-
damental resource bottleneck in cognition.
Vis-à-vis instruction, cognitive load is the total
processing required by a learning activity. It has
three components. First, intrinsic load is due to
the inherent difficulty of an instructional task.
It is indexed by the number of active interact-
ing schemas needed to perform the task. Intrin-
sic load cannot be directly reduced by manip-
ulating instructional factors. However, as the
learner forms schemas and gains proficiency,
intrinsic load decreases. Second, germane load
arises from the cognitive processing that forms
those schemas and boosts proficiency. Third,
extrinsic cognitive load is any unnecessary pro-
cessing. This load can be eliminated by manip-
ulating instructional factors.

The three forms of cognitive load are addi-
tive; their sum cannot exceed working memory’s
limited capacity (Paas et al. 2003a). Intrinsic
processing receives priority access to working
memory. Remaining capacity is shared between
germane and extrinsic processing. When total
load is less than available capacity, an instruc-
tional designer, teacher, or learner can deliber-
ately increase germane load to increase learning
efficiency. Changing instructional factors may
reduce extrinsic load. If working memory ca-
pacity is fully loaded, this can free resources
for germane processing and ultimately produce
more efficient learning. Total cognitive load has
been measured by real-time recordings of per-
formance and psychophysiological indices. It is
most commonly gauged by self-report ratings
collected after the task (Paas et al. 2003b).

Cognitive load is now liberally cited as an
explanatory construct in research ranging over
chemistry problem solving (Ngu et al. 2009),
moral reasoning (Murphy et al. 2009), driver
performance (Reyes & Lee 2008), and even
motherhood (Purhonen et al. 2008). When
cited by researchers outside the learning sci-
ences, the tripartite nature of cognitive load is
typically disregarded.

Reducing extraneous cognitive load links to
several heuristics in Table 1. It is the primary
theoretical grounding for improving learning

www.annualreviews.org • The Psychology of Academic Achievement 655

ANRV398-PS61-25 ARI 5 November 2009 14:23

Table 1 Twenty-five heuristics for promoting learninga

Contiguity effects Ideas that need to be associated should be presented contiguously in space and time.
Perceptual-motor grounding Concepts benefit from being grounded in perceptual motor experiences, particularly at early

stages of learning.
Dual code and multimedia effects Materials presented in verbal, visual, and multimedia form richer representations than a single

medium.
Testing effect Testing enhances learning, particularly when the tests are aligned with important content.
Spacing effect Spaced schedules of studying and testing produce better long-term retention than a single

study session or test.
Exam expectations Students benefit more from repeated testing when they expect a final exam.
Generation effect Learning is enhanced when learners produce answers compared to having them recognize

answers.
Organization effects Outlining, integrating, and synthesizing information produces better learning than rereading

materials or other more passive strategies.
Coherence effect Materials and multimedia should explicitly link related ideas and minimize distracting

irrelevant material.
Stories and example cases Stories and example cases tend to be remembered better than didactic facts and abstract

principles.
Multiple examples An understanding of an abstract concept improves with multiple and varied examples.
Feedback effects Students benefit from feedback on their performance in a learning task, but the timing of the

feedback depends on the task.
Negative suggestion effects Learning wrong information can be reduced when feedback is immediate.
Desirable difficulties Challenges make learning and retrieval effortful and thereby have positive effects on long-term

retention.
Manageable cognitive load The information presented to the learner should not overload working memory.
Segmentation principle A complex lesson should be broken down into manageable subparts.
Explanation effects Students benefit more from constructing deep coherent explanations (mental models) of the

material than memorizing shallow isolated facts.
Deep questions Students benefit more from asking and answering deep questions that elicit explanations (e.g.,

why, why not, how, what-if ) than shallow questions (e.g., who, what, when, where).
Cognitive disequilibrium Deep reasoning and learning is stimulated by problems that create cognitive disequilibrium,

such as obstacles to goals, contradictions, conflict, and anomalies.
Cognitive flexibility Cognitive flexibility improves with multiple viewpoints that link facts, skills, procedures, and

deep conceptual principles.
Goldilocks principle Assignments should not be too hard or too easy, but at the right level of difficulty for the

student’s level of skill or prior knowledge.
Imperfect metacognition Students rarely have an accurate knowledge of their cognition, so their ability to calibrate their

comprehension, learning, and memory should not be trusted.
Discovery learning Most students have trouble discovering important principles on their own, without careful

guidance, scaffolding, or materials with well-crafted affordances.
Self-regulated learning Most students need training in how to self-regulate their learning and other cognitive

processes.
Anchored learning Learning is deeper and students are more motivated when the materials and skills are anchored

in real-world problems that matter to the learner.

a Reproduced from http://psyc.memphis.edu/learning/whatweknow/index.shtml. An elaborated description of each principle plus citations identifying
empirical support is available as 25 Learning Principles to Guide Pedagogy and the Design of Learning Environments. Retrieved Jan. 2, 2009 from http://psyc.
memphis.edu/learning/whatweknow/25principles .

656 Winne · Nesbit

ANRV398-PS61-25 ARI 5 November 2009 14:23

by eliminating unnecessary information (co-
herence), cueing learners’ attention (signaling),
colocating items to be mentally integrated (spa-
tial contiguity), and synchronizing events to
be mentally integrated (temporal contiguity)
(Mayer 2005).

Laboratory tasks designed to elevate cogni-
tive load are reported by learners to feel more
difficult (Paas et al. 2003b). From this, we as-
sume the state of working memory overload
is consciously experienced. Thus, it is within
the purview of metacognition. Students can
avoid overload by segmenting complex tasks for
sequential work or using external mnemonics
such as notes or diagrams. The cost of adopting
learning tactics is initially experienced as added
difficulty. But this investment can pay off in the
long run.

METACOGNITIVE FACTORS

Flavell (1971) is credited with motivating psy-
chologists to research the “intelligent moni-
toring and knowledge of storage and retrieval
operations—a kind of metamemory, perhaps”
(p. 277). He succeeded wildly. Since then, the
broader topic of metacognition—cognition fo-
cused on the nature of one’s thoughts and one’s
mental actions, and exercising control over
one’s cognitions—has generated a body of work
that merits its own Handbook of Metacognition in
Education (Hacker et al. 2009).

Metacognition is basically a two-step event
with critical features. First, learners monitor
features of a situation. They may monitor their
knowledge, whether a peer or resource can pro-
vide information, and possible consequences
if they make a particular move in solving a
problem. The metacognitive account of the
situation is determined by what the learner
perceives, which may differ from its actual qual-
ities. Monitoring compares those perceived
features to standards set by the learner. Often,
these are linked to but not necessarily identical
to standards indicated by a teacher, parent, or
peer. Second, based on the profile of differences
between the learner’s perception of the situa-
tion and standards—which differences there are

and how large they are—the learner exercises
control. The learner may choose to stay the
prior course at a task’s midpoint, adapt slightly
or significantly, or exit the task to pursue
something else. Together, these steps set the
stage for self-regulated learning, a potentially
ubiquitous activity (Winne 1995).

Learners are considered agents. This means
they choose whether and how to engage in
tasks. But learners are not omnipotent. Nor are
they insulated from their cerebral and the ex-
ternal worlds. Agency is reciprocally governed:
As learners change their local environment, the
environment’s web of causal factors modulates
affordances available to them (Martin 2004).
For example, having monitored a problem’s
statement and classified it as solvable, inher-
ent spreading activation in memory may render
information that the problem is difficult. This
may arouse anxiety. Seeking information from
a peer may return a reply that warrants a pos-
itive attribution to effort. Or, it may generate
a negative view that success can’t be achieved
without help from others. Some information
the environment provides (e.g., by spreading
activation) is not controllable, whereas other
information (e.g., the affect associated with a
peer’s assessment) can be at least partially the
learner’s choice.

