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Putting Education in “Educational” Apps: Lessons From the Science of Learning

Authors:
  • Pennsylvania State University, Brandywine

Abstract and Figures

Children are in the midst of a vast, unplanned experiment, surrounded by digital technologies that were not available but 5 years ago. At the apex of this boom is the introduction of applications ("apps") for tablets and smartphones. However, there is simply not the time, money, or resources available to evaluate each app as it enters the market. Thus, "educational" apps-the number of which, as of January 2015, stood at 80,000 in Apple's App Store (Apple, 2015)-are largely unregulated and untested. This article offers a way to define the potential educational impact of current and future apps. We build upon decades of work on the Science of Learning, which has examined how children learn best. From this work, we abstract a set of principles for two ultimate goals. First, we aim to guide researchers, educators, and designers in evidence-based app development. Second, by creating an evidence-based guide, we hope to set a new standard for evaluating and selecting the most effective existing children's apps. In short, we will show how the design and use of educational apps aligns with known processes of children's learning and development and offer a framework that can be used by parents and designers alike. Apps designed to promote active, engaged, meaningful, and socially interactive learning-four "pillars" of learning-within the context of a supported learning goal are considered educational. © The Author(s) 2015.
Content may be subject to copyright.
Psychological Science in the
Public Interest
2015, Vol. 16(1) 3 –34
© The Author(s) 2015
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DOI: 10.1177/1529100615569721
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Introduction
Over 56% of all Americans (A. Smith, 2013) own a smart-
phone. More than a third of these also include tablets
(34%; Rainie, 2012; Zickuhr, 2013) in their cache of digital
personal items. These handheld devices allow us to do
everything from the privacy of our portable offices. They
help us manage our vacations, update our calendars, and
give us immediate access to the Internet, all through the
power of “apps” (applications) or computer programs.
Remarkably, a decade ago, apps were not part of the
e-landscape. Yet just 3 years after the popular iPad was
introduced on July 26, 2010 (Apple, 2010), Apple pro-
claimed that iTunes had achieved its 50 billionth down-
load (Apple, 2013b). Indeed, in December 2013 alone,
consumers downloaded almost 3 billion apps (Apple,
2014), with more than 500,000 apps developed for
iPhone, iPad, and iPod touch users alone (Apple, 2014).
The numbers tell the story. Apps are not just ubiqui-
tous, but also big business: Over $10 billion was spent in
the App Store in 2013 (Apple, 2014). By 2015, revenue
from apps is predicted to triple to $38 billion (Shuler,
2012). Technology is rapidly changing the nature of
adults’ day-to-day and even minute-to-minute experi-
ences. We have not begun to understand the impact of
the app explosion on our economy and society.
While this sweeping change has had significant effects
on the daily lives of adults, its ultimate impact may be
even more significant for the children, toddlers, and even
infants for whom apps are designed and marketed. Over
569721PPIXXX10.1177/1529100615569721Hirsh-Pasek et al.Putting Education in “Educational” Apps
research-article2015
Corresponding Author:
Kathy Hirsh-Pasek, Department of Psychology, Temple University,
Philadelphia, PA 19122
E-mail: khirshpa@temple.edu
Putting Education in “Educational” Apps:
Lessons From the Science of Learning
Kathy Hirsh-Pasek1, Jennifer M. Zosh2, Roberta Michnick
Golinkoff
3, James H. Gray4, Michael B. Robb5, and
Jordy Kaufman6
1Department of Psychology, Temple University; 2Department of Human Development and Family
Studies, Penn State University, Brandywine; 3School of Education, University of Delaware; 4Sesame
Workshop, New York, NY; 5Fred Rogers Center for Early Learning and Children’s Media at Saint Vincent
College; and 6Brain and Psychological Sciences Research Centre, Swinburne University of Technology
Summary
Children are in the midst of a vast, unplanned experiment, surrounded by digital technologies that were not available
but 5 years ago. At the apex of this boom is the introduction of applications (“apps”) for tablets and smartphones.
However, there is simply not the time, money, or resources available to evaluate each app as it enters the market.
Thus, “educational” apps—the number of which, as of January 2015, stood at 80,000 in Apple’s App Store (Apple,
2015)—are largely unregulated and untested. This article offers a way to define the potential educational impact
of current and future apps. We build upon decades of work on the Science of Learning, which has examined how
children learn best. From this work, we abstract a set of principles for two ultimate goals. First, we aim to guide
researchers, educators, and designers in evidence-based app development. Second, by creating an evidence-based
guide, we hope to set a new standard for evaluating and selecting the most effective existing children’s apps. In short,
we will show how the design and use of educational apps aligns with known processes of children’s learning and
development and offer a framework that can be used by parents and designers alike. Apps designed to promote active,
engaged, meaningful, and socially interactive learning—four “pillars” of learning—within the context of a supported
learning goal are considered educational.
Keywords
media, apps, Science of Learning, education, digital, early childhood education
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4 Hirsh-Pasek et al.
80,000 apps are classified as education- and learning-
based (Apple, 2015). In 2013, 58% of parents in the United
States reported that they had downloaded apps for their
children (Common Sense Media, 2013). Indeed, the
Preschool/Toddler category is the most popular category
of apps in the App Store, accounting for 72% of the top
paid apps (Shuler, 2012). The near-instantaneous delivery
of new apps prevents scientists from evaluating specific
apps as they are introduced into the marketplace.
This article focuses on “educational” apps that have
been developed for touch-screen tablets and phones and
marketed to young children ages 0 to 8. We concentrate
on the use of apps by this age bracket for four reasons.
First, intuitive interactions afforded by touch-screen
devices make app content potentially accessible to very
young prereaders—even babies. Indeed, there are so
many apps targeted toward young children that parents
and educators do not know how to navigate the market-
place of possibilities (Guernsey, 2014; Rideout, 2014).
Second, a large number of schools throughout the nation
have integrated the use of tablets into their curriculum
(Apple, 2013a), despite the absence of research to sup-
port this change. Third, less than 20% of a child’s waking
time is spent in school (LIFE Center: Learning in Informal
and Formal Environments, 2005). The amount of time
that children spend with digital media and the surge in
educational apps’ popularity suggest that at least some
apps are being used in an attempt to supplement learn-
ing outside of school. Apps present a significant oppor-
tunity for out-of-school, informal learning when designed
in educationally appropriate ways. Fourth, school readi-
ness is predictive of later achievement (Duncan etal.,
2007). If apps can improve young children’s skills, school
readiness, or executive-function capabilities, then early
learning with apps might have long-term impacts (Goldin
etal., 2014).
In this article, we use data from decades of research in
the Science of Learning to illustrate how the develop-
ment and evaluation of apps could embrace an evidence-
based stance. Importantly, there are a number of
theoretical positions on the ways in which children learn
(Bransford, Brown, & Cocking, 1999), ranging from direct
instruction (Kirschner, Sweller, & Clark, 2006) to free play
(P. Gray, 2013). Our goal in this article is not to choose
among theories of learning or even to craft our own the-
ory of learning. Rather, we attempt to highlight areas of
convergence among the theories from this relatively new,
amalgamated research area dubbed the Science of
Learning. We also do not endorse or evaluate any par-
ticular apps, but rather use targeted apps as illustrations
of four psychological principles (or pillars) that can be
derived from the scientific literature. We suggest that if
we want to put the “education” back in educational apps,
we will need to design and evaluate them in ways that
promote the best learning. Research suggests that chil-
dren learn best when they are cognitively active and
engaged, when learning experiences are meaningful and
socially interactive, and when learning is guided by a
specific goal. This should not suggest that learning can-
not take place outside of these conditions, only that the
research literature suggests that these conditions often set
the stage for effective learning. Therefore, apps that
recruit some or all of these pillars within a learning con-
text are more likely to result in effective learning than
those that do not. This conclusion is warranted by the
literature and deserves to be further refined through
additional empirical research.
It is important to clarify what is meant by “active,
“engaged,” “meaningful,” “socially interactive,” and “in the
service of a learning goal.” These pillars represent areas of
convergence from the newly amalgamated field of the
Science of Learning. “Active” learning implies minds-on
involvement during the learning experience, in addition
to any physical activity that may be occurring, such as
swipes and taps. Children’s engagement—that is, their
ability to stay on task and undistracted—also supports
learning. Meaningful learning goes beyond simple memo-
rization, and occurs when children find the meaning in
what they are learning and are able to not only connect
new material to existing knowledge but expand their cur-
rent knowledge to create new conceptual understanding.
Social interaction revolves around high-quality interac-
tions (e.g., those with knowledgeable social partners or in
collaborative learning situations) that are contingent and
adaptable to the child (Tamis-LeMonda, Kuchirko, & Song,
2014). Finally, and importantly, we will argue that “educa-
tional” apps are those that support a learning goal, be it in
the learning of shapes or the mastery of new vocabulary
words. There exist whole categories of very good apps
that are fun to play with but that have no real educational
goals. These might be highly engaging, but they are
beyond the purview of what we consider “educational.
We review the literature supporting each of these
pillars and the rationale for the educational focus below.
The Importance of Considering the
Development of Principles for App Use
Designers of child-focused apps do not begin with a
blank slate. Instead, they are influenced by current trends
in technology and design, their own interactions with
technology, and their experiences and intuitive sense of
how learning happens or what children will find enjoy-
able. While this is understandable, this approach is often
tainted by misconceptions about learning and education,
as exemplified by the success of the Baby Genius video
series and related “educational” television in the early
2000s. Despite marketers’ explicit and implicit claims of
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Putting Education in “Educational” Apps 5
effectiveness, scientific study (e.g., DeLoache etal., 2010;
Richert, Robb, Fender, & Wartella, 2010; Robb, Richert, &
Wartella, 2009; Zimmerman, Christakis, & Meltzoff, 2007)
revealed that young children were not learning effec-
tively from these television programs and DVDs.
Only a handful of apps are designed with an eye
toward how children actually learn. A small number of
developers at both small start-ups and bigger toy/media
companies have used research-based approaches with
preliminary results of research. For example, a recent
study found that interacting with a vocabulary-focused
app increased young low-income children’s vocabulary
by up to 31% in just a 2-week period (Chiong & Shuler,
2010; Corporation for Public Broadcasting, 2011). While
this may sound encouraging to app developers and users,
little detail was offered about the study design, making it
difficult to evaluate its scientific impact. Given a very lim-
ited precedent of effective app use, there is a need to
propose principles for the design of appropriate apps that
will offer a greater likelihood of educational benefits.
Riding the First Wave and Propelling
the Second Wave of Apps for Use By
Children
The majority of apps in today’s marketplace can be con-
sidered part of the “first wave” of the digital revolution. In
this wave, apps are simply digital worksheets, games, and
puzzles that have been reproduced in an e-format with-
out any explicit consideration of how children learn or
how the unique affordances of electronic media can be
harnessed to support learning. We must find ways to help
parents assess apps that exist in this first wave (Kucirkova,
2014). While there is no way to scientifically study every
app on the market, a set of principles based on science
can be developed and used to evaluate the current crop
of apps. Some preliminary steps have already been taken
with the introduction of rating systems by Children’s
Technology Review, Common Sense Media, and a handful
of parent-oriented app services. For example, Common
Sense Media (https://www.commonsensemedia.org/)
uses 5-point scales to rate individual pieces of media for
“ease of play,” “violence & scariness,“sexy stuff,“lan-
guage,” “consumerism,“drinking, drugs, & smoking,
and “privacy & safety.” Reviewers also give an overall rat-
ing for “quality” and “learning” and select the age of chil-
dren for whom the app is appropriate. While these rating
systems have not been scientifically evaluated, they are
widely used in the field.