Given this account, four metacognitive
achievements can be identified: (a) alertness to
occasions to monitor, (b) having and choos-
ing useful standards for monitoring, (c) accu-
racy in interpreting the profile generated by
monitoring, and (d ) having and choosing use-
ful tactics or strategies. After setting the stage to
reach subject matter achievements by develop-
ing these metacognitive skills, two further steps
are required: (e) being motivated to act and ( f )
modifying the environment or locating oneself
in an environment that affords the chosen ac-
tion (Winne & Nesbit 2009).

Alertness to occasions appropriate to
metacognitive monitoring has not been much
researched beyond studies of readers’ capabili-
ties to detect superficial (e.g., spelling) or mean-
ingful errors in texts. In this limited domain,
detecting errors is proportional to measures of

www.annualreviews.org • The Psychology of Academic Achievement 657

ANRV398-PS61-25 ARI 5 November 2009 14:23

prior achievement and inversely proportional to
load on working memory (Oakhill et al. 2005,
Walczyk & Raska 1992). The former suggests
that standards used in monitoring derive from
prior knowledge, similar to what learners use
to construct a situation model for new infor-
mation (Kintsch 1988). The latter reflects that
working memory’s resources play a ubiquitous
role in the economy of information processing.

Learners may struggle to assimilate use-
ful standards and apply them in monitoring.
Beyond simplistic misperceptions about what
counts when assignments are graded, learners
may focus on information at the wrong grain
size. They may judge work at a global level
when more-specific targets or items should be
the standard (Dunlosky et al. 2005).

Research on learners’ accuracy of metacog-
nitive monitoring has blossomed under the
rubric of judgments of learning. It is rooted in
the concept of feeling of knowing (Hart 1965),
a belief that information is in memory although
it cannot be retrieved. There are four main
findings. First, learners are poor at monitoring
learning and have a bias toward overconfidence
(Maki 1998). Second, engaging with informa-
tion in meaningful ways, such as generating a
summary of a large amount of information, can
improve accuracy (see Thomas & McDaniel
2007). Third, accuracy improves by delaying
monitoring so that learners experience recall (or
lack of it) rather than just scan residual informa-
tion in working memory (Koriat 1993, Nelson
& Dunlosky 1991, Thiede et al. 2005). Fourth,
after experiencing difficulty in recall, judgments
shift from being overconfident to the oppo-
site, dubbed the “underconfidence with practice
effect” (Koriat et al. 2002).

Relatively much more research is avail-
able about tools learners have for exercising
metacognitive control. These tools, commonly
termed metacognitive skills or learning strate-
gies, vary widely and are researched using two
common experimental formats. The first trains
learners to competence in a tactic and then
compares pretraining performance to post-
training performance. The second compares
trained learners to a group not trained in the

tactic. Early studies investigated very specific
learning tactics, such as whether young children
could verbally mediate how they learned asso-
ciations when rules governing associative pairs
changed (Kendler et al. 1972). At the other end
of this continuum, Dansereau and colleagues
(see Dansereau 1985) trained undergraduates
in a typology of strategies summarized by the
acronym MURDER: set mood, understand
requirements of a task, recall key features of
task requirements, detail (elaborating) main
ideas studied, expand information into orga-
nized forms (e.g., an outline), and review. In a
semester-long course, students showed statisti-
cally detectable but modest benefits when using
MURDER (Dansereau et al. 1979). Other
research investigated various methods for
engaging learners with information and pro-
viding opportunities to monitor (see Thomas
& McDaniel 2007), including deciding when
to stop initial study and when to restudy (see
Rohrer & Pashler 2007), self-questioning
(Davey & McBride 1986), and summarizing
information in keyword (Thiede et al. 2003) or
prose form (Thiede & Anderson 2003).

Haller et al. (1988) meta-analyzed 20 stud-
ies on the effects of metacognitive instruction
on reading comprehension. The average ef-
fect size was 0.72. Hattie and colleagues (1996)
meta-analyzed 51 newer studies in reading and
other subject areas. The average effect sizes
due to training in cognitive or metacognitive
skills were 0.57 on performance, 0.16 on study
skills expertise, and 0.48 on positive affect.
Because comparison groups typically represent
“business as usual” conditions, two corollaries
are warranted: Learners don’t naturally learn
metacognitive skills to an optimum level, and
schooling does not sufficiently remedy this dis-
advantage. Findings show training has immedi-
ate benefits, but they leave unanswered a critical
question: Do positive effects of training persist
and transfer?

Dignath et al. (2008) meta-analyzed re-
search investigating whether primary school
children could be trained to use theoreti-
cally more effective forms of self-regulated
learning than they had developed themselves

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and, if so, whether training benefited reading,
writing, mathematics, science, other areas
of academic performance, attributions, self-
efficacy, and metacognitive strategies. Overall,
various kinds of training in self-regulated learn-
ing produced a weighted effect size of 0.69. But
there were two notable issues. First, results were
quite variable. Second, the research was overly
dependent on self-reports about psychological
events such as metacognition and uses of learn-
ing tactics.

Metacognition is not “cold”—affect and
motivationally “hot” variables interact, includ-
ing attributions (Hacker et al. 2008), goal
orientations (Vrugt & Oort 2008), epistemo-
logical beliefs (Pieschl et al. 2008), and self-
efficacy. The picture here is complex and incon-
sistent, in part because learners’ self-reports of
motivation may not correspond to choices they
make to study (Zhou 2008). A broader model
of metacognition is needed.

MOTIVATIONAL FACTORS

Motivation is conceptualized as a factor that
influences learning. It also is an outcome of
learning sought for its own sake. As an influ-
ence, motivation divides into two broad cate-
gories: factors that direct or limit choices for
engagement—choosing to study history for in-
terest but mathematics out of necessity, and
factors that affect intensity of engagement—
trying hard versus barely trying. As an outcome,
motivations concern satisfaction or some other
inherent value.

The vast span of theories and empiri-
cal work on motivational factors and aca-
demic achievement was surveyed, in part, by
Covington (2000) and Meece et al. (2006).
Both reviews emphasized research on motiva-
tion arising from goal-orientation frameworks,
so we briefly update that topic before turning
to other issues.

Covington (2000) divided the field into two
sectors grounded in Kelly’s (1955) distinction
between (a) motives as drives, “an internal state,
need or condition that impels individuals to-
ward action” (p. 173) and (b) motives as goals,

where “actions are given meaning, direction,
and purpose by the goals that individuals seek
out, and. . . the quality and intensity of behav-
ior will change as these goals change” (p. 174).
As Covington noted, this distinction can be
arbitrary because the same behavior can be
conceived as reflecting both forms.

We scan three main areas of contempo-
rary research, acknowledging that others are
omitted. Our choices reflect a judgment about
the intensity of recent work in educational
psychology and fit our view of learners as
self-regulating.

Achievement Goals

Achievement goals describe what learners ori-
ent to when learning, particularly the instru-
mental role of what is learned. The main re-
search question has been whether achievement
goals existing before learning is engaged corre-
late with levels or types of learning. The reviews
by Covington (2000) and Meece et al. (2006)
provide ample evidence that different goals cor-
relate variously with outcomes.

A more interesting issue for self-regulated
learning is whether achievement goals shape
or constrain activities learners choose as they
strive for goals. According to this view, goals
play the role of standards for metacogni-
tively monitoring situations—a task or the
classroom—to classify them in terms of options
for behavior. For example, students holding
mastery approach goals, defined as intentions
to deeply and thoroughly comprehend a sub-
ject, may judge that a situation affords oppor-
tunity to substantially extend expertise. In con-
trast, learners with performance approach goals
may classify that same situation (as an observer
determines sameness) as offering excellent
chances to prove competence to others. Because
of their differing classifications, these learners
may exercise metacognitive control to choose
very different tactics for learning (e.g., Dweck
& Master 2008, Kolic-Vehovec et al. 2008, Miki
& Yamauchi 2005, Pintrich & De Groot 1990).