In this article, we hope to join those ushering in a
second wave of app development—the wave just begin-
ning to take shape—that harnesses guidelines from the
Science of Learning. If researchers and developers work
together, they might develop well-designed apps that
could be fun for all users and provide augmented expe-
riences to low-socioeconomic-status children, helping to
reduce the long-standing achievement gap. This effort is
already underway in New York City, where a massive
investment in technology is being heralded as a key
ingredient for narrowing the gap (City of New York,
Office of the Mayor, 2014). This idea has some currency.
The One Laptop per Child program was used in a poor
rural area of Argentina to demonstrate how the availabil-
ity of well-crafted educational games on an accessible
laptop can promote school readiness in both learning
processes such as attention and problem solving and
academic outcomes such as reading (Goldin etal., 2014).
Indeed, the scientists in the Argentine program collabo-
rated with top researchers in the United States to craft
and design computerized games that stimulated learn-
ing. The Plan Ceibal (http://www.ceibal.edu.uy/) in
Uruguay represents a government initiative to offer all
children in the country access to materials and curricular
digital opportunities. Analyses of this program are
underway.
We are at a unique and important time in the develop-
ment of apps. They are ever present—in schools, in
homes, and even in the crib. At the same time, the past
few decades of research in the Science of Learning have
transformed the way we think about learning and teach-
ing. By melding these parallel threads, media developers
can have access to knowledge that allows them to create
better educational apps, and parents can evaluate apps’
learning potential for their children.
The Science of Learning as a Guide for
Educational Principles
How might we evaluate “educational” apps to determine
their educational value? In the last 20 years, a potential
answer has come from a new field dubbed the Science of
Learning. The term “Science of Learning” was first used in
the early 1990s with the creation of the Journal of
Learning Science. In 1999, the publication of How People
Learn, a report from the National Research Council
(Bransford etal., 1999), secured its place at the juncture
of psychology and education. The field has prospered
since 1999, when the authors of this key volume wrote,
The new Science of Learning is beginning to
provide knowledge to improve significantly
people’s abilities to become active learners who
seek to understand complex subject matter and are
better prepared to transfer what they have learned
to new problems and settings (p. 13).
Several new efforts have popularized this idea for for-
mal schooling (Brown, Roediger, & McDaniel, 2014;
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6 Hirsh-Pasek et al.
Mayer, 2011) and for college teaching (Ambrose, Bridges,
DiPietro, Lovett, & Norman, 2010), among other areas.
Notably, this approach has been taken in the field of
computer-based games (Honey & Hilton, 2011; Mayer,
2014a, 2014b; O’Neil & Perez, 2008; Tobias & Fletcher,
2011), but rarely has the Science of Learning been used
to design apps for young children (notable exceptions
include DreamBox Learning, Kidaptive, Motion Math,
and Next Generation Preschool Math). However, to our
knowledge, this is the first time anyone has derived a
relatively simple set of principles from the Science of
Learning that can be applied to the design and evaluation
of apps for young children.
Knitted together from psychology, linguistics, com-
puter science, animal behavior, machine learning, brain
imaging, neurobiology, and other areas, this newly
minted field asks not merely what we should teach
children—that is, what content—but also how children
best learn the strategies they will need to cope flexibly
and creatively in a 21st-century world (e.g., Benassi,
Overson, & Hakala, 2014; Golinkoff & Hirsh-Pasek, in
press; Pellegrino, 2012; Pellegrino & Hilton, 2013;
Sawyer, 2006). To date, researchers have cast a wide net
over the Science of Learning, and this approach includes
a wealth of topics, from navigation and robotics to lan-
guage learning by man, machine, and animals to early
understanding of mathematics and mastery of literacy,
among others. The National Science Foundation jump-
started conversations among these interdisciplinary
fields and topic areas to form a more coherent under-
standing of how people learn (see LIFE Center: Learning
in Informal and Formal Environments, n.d., and the
Center for Innovative Learning Technologies, n.d.).
Indeed, a similar effort has been made to specifically
yoke the Science of Learning with education for older
children in formal school settings (Dunlosky, Rawson,
Marsh, Nathan, & Willingham, 2013).
The impetus for this new area comes not only from
advances in basic brain science and computer science but
also from problems in our current educational system, which
is based on what Papert (1993) identified as instructionism.
In the introductory chapter to The Cambridge Handbook of
the Learning Sciences, Sawyer (2006) suggested that “instruc-
tionism is an anachronism ... students cannot learn deeper
conceptual understanding simply from teachers instructing
them better ... learners are not empty vessels waiting to be
filled” (p. 2). Indeed, Mayer conceptualized how we have
moved from instructionism (what Mayer called “response
acquisition”) to a more constructivist and active view of the
learner over the last 100 years (Mayer, 1992).
The study of learning and the melding of research psy-
chology and educational practice is not new. For
centuries, the view that learning was synonymous with
conditioning was the prevailing viewpoint, a dominant
theory originating from Plato and Aristotle and espoused
by John Locke and David Hume. In this view, the envi-
ronment plays a key role in building associations and is
solely responsible for learning. Behaviorism reflects a
refinement of these views and emphasizes how children
learn via conditioning. But by the mid-20th century, a
cognitive revolution had taken hold. Instead of an empha-
sis on how behaviors are brought about by conditioning
or building associations, the “black box” of the mind
began to play a key role in our understanding of learn-
ing. From Miller’s (1956) study of memory to Chomsky’s
(1965) view of language learning via the brain’s “Language
Acquisition Device,” the middle of the last century marked
a turning point (Gardner, 1985): Implicit learning pro-
cesses were posited, and equating psychology with the
science of behavior lost ground.
This same change characterized the study of children
and reinvigorated interest in early experience and the
works of Jean Piaget, who had been writing since the
1920s (Flavell, 1963). The father of constructivism, Piaget
(1923/1965) heralded the idea that children are “little sci-
entists” and actively construct their knowledge of the
world—from relying on sensorimotor schema to remem-
ber and find hidden objects as infants (i.e., object perma-
nence) to gaining symbolic understanding and more
complex thinking throughout childhood (Gopnik,
Meltzoff, & Kuhl, 1999). Urie Bronfenbrenner (1979)
added to these theories by focusing the field on the
importance of the context, culture, and environment. He
also bolstered awareness of the need for a forum that
would include education and public policy. This renewed
interest in child development and the linking of educa-
tion and public policy helped to set the stage for the
cross-disciplinary approach espoused by the Science of
Learning.
The efficacy of this approach is impressive. The study
of dead reckoning in ants and animal species has taught
us about the basics of human navigation and spatial
learning (Cheng & Gallistel, 1984). The vast advances
and remaining challenges in machine learning have
taught us about the intricacy of human thinking (e.g.,
Kotsiantis, Zaharakis, & Pinelas, 2006). Statistical models
have revolutionized the way we think about how chil-
dren and adults learn to make sense of a world full of
data (e.g., Brady, Konkle, & Alvarez, 2009; Buchsbaum,
Gopnik, Griffiths, & Shafto, 2011; Munakata & McClelland,
2003; Xu & Garcia, 2008). In this article, we ask what the
Science of Learning has taught us about how humans
(particularly children)—rather than machines, neural net-
works, or animals—learn.
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Putting Education in “Educational” Apps 7
The Four Pillars: Where the Science of
Learning Meets App Development and
Design
A few well-agreed-upon pillars of learning at the core of
the learning sciences have remained steady through the
decades. Humans learn best when they are actively
involved (“minds-on”), engaged with the learning materi-
als and undistracted by peripheral elements, have mean-
ingful experiences that relate to their lives, and socially
interact with others in high-quality ways around new
material, within a context that provides a clear learning
goal. The pedagogical structure of the environment
determines what kind of learning will result. For exam-
ple, drill and practice may foster rote learning of facts,
but it is not likely to promote deeper conceptual under-
standing (see Ravitch, 2010). Similarly, exploration and
discovery without any guidance or scaffolding may not
provide enough support for learning (Mayer, 2004).
Effective learning is facilitated in a flexible context that
supports scaffolded exploration, questioning, and discov-
ery as children work toward well-defined learning goals
(Darling-Hammond, 2008).
When apps instantiate the pillars within the context of
scaffolded exploration, their use contrasts sharply with
the instructionism that many schools still use to educate
children. The “modern” classroom of 2015 may not differ
much from a classroom from earlier generations: desks in
rows, children listening in their seats or on a rug, and
teachers transmitting well-worn knowledge that students
regurgitate to get their grade. These images were rein-
forced by the No Child Left Behind (NCLB) Act, which
was passed in 2001 and in effect until 2012. While noble
in its aim to provide a quality education to all children
regardless of age, race, socioeconomic status, or location,
the implementation of NCLB has resulted in a test-focused
system that emphasizes teaching to the test and drilling
students for factoids (Darling-Hammond & Adamson,
2014; Ravitch, 2010) and has been ineffective at closing
the achievement gap (Dillon, 2009). Critics worry that
despite efforts to remedy the situation with the Common
Core, a test-conscious education system might inadver-
tently emphasize a teach-to-the-test mentality and result
in less effective learning overall (Roediger, 2014).
Findings from the Science of Learning suggest an alter-
native approach to supporting educational experiences,
including the four evidence-based pillars of learning that
provide a starting foundation for the next wave of educa-
tional apps. These are not novel ideas; our application of
these ideas to app creation is. For instance, Chi (2009)
has provided a taxonomy for learning that includes three
levels: active, constructive, and interactive learning. As
she interprets the psychological literature, socially inter-
active learning with another person is better than
constructive learning, in which the child goes beyond a
presented problem to generate a new understanding. In
Chi’s taxonomy, socially interactive and constructive
learning trump active learning, in which a child does
something such as manipulate objects or rehearse mate-
rial; in turn, active learning is better than learning through
listening in the absence of activity.
Although our focus on cognitive activity and social
interaction overlap with Chi’s approach, our goals are dif-
ferent from hers. Whereas Chi’s taxonomy provides a
testable hypothesis intended to advance learning theory,
our four pillars are meant to inform the design and evalu-
ation of a particular class of learning environments—
namely, touch-screen apps. We recognize that learning
need not always be active or social (Dunn etal., 1990), as
research has suggested that direct instruction methods in
which the impetus is on the teacher to present material
can be effective, even for young children or those with
intellectual disabilities (Przychodzin-Havis et al., 2005).
Yet active involvement in a task and social interaction
both appear to be potent ingredients that stimulate learn-
ing (Meltzoff, Kuhl, Movellan, & Sejnowski, 2009;
Okumura, Kanakogi, Kanda, Ishiguro, & Itakura, 2013).
These pillars, which will be described in greater detail
below, are child-centric, meaning that they apply to how
children are involved (or not) in the learning experience.