This line of research faces several chal-
lenges. First, learners are not unidimensional

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in their goal orientations (Pintrich 2000), so
bindings between goal orientations and learn-
ing events are correspondingly complicated.
Second, self-reports have been almost the
only basis for researchers to identify goal ori-
entation(s) (cf. Zhou 2008). One-time self-
reports about adopted goals have some inherent
validity—learners’ declarations are what they
are. But goals may be unstable, and the task’s
context may differ from the survey’s context
(Dowson et al. 2006). Like goal orientations,
self-reports are almost the only data gathered
to reflect tactics that learners use in learning.
These self-reports also are contextually sensi-
tive (Hadwin et al. 2001) and may not be trust-
worthy accounts of tactics learners actually use
during study ( Jamieson-Noel & Winne 2003,
Winne & Jamieson-Noel 2002).

Together, these challenges weaken prior
accounts about how goal orientations lead
to choices of learning tactics that directly
raise achievement. In addition to develop-
ing performance-based measures, gaining ex-
perimental control over goal orientation is a
promising strategy for advancing research in
this area (Gano-Overway 2008).

Interest

Interest predicts choices that learners make
about where and how intensely to focus atten-
tion; whether to engage in an activity; and the
intensity of, concentration on, or persistence
in that engagement. Interest also describes a
psychological state of positive affect related to
features a learner perceives about the environ-
ment. Following a revival of research on interest
and learning in the early 1990s (Renninger et al.
1992), two main forms of interest have been
differentiated. Individual interest captures the
predictive quality of interest, as in “I’m inter-
ested in science.” Situational interest arises ei-
ther from an opportunistic interaction between
a person and features of the transient environ-
ment or because a learner exercises volition to
create a context that is interesting.

Krapp (2005) reviewed research supporting
a model that interest arises because learners

experience feedback as they work. His model
echoes Dewey’s (1913) notion that a fusion of
productive cognition and positive affect abets
interest. Specifically, when feedback about task
engagement supports a view of oneself as com-
petent, agentic, and accepted by others, the
task and its method of engagement acquire a
degree of interest. Future tasks can be moni-
tored for similar qualities, and the learner ac-
cordingly regulates future perceptions as well as
engagement.

Research on interest documents that when
a situation is monitored to match a priori in-
terest, learners choose that situation, persist,
and report positive affect as expected. As a con-
sequence of persistence, learners usually learn
more (Ainley et al. 2002). However, interest
can debilitate when it leads learners to regulate
learning by allocating more or more-intense
cognitive processing to less-relevant but inter-
esting content (Lehman et al. 2007, Senko &
Miles 2008).

Interest dynamically interacts in complex
ways with other variables that mediate the ef-
fects of interest and interest itself. A tiny sam-
ple of the roll call of these variables follows.
Prior interest (Randler & Bogner 2007), prior
knowledge, and the structure of knowledge in
the domain (Lawless & Kulikowich 2006) all
increase achievement and correlate with higher
interest. Mastery goals and values attributed to
tasks regarding their future utility and enjoy-
ment (Hulleman et al. 2008) predict higher in-
terest but not necessarily higher achievement.
Self-concept of ability (Denissen et al. 2007)
positively correlates with interest and medi-
ates achievement. Need for cognition (Dai &
Wang 2007) does the same. To this list we add
self-monitoring and regulation, which we the-
orize increase students’ sense of task-specific
agency and consequently interest (Goddard &
Sendi 2008). Given the centrality of teachers’
and parents’ concerns about students’ inter-
ests in school topics and tasks, this tangle of
findings begs for order. Some order might be
achieved by applying Occam’s razor to coalesce
an overabundance of currently differentiated
variables.

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Epistemic Beliefs

Epistemic beliefs describe views a learner holds
about features that distinguish information
from knowledge, how knowledge originates,
and whether and how knowledge changes. Two
studies sparked an explosion of research in this
area. The first was Perry’s (1970) longitudi-
nal study of undergraduates’ developing views
of these topics. The second was Schommer’s
(1990) extension of Ryan’s (1984) study, show-
ing that epistemic beliefs moderated compre-
hension of text.

A general conclusion is that epistemic be-
liefs predict interactions: When information
is complex and probabilistic and its applica-
tion in tasks cannot be definitively prescribed—
when a task is ill-structured—learners who hold
less well developed and less flexible epistemic
beliefs recall, learn, argue, and solve prob-
lems less well than do peers with better devel-
oped snd more flexible epistemic beliefs (e.g.,
Mason & Scirica 2006, Stathopoulou &
Vosniadou 2007). But when tasks and informa-
tion are not ill structured, holding sophisticated
epistemological beliefs can interfere with re-
call and comprehension (Bräten et al. 2008). In
short, match of aptitude to task matters.

Muis (2007) synthesized theory and research
on epistemic beliefs and self-regulated learning.
She offered four main conclusions. First, learn-
ers observe features of tasks that reflect epis-
temic qualities (Muis 2008). Second, they use
these perceptions to set goals and frame plans
for accomplishing work. Third, as work on a
task proceeds, learners use epistemic standards
to metacognitively monitor and regulate learn-
ing processes (Dahl et al. 2005). Last, engag-
ing in successful self-regulated learning can al-
ter epistemic beliefs, specifically, toward a more
constructivist stance (Verschaffel et al. 1999).

CONTEXT FACTORS

Peer-Supported Learning

Peer-supported learning encompasses collabo-
rative, cooperative, and small-group arrange-
ments in dyads or groups of up to about six

members. It is theorized to offer multiple so-
cial, motivational, behavioral, metacognitive,
and academic benefits. O’Donnell (2006) ob-
served that the varied models of peer-supported
learning are founded on theories emphasizing
sociomotivational or cognitive aspects of the
collaborative process.

Sociomotivationally grounded approaches
to cooperative learning highlight the role of
positive interdependence among group mem-
bers and individual accountability of each mem-
ber. These approaches lead to forming groups
that are heterogeneous in ability, gender, and
ethnicity, and suggest teachers set goals that
require students to work together. For example,
Slavin (1996) developed types of cooperative
learning in which the whole group is rewarded
for each of its members’ gains in performance,
thus incentivizing mutual support for learning
within the group. In what he called the social
cohesion approach (e.g., Johnson & Johnson
1991), small groups work on developing social
skills, concern for others, and giving productive
feedback and encouragement. In this approach,
group members take on predefined roles
(e.g., note keeper), and the teacher assigns a
single grade for the group’s work to reduce
intragroup competition and promote positive
interdependence.

Moderate achievement benefits arise from
types of peer-supported learning that include
positive interdependence, particularly in the
form of interdependent reward contingencies
(Rohrbeck et al. 2003, Slavin 1996). Using
structured roles, as advocated by social cohe-
sion theorists, appears to have little or no ef-
fect on achievement (Rohrbeck et al. 2003) but
may boost students’ social competence and self-
concept (Ginsburg-Block et al. 2006). Peer-
supported learning interventions are particu-
larly effective in boosting achievement, social
competence, self-concept, and task behavior
among urban, low-income, minority students
(Ginsburg-Block et al. 2006, Rohrbeck et al.
2003). Cooperative tasks designed to enhance
student autonomy, such as allowing students to
select goals and monitor and evaluate perfor-
mance, enhance social skills, self-concept, and

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achievement. A plausible but unresearched hy-
pothesis is that practicing metacognitive con-
trol at the group level may help internalize
metacognitive control at the individual level.

Cognitive theories of peer-supported learn-
ing claim it strengthens individual students’
cognitive and metacognitive operations more
than solo learning. Peer-supported learning is
thought to offer more opportunities for re-
trieving and activating schemas, elaborating
new knowledge, self-monitoring, and exercis-
ing metacognitive control (O’Donnell 2006).
For example, using a method called guided
reciprocal peer questioning (King 2002), a
teacher might present a list of generic ques-
tion stems such as “How does . . . affect . . . ?”
and invite students to use the question stems
to generate topic-relevant questions they can
pose within their small group or dyad. Students
can also learn to pose metacognitive questions,
such as “How do you know that?” Having pairs
of elementary students generate questions from
cognitive question stems can enhance learning
outcomes (King 1994, King et al. 1998), but the
efficacy of metacognitive prompting by peers is
less certain.