Is the child active and minds-on (Duckworth, Easley,
Hawkins, & Henriques, 1990)? Is the child engaged in the
learning experience and remaining on task? Is the child
finding meaning that goes beyond the app? Is the child
engaged in high-quality social interaction with others
while playing with the screen? And does the app provide
a learning goal?
Much like the producers and developers of television,
games, and other digital media, some developers have
clear learning goals in mind when designing and market-
ing an app (e.g., to teach children about numeracy), and
others have no clear learning goals for their apps and
design them only to be entertaining. One popular chil-
dren’s app developer, Toca Boca, has apps that are often
at the top of the “education” category in the App Store.
However, Toca Boca’s CEO, Björn Jeffrey, recently stated
(Banville, 2014):
My argument would be: Education is great and it
has its place, but there are other things we can do
for children other than just educate them. Just
looking at learning from a broader sense, there are
things you can learn . . . that are not from a strict
curriculum perspective—things like collaborating
or using your imagination or being creative. There’s
a place for that in an educational context, but they
are also things that can be just learned from doing
completely different things.... I don’t see us as an
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8 Hirsh-Pasek et al.
educational company. I see us as a company that
makes apps for children or digital toys for children
or, more simply, products for children, but it is
about the children first. If they can be used in an
educational context, great, but that’s not the intent.
This quote highlights a distinction that is critical when
thinking about “educational” apps. Throughout this piece,
we use “learning” and “education” interchangeably to
refer to learning in a general sense. This is important to
clarify, because the term “educational” may be interpreted
as describing a formal learning context such as school-
based instruction or tutoring. As decades of research
have shown, learning and education occur in both formal
(i.e., school) and informal (e.g., museums, the play-
ground, home) settings. When the term “educational” is
thought about broadly as inspiring “learning,” we begin
to more clearly define what we mean by educational
apps.
As a starting point, our pillars are applied to another
medium that has featured a torrent of “educational” pro-
gramming that preceded scientific research. Indeed, 40
years of studies on educational television programming
have helped to document the effectiveness of quality
viewing experiences on a range of academic subjects,
including reading, math, science, and other content areas
(see Fisch, 2004; Fisch & Truglio, 2001, for reviews).
These studies offer a window into many of the develop-
mental challenges, limitations, and opportunities for
using screen media as a learning tool with young
children.
To make the case for a new conceptualization of “edu-
cational” apps, we will (a) describe and operationalize
each pillar in depth, (b) ask how the pillar applies to
television research, and (c) describe the ways in which
the pillar could apply to the evaluation, development,
and design of apps for young children.
Active learning
Evidence from the Science of Learning. The idea
that children play an active role in their own learning has
been reinforced since the days of Piaget and Vygotsky.
Piaget and other constructivists suggested that children
are active knowledge builders—they do not simply
observe what is going on around them and copy it or
wait for others to teach them.
When it comes to apps, we need to draw a distinction
between being physically active and mentally active,
because access to every app demands at least some
physical activity. To qualify as active in our pillar, chil-
dren cannot simply tap or swipe, but rather must be
minds-on. We use the term “minds-on” to distinguish
between physical activities that can be done with
relatively little mental effort and those activities that
require thinking and intellectual manipulation. Tapping
in a response to something on a screen to make it rise is
“minds-off,” but activities such as purposefully figuring
out where a puzzle piece goes or learning about abstract
concepts such as cardinality or addition are minds-on.
The literature amply demonstrates the importance of
active or minds-on learning. Grabinger and Dunlap
(1995), for example, described rich environments for
active learning (REALs) as “[providing] learning activities
that engage students in a continuous collaborative pro-
cess of building and reshaping understanding as a natu-
ral consequence of their experiences and interactions
within learning environments that authentically reflect
the world around them” (p. 5). This is the type of active
learning that is critical for fueling children’s knowledge.
Another study examined college students who were
instructed to learn material with the expectation of teach-
ing it to another student. These students learned the
material better than students who just thought they would
be tested (Benware & Deci, 1984). Having to teach puts
students in a more active, minds-on mind-set for learning
the material. The authors stated that “subjects who
learned in order to teach were more intrinsically moti-
vated, had higher conceptual learning scores, and
...were more actively engaged...than subjects who
learned in order to be examined” (p. 755). Further, recent
experiments involving the teaching of physics have illus-
trated that college students learn more by actively con-
structing their knowledge with others than by listening to
lectures (Mazur, 2009).
If adults are presented with a word pair in which one
of the words has a few letters missing and are asked to
generate the full word, they will remember the pair better
than if they passively read it (Hirshman & Bjork, 1988).
The benefit of generating responses is not even limited to
accuracy. When adults generate an incorrect response
and are then given feedback, they show better retention
than if they either were provided with or chose a correct
answer (Potts & Shanks, 2014). When adults were tasked
with learning how to tie a nautical knot via video, they
performed better when they were allowed to actively
manipulate the video (e.g., to pause or rewind it) than
when they were permitted only to watch it (Schwan &
Riempp, 2004). Adults show better problem solving and
learning when they are allowed to take notes than when
they are not (Trafton & Trickett, 2001). Taking notes by
hand versus on a laptop results in better conceptual
understanding, despite the fact that students take a
greater quantity of notes when typing, possibly because
the slower process of handwriting requires them to select
and synthesize information (Mueller & Oppenheimer,
2014). Medical residents who were instructed to mentally
practice before performing surgery in a virtual-reality
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Putting Education in “Educational” Apps 9
environment showed better performance than those who
watched an online lecture (Arora et al., 2011). College
students asked to use mental imagery when reading
about a topic remembered more and transferred this
knowledge more than a group that simply read the text
(Leopold & Mayer, 2014).
These results are not confined to adult learning. Active
learning also boosts academic and social outcomes for
children. Middle schoolers asked to actively draw chemi-
cal reactions rather than just explore them with dynamic
visualization had a better understanding of the underly-
ing mechanisms (Zhang & Linn, 2011). At science muse-
ums, children who are active in their experience (e.g.,
those who ask or answer questions, comment on what
they see, etc.) learn more than those who are not (Borun,
Chambers, & Cleghorn, 1996; see Haden, 2002, for a
review). Ninth graders who generated drawings when
reading about chemical processes outperformed those
who only read (Schwamborn, Mayer, Thillmann, Leopold,
& Leutner, 2010). Finally, high school students involved
in an active-learning lesson about chemistry had fewer
misconceptions and a more positive attitude compared to
those who learned in a more traditional format (Sesen &
Tarhan, 2010).
Recent work has suggested that there might even be
neural differences when children are active versus pas-
sive during a learning experience. When 5- to 6-year-old
children actively manipulated an object while hearing a
new label and then heard that label again, motor areas of
their brains were more likely to be activated upon subse-
quent viewing compared with when they were only
allowed to passively watch an experimenter manipulate
an object (James & Swain, 2011). Furthermore, motor
areas are activated to a greater extent when objects are
learned actively versus passively. Similar increased
recruitment of sensorimotor brain areas occurs when
children write letters versus when they watch an experi-
menter write (Kersey & James, 2013).
One study used a presented storybook reading with
preschool children who had low expressive vocabularies.
The researchers found that “dialogic reading,” in which
the adult involves the child in the story by prompting him
or her and soliciting talk about the content, resulted in
higher vocabulary gains than traditional storybook read-
ing in which the child listened silently (Hargrave &
Sénéchal, 2000). Similarly, children who ask more ques-
tions and label objects during storybook reading compre-
hend more novel words than those who passively listen
to the same story (Sénéchal, Thomas, & Monker, 1995).
Active learning also benefits vocabulary learning.
When 3-year-old children figured out the referent of a
novel label through a process of elimination, they showed
better retention of that label than children who were
explicitly and directly told the label (Zosh, Brinster, &
Halberda, 2013). Impressively, in the Zosh etal. (2013)
study, children learned more in the active-learning condi-
tion even though they spent less time looking at the
target object.
Active manipulation appears to be key for supporting
minds-on learning, even for infants. When 3-month-old
infants were outfitted with mittens that had Velcro that
stuck to objects, they learned about having a goal and
reaching to achieve it. This, in turn, made them more
likely to interpret the actions of others as goal-directed
(Sommerville, Woodward, & Needham, 2005). Similarly,
this active experience appears to help “jump-start” infants’
own reaching behaviors (Libertus & Needham, 2010).
This effect holds throughout infancy, with action produc-
tion helping 12-month-old infants interpret the goal of
another’s action (Cannon, Woodward, Gredebäck, von
Hofsten, & Turek, 2013).
The results of these studies are clear: Learning is not
simply a passive registration of information, nor is it sim-
ply a result of any type of physical activity. Learning that
“sticks” requires active, minds-on learning.
Active, “minds-on” learning in television. Research
also suggests that as viewers, children learn best when
they are not passive, but rather active and engaged. Chil-
dren make decisions about what, when, and how much
television they view based on ongoing sophisticated
decision-making processes. Huston and Wright (1983)
suggested that children watching a television program
are actually sampling small parts of the program by
glancing quickly at the screen or monitoring the audio,
and making ongoing judgments about the content. If
children judge the program to be understandable and
interesting, it is more likely that they will keep viewing
(Anderson & Lorch, 1983). Anderson, Choi, and Lorch
(1987) also observed that a child is more likely to con-
tinue looking at the television if he or she has already
been looking for a period of time. In other words, when
a child first looks at a screen, the chance that he or she
will look away is at its highest; however, as the child
continues viewing, the chances that he or she will look
away go down. This phenomenon is called attentional
inertia, and it has been documented with children watch-
ing Sesame Street (Anderson et al., 1987; Anderson &
Lorch, 1983). When children actively make decisions to
watch and to stay on task, they learn more from educa-
tional programming (Lorch, Anderson, & Levin, 1979).
Formal features of television—such as cuts, move-
ment, sound effects, music, montage, visual effects, and
so on—are modified by producers based on a program’s
content and viewing audience. Features such as the use
of animation or child-directed speech may serve as
instant markers of programming that is interesting or rel-
evant to a child. The child may be more likely to form an
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10 Hirsh-Pasek et al.
immediate expectation that the program will be compre-
hensible and worthy of attention (Huston, Bickham, Lee,
& Wright, 2007). Children’s television producers may also
use formal features to direct attention to important con-
cepts or ideas, such as a sound effect to cue children to
pay attention to the appearance of a character. These
methods are designed to promote minds-on thinking.
Studies of Sesame Street, which incorporates child-
development research into production, have shown that
children ages 3 to 5 can effectively learn vocabulary,
body parts, numbers, initial sounds, decoding, and count-
ing strategies after viewing (Bogartz & Ball, 1971; Rice,
Huston, Truglio, & Wright, 1990). Programs such as Mister
Rogers’ Neighborhood, in which Fred Rogers directly
addressed children, or Barney & Friends, which incorpo-
rated music into its plotlines, have been found to have
positive effects on social-emotional outcomes (Coates,
Pusser, & Goodman, 1976; Singer & Singer, 1998).
Applying active, minds-on learning to apps. Activ-
ity in the context of apps can take a number of forms. For
example, children can touch the screen (e.g., poke,
swipe, pinch), move the device (e.g., shake, tilt, point),
talk or sing into the microphone, listen to music through
speakers or headphones, and wave for a camera con-
nected to gesture-recognition software. But merely tap-
ping a finger or swiping a screen does not qualify as the
kinds of minds-on activity that underpins learning. These
behaviors require little mental attention. For cognitively
active learning to occur, there must be more than mind-
less, stimulus-response reactions to on-screen actions.
For example, swiping diagonally across a screen can be
a strategic move for a child solving a navigation problem
in a mapping game or simply a superficial response to
moving objects in an arcade-style spaceship game. A
child can move her arm to mindlessly blow out a “can-
dle” on an app or, in a collaborative music activity using
two wirelessly connected mobile devices with motion
sensors, a more experienced child can guide the learning
of a novice peer by changing the tempo of her arm
movements to establish a new rhythm to imitate. Like-
wise, in a host of other ways, apps can be designed
around the affordances of mobile devices to incorporate
physical activity and other experiences to spur children’s
minds-on engagement with app content.
The level of mental involvement for children increases
when apps include symbolic systems that promote learn-
ing potential. Consider the range of cognitive activities
involved in learning to understand oral language, written
language, number lines, musical scales, geographic maps,
visual icons, and so forth. When young children first
encounter these various forms of representation, apps
can provide many opportunities for active cognition—
interpretation, translation from words to mental images,
and manipulation of symbolic material. To the extent that
children proactively engage with representations, they
are likely to learn lessons afforded by the particular sym-
bolic system involved. For example, a mathematics app
designed to build skills at understanding quantity may
usefully present analog representations of physical
objects (e.g., photos of red rubber balls) while support-
ing direct manipulation of these virtual objects, together
with verbal labeling of quantities (“You found 5 balls!”)
and numerical representations (“5”). Similarly, literacy
apps can guide children to form letters into words or
arrange words into sentences with the aim of communi-
cating with another person. In music apps, children can
touch notes to hear corresponding sounds or arrange
them on a staff before playing a completed melody.
This flexibility lets designers arrange symbolic material
to support active cognition and minds-on behavior at
various levels of expertise in a domain. Newer apps just
entering the marketplace even allow children to jump
from the screen to manipulables and back again. An app
called Words for Osmo allows players to look at a picture
on the screen showing an object (e.g., a bear) and a
series of spaces that represent the letters in the word for
that object (in this example, four letters). Using real tiles,
children try to guess the word and align the manipulable
tiles to spell that word. The app diagnoses progress
through what is called reflective artificial intelligence
using a built-in mirror. When children guess the word
and spell it correctly, they are rewarded with on-screen
feedback.
Finally, parents and children can be actively involved
with an app when they use it as a platform to discover
new information about a content area. For example,
“interactive” book apps can encourage parent-child dia-
log that stimulates children’s understanding of story con-
tent. An app that promotes mental activity might require
children to choose among story characters or objects that
further the story line. An app that allows children to build
musical compositions actively supports discovery of
chord progressions and aspects of melody.
Control has been cited as a factor in why apps capture
attention, especially as it pertains to interactions with
software. Well-designed software affords children an
appropriate level of control and agency depending on
their age and experience, allowing them to proceed at
their own pace and sustain their interest. For example,
children who read computer e-books with adults paid
more attention to the story when they controlled the
computer mouse than when the adult controlled the
mouse (Calvert, Strong, & Gallagher, 2005). When adults
controlled the mouse, children’s attention waned over
multiple readings. This is an especially important point to
consider, because many preschool-aged children lack the
skills to effectively control a mouse or keyboard (Revelle
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Putting Education in “Educational” Apps 11
& Strommen, 1990). Touch-screen apps, in contrast, may
be controllable by children of almost any age, depending
on how they are designed.
Research has demonstrated that children must be
minds-on in a task to maximize learning. But being
minds-on is not enough—children also need to stay on-
task and engage in the learning process. If a child is
active and asking questions while reading a story and
then a fire alarm goes off, the child’s learning is disrupted.
Similarly, if a child is reading a story in an app and there
are pop-up features and distracting information that take
away from the story line, his or her learning may be
compromised.
Next, we turn our attention to children’s engagement—
that is, their staying focused and on task.
Engagement in the learning process
Evidence from the Science of Learning. The study of
engagement often centers on the idea of student engage-
ment in the classroom. In a review of the literature, Fred-
ricks, Blumenfeld, and Paris (2004) suggested three kinds
of engagement: behavioral engagement (i.e., rule-follow-
ing, effort, persistence, participation in programs), emo-
tional engagement (i.e., affective reactions), and cognitive
engagement (i.e., investment in learning, flexibility in
problem solving). Each type of engagement is critical for
learning because they all foster staying on task. The Sci-
ence of Learning has highlighted the importance of
focused engagement in learning in early childhood.
Engagement and distraction have also been extensively
studied in the context of executive functions—an
umbrella term that covers flexibility in thinking, problem
solving, inhibition of behavior, and attention (see Zelazo,
Muller, Frye, & Marcovitch, 2003, for a review).
Indeed, engagement is evidenced from the earliest
ages. When a child looks at a toy and a parent zooms in
on the child’s focus of attention and talks about the focus
of the child’s gaze, that child is already engaged. When a
child insists that a parent read the same book over and
over during the bedtime routine, the child is engaged. At
its foundation, engagement in all of its forms is predi-
cated on an individual’s ability to stay on task and not be
distracted. In a recent article, Mayer (2014c) came to a
similar conclusion. He spoke of the “coherence” princi-
ple, noting both that people learn more deeply when
extraneous material is excluded and that extraneous pro-
cessing can “drain limited cognitive processing capacity”
(p. 61).
Distraction is becoming a key area of research in the
study of engagement, as children’s normal environments
seem to require constant multitasking. Research on adult
multitasking in the context of texting and driving has
suggested that only 2% of adults are “super taskers” who
can effectively multitask without cognitive overload
(Watson & Strayer, 2010). Research on children has
shown much the same. Background television serves to
distract young children. Even if they spend only a few
seconds looking at the screen, their play is disrupted: The
length of time they play with a toy is decreased, as is
their level of focused attention (Schmidt, Pempek,
Kirkorian, Lund, & Anderson, 2008). Similarly, the quality
and quantity of parent-child interaction decreases when
a television is on in the background (Kirkorian, Pempek,
Murphy, Schmidt, & Anderson, 2009), and parents are
more likely to talk to and play with their infants when
background television is off than when it is on (Courage,
Murphy, Goulding, & Setliff, 2010; Pempek, Kirkorian, &
Anderson, 2014). Despite these findings, Masur and Flynn
(2008) have reported that television is on at least half the
time during children’s solo play and dyadic play in 44%
and 53% of households, respectively.
Children’s engagement during learning can be dis-
rupted in other ways. Tare, Chiong, Ganea, and DeLoache
(2010), for example, found that children learned fewer
novel words and fewer facts from a pop-up book relative
to a simpler, unenhanced storybook. Even when extra
features were designed to call attention to a specific
learning goal (e.g., letters in an alphabet book), children
learned best when they were able to stay on task using a
simpler version of the book (Chiong & DeLoache, 2012).
Barr, Shuck, Salerno, Atkinson, and Linebarger (2010)
noted that even background instrumental music can dis-
tract infants from learning a new action. Parish-Morris,
Mahajan, Hirsh-Pasek, Golinkoff, and Collins (2013)
found that the “bells and whistles” embedded in an
e-book often distracted 3-year-olds from understanding
and remembering the story. In research on textbooks,
entertaining content or information that is not relevant to
the author’s intended theme are known as “seductive
details” (Garner, Brown, Sanders, & Menke, 1992), which
interfere with the retention of the target information.
Preschoolers are particularly susceptible to distraction,
not having much ability to inhibit attention to extraneous
information. Kannass and Colombo (2007) examined the
task performance of 3.5- and 4-year-old children under
conditions of no distractions, continuous distractions,
and intermittent distractions. For the younger children,
any type of distraction resulted in impaired task perfor-
mance, whereas for the 4-year-old children, only continu-
ous distraction impaired performance. Individual
differences also exist in children’s susceptibility to dis-
tractions (Choudhury & Gorman, 2000; Dixon, Salley, &
Clements, 2006). Sustained attention at age 5 (measured
by lack of impulsivity and focused attention) negatively
predicts attention problems at age 9 (Martin, Razza, &
Brooks-Gunn, 2012), and research with an at-risk sample
suggested that these two factors are associated with
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12 Hirsh-Pasek et al.
specific outcomes: Focused attention at age 5 predicts
achievement outcomes at age 9, whereas increased
impulsivity predicts more negative behavioral outcomes
(Razza, Martin, & Brooks-Gunn, 2012). Kindergartners in
a highly decorated classroom who have not yet mastered
the ability to regulate their attention are more distracted,
spend more time off-task, and have fewer subsequent
learning gains than those in a less distracting environ-
ment (A. V. Fisher, Godwin, & Seltman, 2014). However,
susceptibility to distraction is malleable for children and
adults (Kannass, Colombo, & Wyss, 2010; Neville etal.,
2013), which suggests that the environment can help, or
hinder, one’s ability to stay engaged.
The danger of distraction is apparent throughout
childhood and adulthood. When college students multi-
tasked on a laptop during a lecture, not only did they
score lower on a test, but so did others in direct view of
that laptop (Sana, Weston, & Cepeda, 2013). Further evi-
dence to this point is the finding that texting during class
results in decreased performance (Dietz & Henrich,
2014).
Engaged learning in television. To promote engage-
ment, we must maximize strategies that help the learner
stay on task and reduce impediments that distract learn-
ers and sap needed cognitive resources (Benassi etal.,
2014). Television research suggests that one way to
accomplish this goal is to ensure that the challenge pre-
sented in a medium—whether television or an app—hits
the “sweet spot. As with Goldilocks, a program must be
“just right,” presenting material that strikes an optimum
balance between being challenging and accessible. If
content is too easy or too familiar, children may stop
watching. Conversely, content that is too challenging or
unfamiliar may also turn children away from viewing.
However, when a program falls in between, children are
more likely to pay attention and demonstrate interest.
This is known as the traveling lens model of viewing
(Wright & Huston, 1983). What is interesting or challeng-
ing to children changes with their age and familiarity
with the content. Judicious use of formal features, such as
cuts, audio cues, visual cues, and other aspects of pro-
duction, may also direct children’s attention to important
content and keep them engaged, or re-recruit attention if
it has been lost (Calvert, 1999; Huston & Wright, 1983;
Huston etal., 1981). With the advent of eye tracking tech-
nology, researchers are in a position with television—and
apps—to begin to probe where children look and for
how long during a show or game. Research of this nature
should assist in determining the location of the “sweet
spot” as well as elements that are needlessly distracting.
Applying engaged learning to apps. The educational
quality of apps depends on their ability to support
children’s engagement with the learning process. This
means avoiding the myriad distractions potentially avail-
able on-screen and allowing for sustained engagement
sufficient to meet the learning goals. Extraneous anima-
tions, sound effects, and tangential games might be
appealing to a child when activated but not add to the
child’s understanding of the primary content because
they disrupt the coherence of the learning experience
and the child’s engagement.
We next examine three elements of app design that
can afford this kind of deep engagement in learning.
Notably, evidence for the effectiveness of many of these
design characteristics has been found in work done more
generally on multimedia learning (Mayer, 2014a).