A student who helps another by generat-
ing an explanation often learns more from the
exchange than does the student who receives
the explanation (Webb & Palincsar 1996). In
research investigating why only some students
who need help benefit from explanations, Webb
& Mastergeorge (2003) described several qual-
ities of successful help-seekers. They persisted
in requesting help until they obtained expla-
nations they understood. They attempted to
solve problems without assistance and asked
for specific explanations rather than answers
to problems. These students adopted difficult
but productive standards for monitoring and
controlling learning. Classroom observations
by Webb et al. (2008) indicate that teachers
in primary grades can substantially increase the
quality and quantity of explanations peers gen-
erate in collaborative groups by encouraging
them to request additional explanations that ex-
tend or clarify an initial explanation. From the
perspective of SRL, teachers who provide such

encouragements are leading students to set
higher standards for metacognitive monitoring.

In Piagetian terms, equal-status peer inter-
actions are more likely to trigger cognitive dis-
equilibrium, thus engendering more engaged
cooperation than do adult-child interactions
(De Lisi 2002). After exposure to peers’ differ-
ing beliefs, dialogue can develop a new under-
standing that restores equilibrium. In Piaget’s
theory, this process is hindered if collabora-
tors have unequal status, as in adult-child inter-
actions, because the higher-status participant
is less likely to be challenged, and the lower-
status participant tends to accept the other’s
beliefs with little cognitive engagement. In
other words, this is a form of self-handicapping
metacognitive monitoring and control. In con-
trast, Vygotsky (1978) held that children con-
struct knowledge primarily by internalizing in-
teractions with a more capable participant who
adjusts guidance to match the less capable par-
ticipant’s growing ability. This calls for sophisti-
cated monitoring of a peer’s understanding and
sensitive metacognitive control that is gradu-
ally released to the developing learner. Studies
of learning gains by children who collabora-
tively solved problems without external feed-
back found that among children paired with
a lower-ability, similar-ability, or higher-ability
partner, only those paired with a higher-ability
partner tended to benefit from collaboration
(Fawcett & Garton 2005, Garton & Pratt 2001,
Tudge 1992). Tudge (1992) found that the
members of similar-ability dyads were at risk
of regressing in performance as a result of col-
laboration. These results favor Vygotsky’s over
Piaget’s account of how status among collabo-
rators stimulates knowledge construction.

How can learners of nearly equal knowledge
and ability benefit from collaboration? How
can more-capable children adjust help given to
meet a peer’s needs when they may be unable to
monitor even their own abilities? Answers may
lie in cognitive strategy instruction in which
(a) the teacher guides and models group inter-
actions and (b) students are assigned to roles that
require metacognitive monitoring (Palincsar
& Herrenkohl 2002). This approach is best

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reflected in research on reciprocal teaching to
improve the reading comprehension of below-
average readers. Here, the teacher’s role grad-
ually shifts from direct explanation and model-
ing to coaching group interactions. A review of
quantitative studies found that reciprocal teach-
ing is consistently more effective than are meth-
ods in which teachers lead students in read-
ing and answering questions about text passages
(Rosenshine & Meister 1994).

For social-cognitive theorists, collaboration
is an academic context to which individu-
als bring personal efficacy and achievement
goals. Surprisingly, there is a lack of social-
cognitive research on peer-supported learn-
ing (Pintrich et al. 2003). This is not because
social-cognitive theories have no implications
for collaborative learning. As an example, stu-
dents who have performance avoidance goals
and low personal efficacy are less likely to seek
help from teachers and are theoretically also
less willing to seek help from peers (Webb &
Mastergeorge 2003). These students monitor
collaborations using standards that handicap
learning or lack skills for interacting with peers
in more productive ways. At a more fundamen-
tal level, Bandura (2000) argued human groups
manifest a collective efficacy, the members’ per-
ceptions of the efficacy of the group. Because
collective efficacy is interdependent with group
performance and the personal efficacy of its
members, it has potentially important but un-
explored implications for peer-supported learn-
ing. These and other unexamined implications
of sociocognitive theory are opportunities to
elaborate peer-supported learning in terms of
metacognitive monitoring and control.

Research has offered only weak accounts
of the many opportunities for metacognitive
monitoring and control in peer-supported
learning, including soliciting and giving ex-
planation, sharing appropriate schemas, and
using appropriate standards for monitoring
progress. Feldmann & Martinezpons (1995)
found that individual self-regulation beliefs
predicted collaborative verbal behavior and
individual achievement. However, there is little
evidence that self-regulatory ability improves

collaboration and, if so, which aspects of
self-regulation affect qualities of collaboration
that recursively promote academic achieve-
ment. In what is perhaps the most informative
research in this area, low-achieving students
were induced to approach a collaborative
problem-solving activity with either learning
or performance goals as standards for mon-
itoring interactions (Gabriele 2007). Those
with a learning goal demonstrated higher
comprehension monitoring, more constructive
collaborative engagement, and higher posttest
performance. Without further research like
this, the role played by metacognitive moni-
toring and control in peer-supported learning
will remain obscure.

Classrooms and Class Size

The relationship between class size and student
achievement has been widely studied. This issue
is so alluring it has attracted researchers even
from economics and sociology. Smith & Glass’s
(1980) meta-analysis established that reducing
class size tends to raise students’ achievement
in a nonlinear relationship. Removing one stu-
dent from a class of thirty tends to raise the
class mean far less than removing one student
from a class of two. In textbooks and thumbnail
reviews, the nonlinearity of the effect is usually
reduced to a simpler principle: gains in achieve-
ment are achieved when class size falls to 15
students or fewer.

Project STAR (Student Teacher Achieve-
ment Ratio), a large-scale experiment on class
size, is lauded as one of the most significant
educational investigations ever conducted
(Mosteller 1995). The project randomly
assigned approximately 12,000 Tennessee
elementary school students and their teachers
to small (13–17 students) and regular-sized
(22–25 students) classes. The students entered
the experiment in kindergarten, grade 1, grade
2, or grade 3. Although the intervention ended
after grade 3, achievement data were collected
until grade 9. In one analysis of the STAR data,
Krueger (1999) concluded that students in their
first year of small classes scored an average of

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4 percentiles higher and increased that advan-
tage in subsequent years of small classes by
about 1 percentile per year. This analysis offers
limited value to policy makers because the cost
of reducing class sizes by one third is high,
and other interventions are known to produce
larger effects. Even more concerning is that
the benefits of some educational interven-
tions diminish rapidly after the intervention
terminates.

Fortunately, a more-detailed picture has
emerged from the STAR data. Krueger (1999)
reported that low-socioeconomic-status (SES)
students, African American students, and inner-
city students all benefited from small class sizes
more than did the general population. Evi-
dence has also emerged that benefits obtained
from small class sizes in grades K–3, including
the extra gains for disadvantaged groups, per-
sisted until at least grade 8 (Nye et al. 2004).
There is an important complication: Small class
sizes tend to increase variability in achievement
and expand the gap between the highest- and
lowest-achieving students (Konstantopoulos
2008). Still more challenging is that re-
cent observational research reports no positive
achievement effects from small class sizes in
kindergarten (Milesi & Gamoran 2006).

Research relating class size and demo-
graphic variables to achievement fails to explain
how learning is affected. Looking inside the
black box of class size could shine light on this
mystery. Blatchford and colleagues (2002, 2007)
conducted a series of systematic observations in
England of teaching and learning in small and
regular-sized classrooms for students ages 11
and under. They found that children in small
classes interacted more with their teachers, re-
ceived more one-to-one instruction, and paid
more attention to their teachers (Blatchford
et al. 2002, 2007). Teachers and observers in
small classes reported that more time was allo-
cated to assessing individual student products
and progress. Despite these impacts on teach-
ing, Blatchford et al. (2007) concluded teachers
may not take full advantage of reduced class size.
They often persisted with more whole-class in-
struction than necessary and failed to adopt

cooperative learning strategies that become
more feasible in smaller classes.