Contingent interactions. When contingent interac-
tions occur between children and their caregivers, as in
video chats (Roseberry, Hirsh-Pasek, & Golinkoff, 2014),
even 24- to 30-month-olds can learn new words that they
cannot learn when presented noncontingently by a per-
son on television. The contingent interactions that apps
afford are perhaps the most basic element of engage-
ment with a touch screen. When each touch or swipe is
met with an immediate response, children feel in con-
trol, maintain their focus, and continue the interaction.
This sort of responsiveness is a core element of user-
interface design in the field of human-computer interac-
tion (Nielsen, 1993/2014). It is also a growing subject
of investigation among researchers interested in educa-
tional media (Lauricella, Pempek, Barr, & Calvert, 2010).
For example, experimental manipulations that required
children to use a computer to move the story of Dora
the Explorer forward at preselected points were linked
to children’s increased understanding of story content
(Calvert, Strong, Jacobs, & Conger, 2007).
Extrinsic motivation and feedback. Engagement—
and its potential to foster learning—is deepened when
an app responds to children’s activities with meaning-
ful feedback. Apps with an explicit question-answer for-
mat typically provide differential responses to children’s
answers. These responses may include labels of “correct”
or “incorrect,” motivational messages (e.g., “Great work!”
and “Try again”), parasocial displays (e.g., of a crowd
cheering or an animated monkey jumping with joy),
points or badges, and access to additional meaningful
content that progresses the game. By carefully structuring
the feedback as well as allowing progressive access to
content (e.g., presenting more advanced content through
a series of game levels or adaptively, based on user pro-
files), apps can focus children’s attention on the app
experience and extend engagement for a long time.
Children’s engagement in this structured system of
learning and feedback is typically driven by extrinsic
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Putting Education in “Educational” Apps 13
motivation (Ryan & Deci, 2000). Players want to achieve
external rewards and avoid the opposite—typically, the
absence of rewards. However, extrinsic reinforcements
are not limited to question-answer formats but may also
be embedded in a more naturalistic context. For exam-
ple, on-screen environments that let children search for
hidden objects may provide a kind of hide-and-seek
game in which discovering objects is its own reward.
Importantly, praise by an adult or by an app can have
differential effects depending on what is praised. A sig-
nificant body of research conducted by Dweck and col-
leagues (Dweck, 1999, 2006; Gunderson etal., 2013) has
shown that praising children’s intelligence leads them to
avoid the inevitable risks of learning for fear of appearing
stupid and losing face. Alternatively, praising children for
their efforts and hard work helps them understand that
learning is not often instantaneous and motivates them to
persevere through the difficulties they may encounter
and, ultimately, succeed more often. This approach helps
children develop a growth mind-set in which they feel a
sense of control over their own capacity to think and
learn. With this research in mind, the praise offered
through apps should be mindful of praising children for
their effort rather than for their intelligence. The former
can cultivate a growth mind-set, motivating children to
tackle and stay engaged in difficult tasks.
Intrinsic motivation. A driver of app engagement per-
haps most valuable to children’s long-term development
is their intrinsic motivation. Open-ended “sandbox” apps,
which are structured to be as open-ended as play in a
real sandbox, can evoke a player’s unique abilities and
personal passions and create new interests. For instance,
an app like Morton Subotnick’s Pitch Painter, which lets
children place and play musical notes, might awaken an
interest in music. With a poke of their finger, children can
engage in musical play akin to drawing with crayons on
paper. These kinds of user-driven, intrinsically motivat-
ing experiences are known to be deeply engaging for
children and adults alike, as in the experience of “flow,”
in which a person loses his or her sense of time while
engaged in an activity (Csikszentmihalyi, & Csikszentmi-
halyi, 1992).
The type of feedback and rewards offered by apps
should also account for the fact that young children are
intrinsically motivated to learn and solve problems.
Recent work in the classroom has shown that, despite
their widespread use, stickers handed out as rewards
actually dilute children’s internally generated feelings of
accomplishment. Instead of offering stickers or causally
weak information as task-unrelated rewards, research
shows that it is better to reward success with causally rich
information (e.g., about how an object might be used to
achieve a goal; Alvarez & Booth, 2014).
A final consideration for app developers, parents, and
researchers is distraction. Some apps enable children to
tap and swipe the screen in the middle of an ongoing
narrative. These behaviors may activate a new screen,
sound effects, or animations that take the child off-task.
By way of example, in many current storybook apps,
children can activate features that bring them to new
activities during a story reading. An app that focused on
the giant dog Clifford illustrates this problem. The app
began by reading the story to the child, and the narrative
was progressing naturally with an introduction of the
main characters and a story arc when buttons were sud-
denly displayed on the screen and children were asked
to find things that “begin with the letter C.” Breaking the
narrative in this way disrupts learning. Indeed, in an
empirical study, the “bells and whistles” placed within a
story presented on an electronic console interfered with
3-year-olds’ understanding of story elements such as the
plot (Parish-Morris etal., 2013).
Some children are more susceptible to distraction than
others (Choudhury & Gorman, 2000; Dixon etal., 2006).
While one child may be distracted by an “enhancement”
in an app, another may not. Similarly, as the research
shows, distraction is more damaging to younger (3.5years)
than older children (4 years; Kannass & Colombo, 2007).
These findings highlight the importance of creating inter-
faces that enable parents to turn off distracting options.
Using the Science of Learning for guidance, then, we
can conclude that both minds-on learning and sustained
engagement are key factors for successful learning.
Learning may be enhanced when it is made meaningful
as well.
Meaningful learning
Evidence from the Science of Learning. Sustainable
and useful learning comes from experiences that con-
nect to our existing knowledge. Cramming for a final
might help get us a passing grade, but we will be unable
to remember or use that same information the next
week. Indeed, Brown etal. (2014) stated that “people
who learn to extract the key ideas from new material
and organize them into a mental model and connect that
model to prior knowledge show an advantage in learn-
ing complex mastery” (p. 6). Meaningful learning takes
many forms, including learning with a purpose, learning
new material that is personally relevant, and linking new
learning to preexisting knowledge. Decades of research
in the Science of Learning attest to these facts. Bransford
etal. (1999) wrote that if students are to develop com-
petence in an area, they need to have factual knowl-
edge, but “a large set of disconnected facts is not
sufficient” (p. 16). Students also need a conceptual
framework to house these facts and to organize their
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14 Hirsh-Pasek et al.
knowledge in a way that allows them to apply what they
have learned.
The widely cited scholar David Ausubel (1968) theo-
rized that true learning occurs when we make connec-
tions between new material and related content we
already know. He distinguished meaningful learning
from rote learning. Rote learning occurs when new infor-
mation does not link to previously learned content in any
substantial way. In other words, the new information has
no existing information to be “hooked” onto. This is why
rote learning often does not “stick” but fades from mem-
ory. It often lacks meaningful connection to what we
already know.
Shuell (1990) forwarded a similar argument. He held
that rote learning is a precursor to “real” learning, which
takes place only when we develop new understanding
by incorporating newly learned facts into our current
understanding. When we learn the multiplication tables,
for example, we do not yet know division. However, the
multiplication tables gain new importance and meaning
when we begin doing division problems—they become
automatized and serve as the base for the next round of
learning. In other words, complex learning and expertise
are built on having a base of knowledge and skills that
can be fluently retrieved. New mental models and con-
ceptual frameworks are constructed on this base.
In Chi’s (2009) framework, constructive learning is
meaningful when it relies on the active construction of a
mental model of the newly learned material. When mean-
ing is added to rote learning, it propels the change to true
conceptual understanding (Novak, 2002). But this does
not imply that all learning starts out as rote learning; to
the contrary, learning can be meaningful from the start.
Though rote learning may be useful in some situations, it
can often be very shallow. For example, we can imagine
children in the fifth grade working with an app that
makes a game of memorizing the names of the presi-
dents in order. While children can do this, if they have no
idea that the United States is a democracy and that the
presidents are elected rather than chosen by a king, their
understanding of “presidents” is limited. Until children
learn about the United States’ system of government, the
names of the presidents are just that—names memorized
in a vague context.
No doubt, the original “base of knowledge and skills”
upon which more meaningful learning is established will
need to be built. Acquisition of such a base seems to
depend at least in part on drill and practice. For example,
learning the multiplication tables is needed for doing
mathematics; learning the meaning of many vocabulary
words is needed for apprehending school content in read-
ing, science, and social studies. Judging from existing
work on middle school children, this may be where the
use of apps will shine. Apps offer tremendous potential to
support this kind of practice. When computer tutors—
which could be instantiated as apps—have been com-
pared with self-study, they have been shown to produce
greater gains in learning and retention (e.g., Metcalfe,
Kornell, & Son, 2007). Such apps could be invaluable,
making it possible for many more students to profit from
the higher-order conceptual learning advanced in school.
And even more to the point is an article by Walker, Mickes,
Bajic, Nailon, and Rickard (2013) that reported that old-
fashioned drill and practice with the multiplication tables
leads to more fluent learning of these basic facts than
does instruction with a more conceptually oriented “fact
triangle” approach. Thus, the story on meaningful learn-
ing is nuanced: Sometimes apps that invite drill and prac-
tice and are instantiated in a game-like framework can be
educational and effective for building up the base on
which meaningful learning rests.
Sometimes, however, promoting meaningful learning
depends on contexts that stimulate greater motivation. A
child may be more motivated to learn fractions, for exam-
ple, by dividing Halloween candy among siblings than by
answering problems posed on a worksheet. Similarly,
playing a game in which math skills are embedded within
a story line of feeding a school of fish or serving people
in a pizzeria allows children to see potential real-world
applications of mathematical concepts. The benefit of
context and meaning is apparent even in infancy. By
14months of age, learning about the function of a novel
object helps infants categorize objects (Booth & Waxman,
2002). Further, early word learning is “smart,” such that
infants will only extend a novel label to an appropriate
object based on what they know about that kind of
object. For example, children are likely to extend their
new word “chair” to other artifacts with roughly the same
shape (Landau, Smith, & Jones, 1998).
When words were embedded in a written passage,
middle school children showed vocabulary gains (Nagy,
Herman, & Anderson, 1985) relative to a control group
that did not see the passage. This effect is probably due
to the fact that the words were couched in a narrative
that exemplified their meaning. Learning meaningful
information motivates children to stay engaged and on-
task. If children are given causally rich information about
a novel object, they will stay engaged in a boring task
that rewards them with this information. For example,
children were more likely to continue with placing pegs
in a board if they were prompted with a picture of a
novel object and given new, meaningful information
about that object. These children were more likely to
continue with the boring task than children who received
less rich information or even tangible rewards (stickers;
Alvarez & Booth, 2014). Finally, meaning is invoked
when learning contexts are familiar. Hudson and Nelson
(1983) found that children 4 to 7 years of age are likely
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Putting Education in “Educational” Apps 15
to remember more story events when the narrative they
are hearing is familiar (e.g., about a birthday party) ver-
sus unfamiliar (e.g., about baking cookies).
Meaningful learning also produces effects for adults.
In a health care context, learning within the context of a
narrative helps both patients and doctors. For instance,
Hinyard and Kreuter (2007) stated:
To date, the dominant paradigm in health
communication has involved using statistical
evidence, probability, and appeals to logic and
reason to persuade and motivate people to adopt
behavioral changes. Increasingly, however, health
communication developers are turning to narrative
forms of communication like entertainment
education, storytelling, and testimonials to help
achieve those same objectives (p. 777).