This is consistent with conclusions of the
STAR project. On the whole, teachers assigned
to smaller classes did not strategically modify
their teaching (Finn & Achilles 1999). Indeed,
taking a sociological perspective, Finn et al.
(2003) proposed that improved learning out-
comes in small classes are strongly mediated by
students’ sense of belonging and their academic
and social engagement. Students’ choices about
how they learn and teachers’ choices about how
they teach are manifestations of metacognitive
control. These choices are shaped by standards
they each use to metacognitively monitor their
circumstances and themselves. In short, stan-
dards matter. How do students and teachers ac-
quire them, search for and select them, and use
them in these situations?

If resources are allocated to decreasing class
sizes in the early grades, how can administrators
and teachers know when students are ready to
learn in larger classrooms, where they have less
teacher support? We speculate that students’
abilities to independently monitor and regulate
their learning are crucial to successful perfor-
mance in larger classes. We recommend devel-
oping performance-based tools to assess when
children have self-regulating skills for learning
where there is less teacher attention.

Homework

In her article “Homework is a Complicated
Thing,” Corno (1996) described difficulties
in forming widely applicable, evidence-based
homework policies. Corno’s title is still the best
one-line summation of what is known about the
psychology of homework. This is yet another
case illustrating that hundreds of investigations
using a variety of methods have only weakly in-
formed teaching practices and policy, perhaps
because these studies failed to consider learners
as metacognitive agents.

Teachers assign readings, problem sets, re-
ports, and projects as homework for a vari-
ety of instructional purposes, including prac-
ticing skills demonstrated in class, preparing

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for class discussions, and creatively integrat-
ing and applying knowledge acquired from
multiple sources (Epstein & Van Voorhis
2001). Homework also may be assigned
with intentions to develop time-management
and other self-regulatory skills, stimulate
parental involvement, and foster parent-teacher
communication.

Historically, homework has been controver-
sial. Periodic calls to abolish it are grounded
in claims that it is instructionally ineffective
and pulls time away from family activities.
Calls for abolishing homework interleave with
calls for assigning more homework to increase
children’s preparation for a knowledge-based,
competitive world. Homework can be misused
when teachers assign too much or use it to pun-
ish (Corno 1996). In investigating links between
stress and homework, Kouzma & Kennedy
(2002) found Australian senior high school
students reported a mean of 37 hours of home-
work per week. Time spent on homework cor-
related with self-reported mood disturbance.
Advocates for educational equity have claimed
that homework can increase the performance
gap between high- and low-achieving students
(McDermott et al. 1984).

The relationship between homework and
academic achievement is most fully mapped
in two landmark meta-analyses (Cooper 1989,
Cooper et al. 2006). Cooper (1989) set out a
detailed model of homework effects that in-
cludes (a) exogenous factors such as student
ability and subject matter, and assignment char-
acteristics such as amount and purpose; (b) class-
room factors, such as the provision of materials;
(c) home-community factors, such as activities
competing for student time; and (d ) classroom
follow-up factors, such as feedback and uses of
homework in class discussions. The strongest
evidence for homework’s efficacy comes from
intervention studies, some using random as-
signment, in which students were or were not
given homework. Cooper’s meta-analyses sta-
tistically detected advantages due to homework
in these studies, with weighted mean effect
sizes for student test performance of d = 0.60
(Cooper et al. 2006) and d = 0.21 (Cooper

1989). In a review of studies correlating self-
reported time spent on homework and achieve-
ment, Cooper et al. (2006) statistically de-
tected a positive weighted average effect size of
r = 0.25 for high school students but did not
detect an effect for elementary students. They
reported some evidence of a curvilinear rela-
tionship between amount of homework and
performance. In Lam’s study of grade 12 stu-
dents cited by Cooper et al. (2006), the benefit
from homework was strongest for students do-
ing 7 to 12 hours of homework per week and
weakest for students doing more than 20 or less
than 6 hours per week.

Trautwein and colleagues (Trautwein 2007,
Trautwein et al. 2009) argued that homework is
a “classic example of the multi-level problem”
whereby generally positive effects of homework
reported in Cooper’s meta-analyses mask con-
siderable underlying complexity. Working with
data from 1275 Swiss students in 70 eighth-
grade classes, they distinguished three levels of
analysis. At the class level, they found a positive
relationship between the frequency of home-
work assigned by teachers and classes’ achieve-
ment. At the between-individual level, achieve-
ment related positively to students’ homework
effort but negatively to homework time. At the
intraindividual level, in which students were
assessed longitudinally, the time-achievement
effect flipped direction—homework time
related positively to achievement.

Cooper and Trautwein and their colleagues
call for better-designed and more-ambitious
research on homework. As in so many ar-
eas of educational research, there is a need
for large-scale experiments, longitudinal ob-
servations, hierarchical analyses, and improved
methods for gathering qualitative, time-on-
task, and fine-grained data that trace cognitive
processes. Research also is needed on the ef-
fects of potentially moderating variables such
as culture, grade level, subject area, cognitive
ability, and the manifold factors identified in
Cooper’s model. Finally, there is a need to de-
velop and investigate innovative homework ac-
tivities and compare them with conventional
forms of homework.

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Alongside these macro-level relations, we
theorize self-regulation is a key factor in de-
termining the effects of homework activities.
Here, there is a dearth of research. In one
observational study, Zimmerman & Kitsantas
(2005) found that homework experiences pos-
itively predicted secondary students’ sense of
personal responsibility and self-efficacy be-
liefs, including self-monitoring and organizing.
Those beliefs predicted academic achievement.
In research on the other side of the recip-
rocal relationship, training in homework self-
monitoring was equally effective as parental
monitoring in raising homework-completion
rates above those of a no-intervention control
group (Toney et al. 2003).

Socioeconomic Status

In educational research, SES is most commonly
measured by a composite of parents’ educa-
tion, occupation, and income. Despite older,
widespread beliefs about its overwhelming pre-
dictive power, SES is only a moderately strong
predictor (relative to other known factors)
of school achievement in the United States
(White 1982). The most recent meta-analysis
of U.S. studies found correlations between
SES and achievement of 0.23 to 0.30 when
measured at the student level (Sirin 2005). By
comparison, this effect size is about the same
as the meta-analytically derived correlation
between parental involvement and achieve-
ment (Fan & Chen 2001) and considerably
weaker than correlations of achievement with
educational resources available in the home
(r = 0.51) (Sirin 2005) and parental attitudes
toward education (r = 0.55) (White 1982).
Internationally, the effects of SES are pervasive
and operate both within and between countries
(Chiu & Xihua 2008).

Determining which factors mediate
the relationship between SES and students’
achievement is challenging because the relevant
research is observational, and data range in lev-
els from the student to whole countries. Using
multilevel modeling of data from 25 countries,
Park (2008) investigated the role of the home
literacy environment (early home literacy

activities, parental attitudes toward reading,
and number of books at home) in mediating
the relationship between parental education
and reading performance. He found the
home literacy environment strongly predicted
reading achievement even after statistically
controlling for parental education, but it only
partially mediated the relationship between
parental education and reading performance.

Another factor that may account for bet-
ter reading performance by higher-SES chil-
dren is orally transmitted vocabulary. A U.S.
study (Farkas & Beron 2004) found a gap be-
tween the oral vocabulary of high- and low-
SES children by three years of age, but this
did not increase after children entered kinder-
garten. This suggests that school helps equalize
prior differences between children from differ-
ent socioeconomic backgrounds. A structural
equation modeling study found that parent-
led home learning experiences (e.g., reading,
games, and trips to the zoo or park) medi-
ated the relationship between SES and liter-
acy (Foster et al. 2005). We have not found
research investigating the relationship between
SES and metacognitive monitoring and control
and whether these skills mediate the effects of
SES on achievement. Thus, a full explanation
of how SES affects learning is not available.

In summary, low SES appears to create
significant but not insurmountable barriers to
achievement in elementary school and beyond.
The effects of SES are likely mediated by fac-
tors such as educational resources available in
the home, parental aspirations for their chil-
dren’s education, home literacy activities, and
parental transmission of oral vocabulary. More
high-quality research is needed to investigate
the most effective types of interventions for
low-SES children, especially whether programs
that develop metacognitive and self-regulatory
skills could reduce the disadvantages they face.

PERSISTENT DEBATES

Learning and Cognitive Styles

We have never met a teacher who held
that teaching is maximally successful when all

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learners are taught identically. The opposite
view—that teaching should adapt to learn-
ers’ individual differences—requires identify-
ing one or more qualities of learners upon
which to pivot features of instruction. One class
of such qualities is styles.

Allport (1937) is credited with introducing
the phrase “cognitive style” to describe peo-
ple’s preferred or customary approaches to per-
ception and cognition. When situations involve
learning, stylistic approaches are termed “learn-
ing styles” (Cassidy 2004).

In an early paper, Messick (1970) distin-
guished nine cognitive styles. More recently,
Coffield et al. (2004) cataloged 71 different
models grouped into 13 families. Kozhevnikov
(2007) classified 10 major groupings. Sternberg
et al. (2008) collapsed all these into two cate-
gories. Ability-based styles characterize the typ-
ical approach(es) a learner takes in achievement
tasks, such as representing givens in a prob-
lem using symbolic expressions or diagrams.
Personality-based styles describe a learner’s
preference(s) for using abilities. Typical and
preferred approaches may or may not match.

A recent theoretical synthesis (Kozhevnikov
2007) described styles as “heuristics [that] can
be identified at each level of information pro-
cessing, from perceptual to metacognitive. . .
[whose] main function is regulatory, control-
ling processes from automatic data encoding
to conscious allocation of cognitive resources.”
Very few studies are researching this view. The
vast majority of research in educational set-
tings aligns with Messick’s (1984) view that
styles “are spontaneously applied without con-
scious consideration or choice across a wide
variety of situations” (p. 61). Therefore, stud-
ies have mainly developed and contrasted self-
report inventories or explored correlates of
styles while attempting to show that match-
ing styles to forms of instruction has benefits
while mismatching does not. Learners often
can reliably describe themselves as behaving
stylistically. Their reports correlate moderately
with various demographic variables, individ-
ual differences, and achievement (e.g., Watkins
2001, Zhang & Sternberg 2001). Contrary to

expectations, matching instruction to style does
not have reliable effects (Coffield et al. 2004).

There are challenges to using styles in psy-
chological accounts of school performance.
First, thorough and critical syntheses of the psy-
chometric properties and validity of self-report
style measures are scant. One of the few was
Pittenger’s (1993) review of the Myers-Briggs
Type Indicator. He concluded, “. . .there is no
convincing evidence to justify that knowledge
of type is a reliable or valid predictor of impor-
tant behavioral conditions” (p. 483). Second,
studies investigating the match of self-
reports to behaviors are also rare. Krätzig &
Arbuthnott’s (2006) study of visual, auditory,
kinesthetic, and mixed learning styles found no
correlation between self-reported preferences
for styles and objective scores on cognitive tasks
measuring what the style was about. The study
of field dependency-independence by Miyake
et al. (2001) led them to conclude that this style
“should be construed more as a cognitive abil-
ity, rather than a cognitive style” (p. 456).

Discovery Learning

Discovery learning is most strongly associated
with science and math education. It has roots in
the Piagetian view that “each time one prema-
turely teaches a child something he could have
discovered for himself, that child is kept from
inventing it and consequently from understand-
ing it completely” (Piaget 1970, p. 715). Bruner
(1961) theorized that discovery learning fosters
intrinsic motivation, leads to an understand-
ing of and inclination toward the heuristics
of inquiry, and allows for the active self-
organization of new knowledge in a way that
fits the specific prior knowledge of the learner.
According to Hammer (1997, p. 489), discovery
learning usually “refers to a form of curriculum
in which students are exposed to particular
questions and experiences in such a way that
they ‘discover’ for themselves the intended
concepts.” In unguided and minimally guided
discovery learning, the role of the teacher is
constrained to providing a learning environ-
ment or problem space and perhaps posing

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questions. In discovery learning, teacher-posed
questions should lead the student toward
Piagetian disequilibrium, which is conceived
as cognitive conflict between prior knowledge
and new information from the environment.

Proponents of discovery learning believe
it produces highly durable and transferable
knowledge, a claim consistent with some ob-
servational evidence. For example, children in
grades one and two who spontaneously in-
vented and used arithmetic strategies subse-
quently showed greater understanding of base
10 number concepts and better performance
on transfer problems than did children who
initially acquired the standard arithmetic algo-
rithms from instruction (Carpenter et al. 1998).

In a widely cited review, Mayer (2004)
criticized discovery methods that emphasize
unguided exploration in learning environments
and problem spaces. Describing a belief in the
value of pure discovery learning as “like some
zombie that keeps returning from its grave”
(p. 17), he reviewed investigations in three
domains—problem-solving rules, conservation
strategies, and Logo programming strategies.
Mayer (2004) observed how in each case, ac-
cumulated evidence favored methods in which
learners received guidance. He questioned
the supposed connection between discovery
teaching methods and constructivist theories,
arguing that cognitive activity, not behavioral
activity, is the essential requirement for con-
structivist learning. He maintained that, as a
consequence, “active-learning” interventions
such as hands-on work with materials and
group discussions are effective only when they
promote cognitive engagement directed toward
educational goals.

The debate often pits discovery learning
against direct instruction. Direct instruction
is a broad domain of explicit teaching prac-
tices that include stating learning goals, review-
ing prerequisite knowledge, presenting new
information in small steps, offering clear in-
structions and explanations, providing opportu-
nity for frequent practice, guiding performance,
and giving customized, explanatory feedback
(Rosenshine 1987). Originating as an approach

to teaching primary reading, direct instruc-
tion has been successful within a wide range
of general- and special-education programs
at the elementary level (Swanson & Hoskyn
1998).

Discovery learning has been seen as a tool
for acquiring difficult, developmentally signif-
icant knowledge, such as the control of vari-
ables strategy (CVS) used in designing experi-
ments. However, when Klahr & Nigam (2004)
randomly assigned elementary students to learn
CVS by discovery or direct instruction, many
more succeeded in the direct-instruction condi-
tion. Moreover, on an authentic transfer task in-
volving evaluating science fair posters, the many
students in the direct instruction condition who
showed success while learning performed as
well as the few students in the discovery group
who also showed success while learning. Dean
& Kuhn (2007) randomly assigned students
learning CVS to direct instruction, discovery
learning, and a combination of the two. Direct
instruction was presented only during an initial
session, and the discovery learning treatment
extended over 12 sessions. In this study, direct
instruction produced an immediate advantage,
which disappeared in a posttest and a transfer
task given several weeks after the termination of
the discovery learning sessions. Although both
of these experiments implemented direct in-
struction as a single session in which CVS was
presented and modeled by a teacher, the experi-
ments failed to include teacher-guided practice
with feedback, which is a powerful and essential
component of direct instruction.

A review by Kirschner et al. (2006) explained
the evidence against minimally guided instruc-
tion in terms of cognitive load theory. They
cast discovery learning as a type of problem
solving that requires a cognitively demanding
search in a problem space. According to cogni-
tive load theory, such a search is extrinsic load
that requires time and cognitive resources that
otherwise could be used for understanding and
elaborative processing of solution schemas. To
support this claim, they cited evidence that
novices learn to solve problems more effec-
tively by initially studying worked solutions

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before starting to solve problems (Tuovinen &
Sweller 1999).