When health information is embedded within a mean-
ingful context, outcomes improve. For example, doctors
who are told a narrative about a patient are more likely
to remember the guidelines for prescribing opioids than
are those who are simply told the guidelines (Kilaru
etal., 2014). The latter are likely to forget the guidelines
quickly and even make up guidelines that do not exist.
For college students, taking longhand notes results in
better learning, likely because students are looking for
meaning and more deeply processing the lecture mate-
rial than when they type verbatim notes (Mueller &
Oppenheimer, 2014). Indeed, the brain processes familiar
and novel information differently. Presenting adults with
familiar faces that are meaningful to them recruits a
broader network of brain regions than does presenting
novel faces (Heisz, Shedden, & McIntosh, 2012). When
adults are presented with novel shapes but find meaning
in those shapes (i.e., a potato chip that looks like Elvis),
their pattern of brain activation differs from when they
do not find additional meaning (Voss, Federmeier, &
Paller, 2012). Thus, and this may be the key, when we
process information that is more meaningful, we often
(though not always) are more mentally active, making
more connections across brain areas.
How is newly learned information meaningfully
encoded to form new memories? Levels-of-processing
theory suggests that the depth at which an item is men-
tally processed, or elaborated on, determines the strength
of memory for that item (Craik & Tulving, 1975). The
durability of a memory trace is related to the semantic
depth at which it is processed. Deeper processing, such
as accessing the semantic meaning of a word, produces a
stronger memory trace than processing that same word at
a more shallow level, such as noting its orthography or
phonemic features. Although its main application has
been in verbal learning, this framework closely resembles
the theory proposed by Ausubel (1968). In this way, pho-
netic processing parallels rote learning, as they both
incur a less durable memory trace. Meaningful learning
that is connected to prior knowledge allows us to tap
into deeper semantic levels of processing.
Another indicator that meaningful learning has
occurred is in problem solving and cognitive flexibility. If
learners have truly created a new understanding of a
concept, they should be able to use that information to
solve novel problems and flexibly transfer that knowl-
edge to other problems (Goldstone & Day, 2012). If, for
example, a child knows what “half” means only when
asked about a cookie split between two siblings, learning
is not complete. Similarly, a toddler’s having memorized
that 2 plus 2 equals 4 but not knowing that 2 plus 1
equals 3 suggests that the numbers do not have meaning
for him or her.
Meaningful learning in television. Several studies
have demonstrated that children better learn educational
content from television when it is “on the plotline.” Chil-
dren are better able to recall educational content that is
directly tied to the narrative of a program (Fisch, 2004;
Hall & Williams, 1993). For example, when children
watch a character in a show solve a mystery by figuring
out the missing letters in a written clue (Fisch, 2004), they
are more likely to remember the spelling of the word
than when the word is just repeated. Characters who
advance the plot by using targeted educational concepts
or content lead to more retention of those concepts by
children. Educational content that is irrelevant or tangen-
tial to the plotline is less likely to be recalled.
Applying meaningful learning to apps. How can an
app be assessed for something as complex and unob-
servable as “meaning”? A reasonable proxy might be to
consider the quantity and quality of connections between
the app experience and the wider circles of a child’s life.
For example, one might ask: Does the app ask the child
to go beyond rote learning? Does the app experience tap
into the child’s personal history, activate prior knowledge
of a subject, or build a rich narrative? Does it extend
important interpersonal experiences with parents, sib-
lings, or peers? How does it connect to the child’s role in
his or her school community and, ultimately, to related
domains of knowledge, such as science, mathematics, or
history (cf. Rogoff, 1995)?
Similar to television content created to be on the plot-
line, apps that require children to solve problems or
demonstrate proficiency in a content area in the service
of a larger game narrative may be more successful than
apps in which challenges are not integrated into the
game’s narrative or context. For example, the game
Motion Math: Pizza! embeds math concepts into the
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16 Hirsh-Pasek et al.
running of a pizzeria. Children must understand money
concepts and how to complete specific mathematical
operations in order to run a successful pizza parlor.
A number of apps in the marketplace require shallow,
rote memorization. For example, an app that asks a child
to touch a triangle and then showers the child with
applause before it moves on to asking about a blue
square is hardly meaningful. Contrast that with an app
that explains and demonstrates that “a triangle has three
sides,” shows the three sides with colorful accents, and
then asks the child to “find the triangles” in an everyday,
meaningful scene in a hidden-pictures task.
Other apps might make connections with the child’s
home environment. Consider an app that engages chil-
dren and parents in mathematics activities around the
home using the device’s camera. The child is asked to
take a photo of something square or of a group of three
things, for example. As children examine and photograph
familiar objects in their home, they connect previous per-
sonal experiences to the app activity—for example,
grouping three favorite dolls for a group portrait brings a
personal meaning to the quantity involved, especially
when the fourth favorite one is necessarily left out.
Children next show the photograph to a parent to assess
the quantity it shows, which builds links between the
app experience and an essential interpersonal relation-
ship. If the child does the same thing with the other par-
ent and an older sibling, these meaningful connections
grow threefold. Finally, children can connect this home
experience with classroom mathematics activities, by
engaging in other activities that involve grouping quanti-
ties in a meaningful new way.
Thus, when thinking about apps, it is important to
promote meaningful learning that goes beyond just learn-
ing that the letter A is made with two long lines with a
short line in between them. A has various sounds; when
followed by an E after a consonant, it sounds like its
name (as in ape); and it also stands for a job well done.
There is no denying that apps can teach children isolated
facts, but meaningful interactions with the content that
link to children’s lives will lead to greater retention and
spur conceptual change.
Active, engaged, and meaningful experiences are three
of the pillars that move deftly from the Science of Learning
into app design. The final pillar is social interaction. At
first glance, this pillar might seem in opposition to the
quiet absorption of a child playing alone with a device in
the back of an SUV during a long road trip. Yet research
suggests that high-quality social interaction can be a key
component of learning, especially for younger children.
More digital experiences need to reflect the idea that
social interactions between children, between children
and adults, and even between on-screen characters like
Elmo and viewers or users spur interest and learning
(Calvert & Richards, 2014).
Social interaction
Evidence from the Science of Learning. Csibra and
Gergely (2009) suggested that the transmission of infor-
mation between individuals acts as a kind of “natural
pedagogy” (p. 148). From within an hour of birth, infants
will imitate a social partner’s tongue protrusion (Meltzoff
& Moore, 1983), and by 12 to 21 days, they can imitate
both facial expressions and manual gestures (Meltzoff &
Moore, 1977). By 6 months of age, infants initiate more
looks toward a caregiver when they are shown some-
thing that violates their expectation than when something
expected happens (Walden, Kim, McCoy, & Karrass,
2007).
Infants’ use of social cues occurs across many con-
texts. The mere presence of a social partner promoted
infant learning of the properties of novel objects: Nine-
month-old infants succeeded in learning only when this
task included the presentation of a face looking at the
stimuli to be learned and a voice saying, “Hi, baby, look
at this!” They failed at the same task without this support
(Wu, Gopnik, Richardson, & Kirkham, 2011). Infants also
readily distinguish between communicative and noncom-
municative contexts, an ability that has far-reaching con-
sequences for learning (Yoon, Johnson, and Csibra,
2008). Simply put, social interaction itself enables learn-
ing. And while the evidence that infants use statistical
reasoning across a variety of domains is clear (Saffran,
Aslin, & Newport, 1996; Saffran & Wilson, 2003; L. Smith
& Yu, 2008; Xu & Garcia, 2008; Yu & Smith, 2007;
Yurovsky, Yu, & Smith, 2012), other research has shown
that when social cues are provided, infants perform even
better in complex situations than if they are provided
only with statistical information (Wu etal., 2011; Yu &
Ballard, 2007).
One domain that appears to particularly benefit from
social learning is language. The positive effect of social
interaction is apparent even at the level of infants’ ability
to discriminate between the phonemes of a new lan-
guage (Kuhl, Tsao, & Liu, 2003). Infants typically lose the
ability to discriminate between phonemes that are not
used in their native language between 6 and 12 months
of age; however, experience with a live speaker talking in
Chinese, but not a video recording of that same speaker,
was enough to allow English-reared infants to maintain
phonemes from Chinese that they would ordinarily have
lost. Kuhl (2007) suggested that not only is social interac-
tion important for this type of early language learning,
but it may act as a gate that is necessary for language
learning:
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Putting Education in “Educational” Apps 17
I advance the hypothesis that the earliest phases of
language acquisition—the developmental transition
from an initial universal state of language processing
to one that is language specific—requires social
interaction. . . . I argue that the social brain “gates”
the computational mechanisms involved in human
language learning (p. 110).
The importance of social interaction for language
learning does not end with phonemic discrimination. By
5 months of age, infants have learned that their vocaliza-
tions impact social partners. They stop vocalizing if their
social partner stops responding to them (i.e., by display-
ing a still face). The magnitude of their response to a still
face predicts their language development 8 months later
(Goldstein, Schwade, & Bornstein, 2009). The respon-
siveness of a caregiver to an infant’s vocalization predicts
that infant’s subsequent vocalizations in general (Dunst,
Gorman, & Hamby, 2010; Tamis-LeMonda etal., 2014)
and the frequency of vocalizations directed toward the
caregiver in the following months (Gros-Louis, West, &
King, 2014). When a parent responds to an infant’s bab-
bling in a contingent way, the infant is more likely to pick
up on phonological patterns and generalize these forms
to his or her own vocalizations (Goldstein & Schwade,
2008). Infants’ ability to gaze follow and point at 10 to 11
months of age predicts their vocabulary at age 2 (Brooks
& Meltzoff, 2008). By 12 months of age, infants will fol-
low the gaze of either a human or a robot, but they will
show increased object learning only when following the
human gaze (Okumura etal., 2013), a finding consistent
with the results of the Wu etal. (2011) study described
above.
Throughout the preschool years, children learn much
from their peers and from other adults. Sawyer (2006)
noted that “outside of formal schooling, almost all learn-
ing occurs in a complex social environment” (p. 9), and
there is agreement that social interaction is central to
learning. In fact, according to Vygotsky (1978), the social
dialogs preschoolers engage in are crucial for advancing
their cognitive development. Laura Berk (2003) described
how her 21-month-old imitated her 4-year-old in pre-
tending to bake a pineapple upside-down cake. The
4-year-old taught his younger sibling the steps—reinforc-
ing his own knowledge—and several hours later, the
21-month-old engaged in many of behaviors he had been
shown earlier—but in pretend play. Social interaction
allows children to observe and imitate older siblings,
peers, and their elders, and in doing so, they learn about
how events in the world typically unfold. But young chil-
dren can learn more than concrete actions from imitation.
Thirty-six-month-olds can even learn rules by imitating
adults, such as by sorting objects by both visible (color)
and nonvisible properties (object noises; Williamson,
Jaswal, & Meltzoff, 2010). Further, preschool children use
statistical information differently if they perceive the
demonstrator to be naïve versus knowledgeable
(Buchsbaum etal., 2011). By the age of 4, children are
able to use the pedagogical intent of a speaker to guide
their inductive generalizations (Butler & Markman, 2012).
Thus, when an adult speaker acting as a teacher said
“Watch this!” while showing children that a (secretly mag-
netized) block could pick up paper clips, children made
a generic inference that other identical blocks should
also pick up paper clips and were surprised when that
did not happen. However, when they were given this
information as if by accident, they did not make that gen-
eralization and did not continue exploring the blocks’
properties.
Social interaction also impacts children’s understand-
ing in school. The benefits of collaborative learning, in
which students work together toward a common learning
goal rather than in solo learning environments, have
been known for decades (see Johnson, Maruyama,
Johnson, Nelson, & Skon, 1981, for a review). It appears
that one specific type of learning might particularly ben-
efit from collaborative learning: critical thinking skills.
Gokhale (1995) directly compared students’ learning of
rote drill-and-practice skills and critical thinking skills
when working alone or working with groups. While stu-
dents in both conditions performed the same on the rote
learning material, those working collaboratively showed
better critical thinking skills. Having to explain one’s rea-
soning to another and think through an argument deep-
ens one’s understanding of the problem at hand.
Ironically, even computer-based learning environ-
ments are capitalizing on social interaction for maximiz-
ing learning. The computer program Betty’s Brain has
students teach a character called a Teachable Agent (TA)
or just learn the material by themselves (not from a
teacher). The use of a TA has paradoxical effects. When
students think they are teaching the TA, they spend more
time learning the material and actually learn more than
when they are learning for themselves. Interestingly, the
effect is strongest for weaker students. Clearly, having a
sense of responsibility to teach another increases chil-
dren’s motivation to learn (Chase, Chin, Oppezzo, &
Schwartz, 2009). Further, when interaction with an avatar
is controlled by a real person rather than a computer,
people experience higher levels of arousal, learn more,
and pay more attention (Okita, Bailenson, & Schwartz,
2008).
Research shows that social contingency in particular is
a key factor in learning. That is, when a back-and-forth
cycle is established between two speakers, in which the
reaction of one speaker is in response to the other, pow-
erful learning can occur. While evidence suggests limited
learning of new words from screen media in the first
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18 Hirsh-Pasek et al.
3years of life (Barr & Wyss, 2008; Krcmar, Grela, & Lin,
2007; Scofield & Williams, 2009), a phenomenon some-
times referred to as a video deficit (Anderson & Pempek,
2005; Robb et al., 2009; Zimmerman et al., 2007), the
undisputed finding is that human interaction trumps
electronic “interaction” (Krcmar etal., 2007; Kuhl etal.,
2003; Reiser, Tessmer, & Phelps, 1984; Roseberry, Hirsh-
Pasek, Parish-Morris, & Golinkoff, 2009). Recent work has
pointed to the natural give-and-take that happens in face-
to-face interaction. In fact, when social contingency was
established in an electronic format (i.e., via Skype or a
similar live video chat program), children learned equally
well from a real person and a “digitally live” on-screen
interaction (Roseberry et al., 2014). They did not learn
from watching a digital interaction between the adult and
another child.
This raises an important point: For social interaction to
benefit learning, it must be high quality. One can easily
imagine how a child may be distracted in the classroom
by other children screaming or playing games. Similarly,
one could imagine a child using an educational app
being distracted by another child (or sibling, or parent)
making off-topic comments. Simply having a social part-
ner is not enough. The social interaction has to be of a
high enough quality that it does not detract from the
learning situation. Overwhelmingly, research in educa-
tion has found that cooperative and collaborative learn-
ing environments are optimal (see Johnson & Johnson,
2009, for a review).
Socially interactive learning in television. Several
authors have pointed out that it makes conceptual sense
to investigate interactivity (and media generally) through
a social lens (Luckin, Connolly, Plowman, & Airey, 2003;
Reeves & Nass, 1996; Richert, Robb, & Smith, 2011; Strom-
men, 2003). Given that media in all forms is overwhelm-
ingly populated by humans and human-like characters,
viewers are really sharing a type of interaction with
another social partner (Reeves & Nass, 1996; Richert
etal., 2011; Strommen, 2003). If media are perceived as
providing social partners, even two-dimensional repre-
sentations of puppets, it will impact how children react to
them. Improving learning from media requires sensitivity
to children’s social expectations and might be achieved
through a variety of means, including contingent feed-
back or a character with whom the child has an emo-
tional relationship (e.g., Elmo from Sesame Street or Dora
from Dora the Explorer; Strommen, 2000). In this context,
parasocial features of television are relevant for children’s
learning from the medium.
Many television studies have supported this notion.
For example, 2-year-olds who had an extended socially
contingent interaction (playing games, answering ques-
tions, etc.) with a person through a television screen
were more likely to successfully complete a search-and-
retrieval task using information provided by the on-
screen person than children who only saw a
noncontingent, prerecorded video of the person (Troseth,
Saylor, & Archer, 2006). Experience successfully interact-
ing with an on-screen partner may contribute to the per-
ception that on-screen events provide information.
Similarly, O’Doherty et al. (2011) found that when
30-month-old children either observed or participated in
a socially reciprocal interactive exchange, they showed
increased word learning relative to when they engaged
in a nonsocial interaction, such as with a person on tele-
vision who was unresponsive to either the child or some-
one else. Contingent social responding is important for
learning—especially for younger children.
Coviewing. Though television is not inherently social,
it morphs into a social activity through coviewing, the act
of watching television with children. Coviewing has been
linked to how well children learn educational content.
Teachers, siblings, peers, and others in a child’s envi-
ronment can be coviewers, but most research to date
has focused on parental coviewing. Coviewing involves
a spectrum of behaviors, from a child and parent quietly
watching a program together to parents actively engag-
ing children to make televised material more accessible.
Effective coviewing behaviors are sensitive to individual
children’s developmental levels and include techniques
such as asking children questions about what they are
seeing or hearing, prompting children to imitate songs
or movements, and pointing out or labeling objects or
actions on screen (Valkenburg, Krcmar, Peeters, & Mar-
seille, 1999).
Coviewing can affect children’s viewing in a number
of ways. For example, Demers, Hanson, Kirkorian,
Pempek, and Anderson (2013) reported that infants were
more likely to look at a television right after a parent
looked toward the screen and to look for a longer period
of time when they switched gaze in response to a par-
ent’s gaze. Parents can also intervene in television view-
ing, adding a social dimension to the experience. Children
watching Sesame Street learned numbers and letters bet-
ter when their parents asked them to name numbers and
letters during a viewing, but not when the parents them-
selves named them (Reiser etal., 1984; Reiser, Williamson,
& Suzuki, 1988; Rice etal., 1990). Children also under-
stood televised stories better and learned more vocabu-
lary when parents used techniques similar to effective
shared book-reading behaviors, including asking open-
ended questions and having children recall stories after
viewing (Strouse, O’Doherty, & Troseth, 2013). However,
a study by DeLoache etal. (2010) using videos designed
to teach vocabulary showed that toddlers did not learn
new words from the videos even when parents coviewed
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Putting Education in “Educational” Apps 19
with them. Children showed word learning only when
their parents interacted with them around the new words
without the video.
Importance of parasocial factors. Finally, relatively
recent research has suggested that children receive edu-
cational benefits when viewing programs with familiar
characters. Children may have parasocial relationships
with on-screen characters, in which children perceive
themselves to be meaningfully interacting with a character
even though the character cannot actually respond. In the
television show Dora the Explorer, Dora asks questions or
gives a prompt, waits for a response, looks directly at the
camera, and pauses for a few seconds as if waiting for the
child to reply. The program has been linked to educational
gains, including improved expressive vocabulary (Line-
barger & Walker, 2005). Characters with which 2-year-olds
have parasocial relationships can also help teach them
early math skills (Lauricella, Gola, & Calvert, 2011) better
than a video of an unfamiliar figure. Parasocial relation-
ships can also help children make healthier food choices
(Kotler, Schiffman, & Hanson, 2012).
Applying social interaction to apps. Apps allow for
a remarkable degree of contingency—but only to a point.
One strength of apps on touch screens is that they allow
for an immediate response when a child makes a selec-
tion via a tap or swipe on the screen. However, apps are
not fully socially interactive and adaptive. Imagine a child
exploring a book via an app. One benefit is that when
the child touches a picture of a cow, he or she will imme-
diately see and hear a cow moo and perhaps even chomp
on some grass. However, most apps cannot respond to a
toddler saying the word “cow” or “moo” with praise and
encouragement (but see progress on this front through
apps like SpeakaZoo and Winston Show; Cha, 2013).
This type of responsivity has been shown to be a key
facilitator of language development in young children
(Dunst et al., 2010; Goldstein & Schwade, 2008; Gros-
Louis etal., 2014; Roseberry etal., 2014; Tamis-LeMonda
etal., 2014). Responsivity is limited in apps compared to
natural human interaction.
The app world invites a number of different social
environments, each of which needs to be taken into
account when we ask about the relationship between
social interaction and learning. App design can incorpo-
rate the potential educational benefits of social interaction
in three ways. First, multiple users may engage in face-to-
face interactions around the screen, perhaps while com-
peting in a game or collaborating on a project. That is,
they can take turns. Or, an app may prompt these kinds
of interactions further away from the screen, as when chil-
dren search together for household objects during a trea-
sure-hunt activity. In any case, apps may provide varying
degrees of structure for these interactions. They may cre-
ate a potentially social context in which two children may
engage in a similar activity at the same time. Alternatively,
they may provide well-defined roles with prompts for
specific educational dialog, such as “scientists” pursuing
the course of systematic inquiry.
Second, users may engage in mediated interactions
through technologies such as video teleconferencing
(e.g., Skype or FaceTime), voice over IP audio, or various
types of screen-sharing apps that allow collaborative
visual communication through typing, drawing, or inter-
acting with virtual objects (e.g., Drawing Together!,
Kindoma, Minecraft). The resulting social interactions
parallel a great deal of what is possible in face-to-face
interactions, with the obvious absence of direct physical
contact between people. Research evidence suggests that
these kinds of mediated social interactions have benefits
for learning similar to those of face-to-face interactions
(Roseberry et al., 2014). Existing research on this topic
has focused on traditional video formats, but newer inter-
active media formats may provide similar effects, provid-
ing an incentive for media creators to design characters
with whom children can relate.
Third, as with television, apps can support develop-
ment of parasocial relations with on-screen characters.
Characters presented on touch-screen devices, however,
can be designed for more realistic two-way interactions
with users. Companies are beginning to create apps with
animated characters that respond to the content of chil-
dren’s speech. These kinds of parasocial interactions for
entertainment and education are likely to expand in com-
ing years.
Active, engaged, meaningful, and socially interactive
experiences support learning, and if these concepts are
harnessed within apps, the potential benefit for learning
in early childhood is significant. Along with the four pil-
lars, a final lesson from the Science of Learning literature
involves the context for learning. Is the context one that
promotes exploration toward a learning goal, or superfi-
cial rote learning? Apps that engage these pillars within
the context of a learning goal are the most likely to be
truly educational.
Scaffolded exploration toward a
learning goal
Evidence from the Science of Learning. The four pil-
lars of active, engaged, meaningful, and socially interac-
tive experiences may be oriented toward entertainment
or educational apps. Research in the Science of Learning
suggests that they are more likely to result in significant
learning if they are embedded in an educational context
that supports scaffolded exploration, questioning, and
discovery in relation to well-defined learning goals.