Rittle-Johnson (2006) pointed out that dis-
covery learning theorists tend to conflate the
two separate cognitive processes of reasoning
about solutions and inventing them. She did a
2 × 2 experiment in which elementary school
children learning the concept of mathematical
equivalence were assigned to either instruction
or invention and either self-explanation or no
self-explanation. The invention condition of-
fered no advantages. Both instruction and self-
explanation conditions produced advantages
for procedural learning on a delayed posttest,
and only self-explanation produced advantages
for transfer. It may be that self-directed
elaborative processing, in this case manifested
as self-explanation, is the only way to obtain
high-level transfer (Salomon & Perkins 1989).
The search of the problem space entailed by
unguided discovery may hinder high-level
transfer by taxing cognitive resources.

Another explanation of evidence favor-
ing guided instruction is that students lack
metacognitive skills needed to learn from
unguided exploration. They may be unable to
manage time to explore all relevant possibil-
ities, keep track of which conditions and cases
they have already explored, accurately monitor
what they know and need to know, and monitor
what works over the course of learning.

There is a need for better theory and
evidentiary support for principles of guided
discovery. We recommend investigating mul-
tiple ways of guiding discovery so that, ideally,
every child is led to the brink of invention
and extensive search of the problem space is
avoided. Metacognitive guidance could include
suggestions to generate a hypothesis, to make
a detailed action plan, and to monitor the gap
between the research question and the obser-
vations. These cognitive and metacognitive
activities improve learning outcomes (Veenman
et al. 1994).

The timing of metacognitive guidance may
be critical. Hulshof & de Jong (2006) provided
“just-in-time” instructional tips in a computer-
based environment for conducting simulated

optics experiments. A new tip became acces-
sible every three minutes and could be con-
sulted at any time thereafter. Although consult-
ing the tips was optional, and tips contained no
information that was directly assessed by the
posttest, students randomly assigned to a condi-
tion that provided the tips outperformed peers
in a control condition on the posttest. A poten-
tial drawback to this type of optional support is
that students may misjudge their need for guid-
ance and fail to access a needed tip or make
excessive use of tips to avoid genuine cogni-
tive engagement with the problem (Aleven et al.
2003). Theories about guided learning that may
emerge from such research should strive to
account for the motivational, cognitive, and
metacognitive factors reviewed in this article.

METHODOLOGICAL ISSUES
IN MODELING A PSYCHOLOGY
OF ACADEMIC ACHIEVEMENT

Paradigmatic Issues

The psychology of school achievement has
been studied mainly within a paradigm that we
suggest faces difficult challenges. Intending no
disrespect, we call this the “snapshot, bookend,
between-groups paradigm”—SBBG for short.
Recall Roediger’s (2008) conclusion that the
“only sort of general law, is that in making any
generalization about memory one must add that
‘it depends’” (p. 247). We posit that his claim
generalizes to most if not all findings in a psy-
chology about the way things are because of
rules for doing research according to the SBBG
paradigm.

SBBG is snapshot because data that reflect
the effect of a causal variable almost always are
collected just once, after an intervention is over.
We acknowledge some studies are longitudinal
but maintain that snapshot studies overwhelm-
ingly form the basis of today’s psychology of
academic achievement.

Beyond the shortcoming of insufficiently
tracing events between the bookends of a
learning session, there is another reason that
educational psychology’s snapshot-oriented

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research paradigm may model academic
achievement incompletely. Students in class-
rooms and people in training learn new infor-
mation and shift motivation and affect across
time. A snapshot study captures just one
posttest or pre-to-post segment within a longer
trajectory of psychological events. The field has
insufficiently attended to how segments con-
catenate. This is a necessary concern in mod-
eling a trajectory of learning because the next
segment may not match a researcher’s predicted
concatenation. But this issue is not one to vali-
date analytically and a priori. Data are required
to characterize how, at any point in the trajec-
tory of a learning activity, a learner metacogni-
tively monitors and exercises the metacognitive
control that forms a trajectory of learning.

SBBG is a bookend paradigm because re-
searchers rarely gather data representing proxi-
mally cognitive or motivational events between
the time when learners are randomly assigned
to an intervention and the time when potential
effects are measured after the intervention is
over. Ideally, random assignment reduces the
necessity to gather data before an intervention.
(But see Winne 2006 for an argument about
challenges to random assignment as a panacea
for erasing extraneous variance.) Otherwise,
premeasures are secured to reduce “error” vari-
ance by blocking or statistically residualizing
the outcome variable. (But see Winne 1983 for
challenges to interpretation that arise in this
case.) Random assignment and premeasures
cannot identify cognitive processes that create
changes in achievement. Randomness cannot
help researchers interpret a systematic effect.
Change in a learner’s achievement can be
conditioned by an aptitude that remains con-
stant for that learner during the intervention,
but that change cannot be caused unless this
aptitude varies during the intervention.

An alternative that could illuminate
achievement-changing processes inside an
intervention is to gather data to proximally
trace those processes (Borsboom et al. 2003,
Winne 1982). Regrettably, data of this kind
are rarely gathered because it is impractical.
(But see Winne 2006 for ideas about how

impracticalities might be overcome using
software technologies.) Thus, in bookend ex-
periments, psychological processes that unfold
as learners experience the intervention must
be inferred rather than validated using fine-
grained data gathered over time between the
experiment’s bookends (Winne & Nesbit 2009).
Traces of processing allow opening the book
between a traditional experiment’s bookends
and viewing each “page” situated in relation
to prior events and following events. This
allows merging psychologies of “the way things
are” with “the way learners make things.”
Modeling should honor the dual role of events
observed at points within the intervention, first
as the outcome of prior psychological process
and second as a process that generates the
next state. Empirically investigating a learning
trajectory, therefore, entails gathering data that
can more fully contribute to accounting for
change over time. This stands in contrast to
data that reflect only the cumulative products
of multiple processes that unfold over time
with an intervention.

SBBG is a between-groups paradigm be-
cause it forces interpretations about whether
an intervention changes learners’ achievement
to be grounded in differences (variance) be-
tween the central tendencies of a treatment
group versus a comparison group. Data are
lacking that trace how learners make things.
Therefore, variance within each group due, in
part, to individuals’ self-regulating learning—
metacognitive monitoring and control applied
“on the fly” —has to be treated as “residual” or
“error.” In fact, the epitome of an experiment
in the between-groups tradition would zero out
individual differences in the ways learners make
things.

If learners are agents, this approach
leaves out key parts of the story about how
achievement changes. The between-groups
experimental approach relieves this tension by
explaining effects in terms of a psychological
process that does not vary across individuals
despite researchers’ belief in variance in the way
learners make things. Thus, without opening
the book of each group member’s experience,

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“between-subjects models do not imply, test,
or support causal accounts that are valid at the
individual level” (Borsboom et al. 2003, p. 214).
The result is that a psychology about the way
things are becomes an “it depends” science
because between-groups experiments must
neglect causal effects that arise from individual
differences in the way learners make things.

A Revised Paradigm

We suggest that a more productive psychol-
ogy of academic achievement should probe and
map how learners construct and use informa-
tion within boundaries set by the way things are.
This entails three major paradigmatic changes.
First, gather data that trace variance in learn-
ers’ psychological states over time during an in-
tervention. Supplement snapshot data. Second,
conceptualize trajectories of learning as a suc-
cession of outcomes reciprocally determined by
learners who choose information and modes
of processing it to construct successive infor-
mational products. Read between bookends.
Third, in the many situations where random
assignment is not feasible and even where it is,
define groups of learners a posteriori in terms
of trace data that prove learners to be approx-
imately homogenous in their information pro-
cessing. Fix causes at the individual level, then
explore for mediating and moderating vari-
ables post hoc. A paradigm that includes tracing
agents’ self-regulated processes provides raw
materials that can support grounded accounts
of what happens in the psychology of academic
achievement at the same time it accommodates
variations in instructional designs.

SHAPES FOR FUTURE RESEARCH

We judge that the field of educational psy-
chology is in the midst of striving to integrate
two streams. One stream investigates whether
achievement improves by manipulating instruc-
tional conditions (e.g., class size, discovery
learning) or accommodating trait-like individ-
ual differences (e.g., epistemic beliefs) or social
conditions (e.g., SES). In these studies, what

individual learners do inside the span of a learn-
ing session and how each learner adjusts goals,
tactics, and perceptions have been of interest.
But these generating variables have rarely been
directly operationalized and, when acknowl-
edged, they are mostly treated as error variance
terms in analyses of data. The second stream of
studies seeks to operationalize reciprocally de-
termined relations among a learner’s metacog-
nition, broadly conceptualized, and outcomes.
In these studies, bookend variables set a stage of
movable props: standards for metacognitively
monitoring and choices exercised in metacogni-
tive control. Learners choose the information-
processing tools they use within bounds of a
psychology of the way things are.

We take as prima facie that changes in aca-
demic achievement have origins in psycholog-
ical phenomena. Snapshot, bookend between-
groups studies in educational psychology have
not traced those phenomena, as Winne (1983)
and Borsboom et al. (2003) argued. Educational
psychology should turn its attention to methods
that penetrate correlations among distal vari-
ables. The goal should be to develop maps of
proximal psychological processes that reflect
causes of learning. In doing so, we hypothe-
size research must concern itself with learn-
ers’ metacognitive monitoring and control.
These processes set into motion forms of self-
regulated learning that have been demonstrated
to influence achievement. Studies should be not
only more intensely focused on proximal indi-
cators of psychological processes; researchers
also need to gather data inside the bookends
of learning sessions to track reciprocally de-
termined relations that shape learning trajec-
tories. In short, we recommend that snapshot,
bookend between-groups research be comple-
mented with a microgenetic method (Siegler &
Crowley 1991). This suggests several require-
ments. One is operationally defining traces to
describe which psychological processes in the
realm of “the way things are” are applied dur-
ing learning. Another is determining which
standards learners apply in their metacognitive
monitoring that leads to metacognitive control.
These data model the way learners make things.

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By a mix of natural exploration and instruc-
tion, learners develop their own heuristics, re-
flective of a naı̈ve psychology of the way things
are, about how cognitive and external factors
can be arranged to acquire and successfully
use academic knowledge. As agents, they oper-
ationalize those heuristics by metacognitively
monitoring and controlling mental states and
by manipulating external factors. By tracking
their academic achievements and side effects
over time, they become informed about how to
regulate engagement in learning to improve the
results of subsequent engagements. In short,
over time, self-regulating learners experiment
with learning to improve how they learn along-
side what they learn (Winne 1995).

Findings from the psychology of the way
things are will become better understood as we
advance the psychology of how learners make
things. This will involve learning more about
standards that learners use to metacognitively
monitor, the nature of monitoring per se, how
learners characterize a profile of features gen-
erated by monitoring, and how potential ac-
tions are searched for and matched to a profile
generated by monitoring that sets a stage for
metacognitive control. Metaphorically, because

learners are in the driver’s seat, educational psy-
chology needs a model of how learners drive to
understand more fully how they reach desti-
nations of academic achievement. By incorpo-
rating metacognition and its larger-scale form,
self-regulated learning, into data and analyses of
data, rather than randomizing out these factors,
we submit a psychology of academic achieve-
ment can advance theoretically and offer more
powerful principles for practice.

Our hypothesis is that gluing together the
two psychologies of the way things are and the
way learners make things will reduce the de-
gree of Roediger’s “it depends” hedge on laws of
memory (and learning). Two inherent sources
of variance need examining: What do learners
already know and access over the fine-grained
course of a learning session? How do learners
self-regulate learning across sessions to adapt
in service of achieving their goals? Richer in-
terpretations will need to be grounded on fine-
grained trace data that fill in gaps about pro-
cesses in learning, specifically: Which heuristics
for learning do learners consider, choose, apply,
and adapt? How do those processes by which
learners make things and self-regulate unfold
under constraints of how things are?

DISCLOSURE STATEMENT

The authors are not aware of any biases that might be perceived as affecting the objectivity of this
review.

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Annual Review of
Psychology

Volume 61, 2010 Contents

Prefatory

Love in the Fourth Dimension
Ellen Berscheid � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Brain Mechanisms and Behavior

The Role of the Hippocampus in Prediction and Imagination
Randy L. Buckner � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �27

Learning and Memory Plasticity; Neuroscience of Learning

Hippocampal-Neocortical Interactions in Memory Formation,
Consolidation, and Reconsolidation
Szu-Han Wang and Richard G.M. Morris � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Stress and Neuroendocrinology

Stress Hormone Regulation: Biological Role
and Translation Into Therapy
Florian Holsboer and Marcus Ising � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �81

Developmental Psychobiology

Structural Plasticity and Hippocampal Function
Benedetta Leuner and Elizabeth Gould � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 111

Cognitive Neuroscience

A Bridge Over Troubled Water: Reconsolidation as a Link Between
Cognitive and Neuroscientific Memory Research Traditions
Oliver Hardt, Einar Örn Einarsson, and Karim Nader � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 141

Cognitive Neural Prosthetics

Richard A. Andersen, Eun Jung Hwang, and Grant H. Mulliken � � � � � � � � � � � � � � � � � � � � � � 169

Speech Perception

Speech Perception and Language Acquisition in the First Year of Life
Judit Gervain and Jacques Mehler � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 191

AR398-FM ARI 7 November 2009 7:47

Chemical Senses (Taste and Smell)

An Odor Is Not Worth a Thousand Words: From Multidimensional
Odors to Unidimensional Odor Objects
Yaara Yeshurun and Noam Sobel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 219

Somesthetic and Vestibular Senses

Somesthetic Senses
Mark Hollins � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 243

Basic Learning and Conditioning

Learning: From Association to Cognition
David R. Shanks � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 273

Comparative Psychology

Evolving the Capacity to Understand Actions, Intentions, and Goals
Marc Hauser and Justin Wood � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 303

Human Development: Processes

Child Maltreatment and Memory
Gail S. Goodman, Jodi A. Quas, and Christin M. Ogle � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 325

Emotional, Social, and Personality Development

Patterns of Gender Development
Carol Lynn Martin and Diane N. Ruble � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 353

Adulthood and Aging

Social and Emotional Aging
Susan T. Charles and Laura L. Carstensen � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 383

Development in Societal Context

Human Development in Societal Context
Aletha C. Huston and Alison C. Bentley � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 411

Genetics and Psychopathology

Epigenetics and the Environmental Regulation
of the Genome and Its Function
Tie-Yuan Zhang and Michael J. Meaney � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 439

Social Psychology of Attention, Control, and Automaticity

Goals, Attention, and (Un)Consciousness
Ap Dijksterhuis and Henk Aarts � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 467

Contents vii

AR398-FM ARI 7 November 2009 7:47

Bargaining, Negotiation, Conflict, Social Justice

Negotiation
Leigh L. Thompson, Jiunwen Wang, and Brian C. Gunia � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 491

Personality Development: Stability and Change

Personality Development: Continuity and Change Over the
Life Course
Dan P. McAdams and Bradley D. Olson � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 517

Work Motivation

Self-Regulation at Work
Robert G. Lord, James M. Diefendorff, Aaron C. Schmidt, and Rosalie J. Hall � � � � � � � � 543

Cognition in Organizations

Creativity
Beth A. Hennessey and Teresa M. Amabile � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 569

Work Attitudes ( Job Satisfaction, Commitment, Identification)

The Intersection of Work and Family Life: The Role of Affect
Lillian T. Eby, Charleen P. Maher, and Marcus M. Butts � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 599

Human Factors (Machine Information, Person Machine Information,
Workplace Conditions)

Cumulative Knowledge and Progress in Human Factors
Robert W. Proctor and Kim-Phuong L. Vu � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 623

Learning and Performance in Educational Settings

The Psychology of Academic Achievement
Philip H. Winne and John C. Nesbit � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 653

Personality and Coping Styles

Personality and Coping
Charles S. Carver and Jennifer Connor-Smith � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 679

Indexes

Cumulative Index of Contributing Authors, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � 705

Cumulative Index of Chapter Titles, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 710

Errata

An online log of corrections to Annual Review of Psychology articles may be found at
http://psych.annualreviews.org/errata.shtml