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20 Hirsh-Pasek et al.
The importance of the learning situation itself cannot
be understated. For apps, it is a primary concern. For
instance, one can easily imagine a game in which a child
is active and engaged but nothing is learned. Research in
the Science of Learning suggests that the context itself
can act as a scaffold, or support, for learning.
According to Sigel (1987), “the child as an active
learner has to have opportunities for self-directed activi-
ties through play and other exploratory adventures as a
means of self-stimulation and healthy development”
(p.214). Sigel and colleagues believed that the optimal
early learning environment promotes physical and cogni-
tive exploration (alone and with others) and a warm,
encouraging atmosphere (Copple, Sigel, & Saunders,
1979). Sigel (1987) wrote,
Children can learn anything if it is properly arranged;
that appropriate structuring of the very young child’s
learning environment with accompanying, properly
calibrated materials will enable that child to learn to
read, to acquire an advanced vocabulary, and to do
arithmetic calculations (p. 212).
Debates about the best learning contexts have been
raging for decades (Hirsh-Pasek & Golinkoff, 2011). One
end of the continuum supports free play, in which a play
situation is not structured or designed in a purposeful
way (P. Gray, 2013). At the other end of the continuum
lies didactic learning, or direct instruction, in which
adults explain to children how something works or what
they need to know. There are merits to both approaches.
A recent meta-analysis examining 164 studies, however,
suggested that direct instruction resulted in better learn-
ing compared to free play in which no learning goal was
defined, but that assisted discovery methods, in which an
adult helped guide the child’s experience but took a sup-
portive rather than primary role, resulted in the best over-
all learning outcomes (Alfieri, Brooks, Aldrich, &
Tenenbaum, 2011).
In education, the use of manipulatives—physical
objects such as blocks that are designed to help children
learn mathematical concepts through hands-on manipu-
lation—is commonplace and has some empirical support
(see Lillard, 2005; Pouw, van Gog, & Paas, 2014; and see
Sowell, 1989, for a review; but see McNeil & Jarvin, 2007;
Uttal, Scudder, & DeLoache, 1997). The key is that chil-
dren are not just actively engaged in a situation but are
given the appropriate tools that, when actively explored,
allow them to acquire a new concept or understanding.
When low-income children play linear board games in
short interventions with an adult and other children, they
gain significantly in their understanding of numbers
(Ramani & Siegler, 2008, 2011; Siegler & Ramani, 2008,
2009). This same benefit has been shown in the use of
dynamic visualizations, rather than static images, for
enhancing student learning of scientific concepts, such as
photosynthesis (Ryoo & Linn, 2012).
In the domain of spatial cognition, K. R. Fisher, Hirsh-
Pasek, Newcombe, and Golinkoff (2013) directly com-
pared learning about geometric knowledge through
direct instruction, free play in which children could do
whatever they wanted with geometric materials, and
guided play in which a more experienced play partner
scaffolded play and followed the child’s lead. Only chil-
dren who learned via guided play transferred their new
understanding of shapes to noncanonical shapes (e.g.,
triangles with acute angles) immediately after training
and 1 week later. In another study, this time focusing on
reading outcomes, at-risk preschoolers showed greater
literacy gains when their instruction was paired with
guided play (Han, Moore, Vukelich, & Buell, 2010).
Guided play (K. R. Fisher, Hirsh-Pasek, Golinkoff, Singer,
& Berk, 2011; Hirsh-Pasek & Golinkoff, 2008) is child
directed, builds upon children’s interests, and entails
interacting with an adult who has a learning goal in mind.
In each of the above studies, playing alone did not gener-
ate as much learning as exploration with a learning
objective in mind. Note also that these experiments using
guided play sit midway between the two anchors of
direct instruction and play in the educational debates
(Hirsh-Pasek & Golinkoff, 2011; Weisberg, Hirsh-Pasek, &
Golinkoff, 2013).
The same benefits of guided play and the importance
of “setting the stage” for learning are seen when it comes
to fostering language development and literacy. Preliminary
evidence from a large-scale, ongoing intervention study
with preschoolers in Head Start indicates that a guided,
play-based vocabulary intervention in which children play
with replicas that relate to a story works as well as an
adult-led, more directed play context (Dickinson, Hirsh-
Pasek, Golinkoff, Nicolopoulou, & Collins, 2013). Both of
these focused, play-based contexts are equivalently suc-
cessful, and both are superior to free play.
However, the Science of Learning does not suggest
that there is no benefit of direct instruction. Direct instruc-
tion can work and in some cases has been found to be
particularly effective (Klahr & Nigam, 2004). Work by
David Klahr and his colleagues has suggested that more
supported learning is the key to mastering second-grade
science. In a comparison of directed learning versus dis-
covery learning, Strand-Cary and Klahr (2008) found that
children showed better mastery of the concept of experi-
mental confounds in both the short term (1 week) and
long term (3 years) when they had been more directly
instructed on the content. A close inspection of Klahr’s
direct-instruction condition, however, renders it some-
what like the guided-play conditions discussed above
(Chi, 2009).
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Putting Education in “Educational” Apps 21
The double-edged sword of a strict and nonresponsive
direct instruction format is that it limits exploration and
may prevent children from learning beyond exactly what
they are taught (Alfieri etal., 2011; Bonawitz etal., 2011).
Bonawitz and Schultz presented 4-year-olds with a toy
that had four hidden functions. In one condition, chil-
dren were told what the toy could do, shown one of its
functions, and then left to explore the toy. In the other
condition, an experimenter accidentally “tripped” on one
of the functions (the same function demonstrated in the
other condition) before the children were left to explore
the toy. Children in the exploration condition were much
more likely to discover all of the toy’s remaining func-
tions, whereas those in the directed condition seemed
restricted to the function that had been shown to them.
Pedagogy can be useful but seems to shortchange explo-
ration and additional learning. In this case, it had the
unanticipated outcome of implying that children had
learned all they needed to know about the novel toy—
otherwise, the “teacher” would have told them more.
Direct instruction coupled with exploration may be
the more effective strategy. DeCaro and Rittle-Johnson
(2012) presented second-, third-, and fourth-grade chil-
dren with unfamiliar math problems and found that chil-
dren who first had the opportunity to try to figure a
problem out for themselves and explore possible solu-
tions before receiving direct instruction showed better
conceptual understanding than children who first
received instruction and were then allowed to practice.
These are exactly the benefits some of us experience
when we teach: Figuring out content in a way that allows
us to explain it makes the subject our own and deepens
our understanding.
Research suggests that giving either complete free rein
to children or using solely the contrasting method of
direct instruction may not be optimal for learning. As
Kagan and Lowenstein (2004) noted, “the literature is
clear: Diverse strategies that combine play and more
structured efforts are effective accelerators of children’s
readiness for school and long-term development” (p. 72).
Guided play, with time for exploration, may increase
learning gains. The main point is that the pedagogy used
for promoting learning has consequences for what is
learned and how long it is retained. With the right con-
texts that set up the learning pillars and that enable
exploration toward a learning goal, we set the stage to
create truly educational materials (Weisberg, Hirsh-Pasek,
Golinkoff, & McCandliss, 2014).
Scaffolded exploration in television. The program
Blue’s Clues initially ran the same episode every day for
a week based on research showing that children inter-
acted more with the program as they mastered the con-
tent (Crawley etal., 2002). This finding echoes a parallel
finding concerning reading books to young children:
Children benefited from repeated readings of the same
book rather than from being read a variety of books that
presented the same information (Horst, Parsons, & Bryan,
2011). Viewing a show multiple times helps children
learn content that they may not have fully understood
during an initial viewing, as well as reinforcing mastery
of content once it has become more familiar (Fisch &
Truglio, 2001). However, once children begin to gain a
greater understanding of a concept, encountering the
same content repeatedly in different contexts may help
them to generalize the content to new situations (Fisch &
Truglio, 2001). For example, a Sesame Street episode
teaching about the letter B may present B in the context
of different words, such as bed, bath, or bird, and in dif-
ferent segments (animated vs. nonanimated, sung vs.
spoken, etc.). In all of these cases, children were given
the opportunity to explore within a context that sup-
ported learning—a context with a learning objective that
was tailored to their understanding. In this sense, the
television findings parallel those noted in guided play.
The same can be said of curricula that is specifically
tied to television programming. PBS’s Ready to Learn, with
its accompanying materials and digital resources, offers
one example of a way in which screen time can be infused
with a learning goal. Neuman’s (2010) work around the
program Super Why! offers another prime example.
Applying scaffolded exploration in apps. In our
analysis, the educational “context” of children’s app
experience includes both the external setting of use and
the internal app design, in terms of how the app guides
children’s experience toward a learning goal. It encom-
passes concepts such as learning design in the field of
human-computer interaction (J. H. Gray, Bulat, Jaynes, &
Cunningham, 2009) and pedagogy in education.
Scaffolding is a pedagogical structure that helps chil-
dren accomplish a task that they would not be able to
accomplish by themselves and that is removed over time,
allowing children to accomplish the same task indepen-
dently (Wood, Bruner, & Ross, 1976). For certain types of
apps, external scaffolding can transform children’s expe-
rience from relatively haphazard poking and swiping to
a guided exploration of age-appropriate content. For
example, visual reference apps such as SoundTouch or
The Human Body provide multimedia representations of
animals, vehicles, instruments, the human circulatory sys-
tem, and other highly engaging content through photo-
graphs, audio, video, and animations. Very young
children exploring these apps alone may have relatively
superficial sensory experiences, whereas, with the guid-
ance of an educationally oriented adult, they could
engage in a genuine inquiry about categories of animals
or processes inside their own bodies.
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Built into the app design itself, scaffolding toward a
learning goal can take various forms, ranging from hint
systems that provide supportive background knowledge,
to curriculum leveling strategies that provide more or less
challenging options during a play session, to sophisti-
cated adaptive learning systems that model relevant
behavior, understanding, and opportunities for each child
in order to prescribe personalized sequences of learning
experiences. Examples in the latter category include apps
from DreamBox Learning and Kidaptive, both of which
adjust content for individual children with the goal of
providing the most engaging and effective learning chal-
lenges at every moment. For example, a child struggling
with the mathematical qualities of a 1-to-10 number line
might be presented with additional conceptual support
or a simpler 1-to-5 number line, depending on his or her
patterns of performance after receiving similar help in
the past.
App Analysis: Categories and
Exemplars
Previously, we demonstrated how the four pillars from
the Science of Learning can be operationalized to identify
whether children are active, engaged, connecting to
meaningful material, and socially interactive when work-
ing with apps. We also argued for the importance of con-
text, such that true educational apps should support
scaffolded exploration toward a learning goal. We sug-
gest that this is a conceptual approach that will benefit
various groups, including app developers—who endeavor
to maximize the learning potential of their creations—
and parents and early childhood educators, who can
assess the learning potential of an app regardless of the
intent of the designers or marketing claims. The extent to
which this approach will benefit these groups depends
on the ease with which it can be put into practice. Below,
we offer a schematic framework with examples of how
one could go about analyzing apps for their educational
potential with respect to each pillar of learning. It is
important to note that this evaluation framework is not
intended as a strict set of guidelines for app analysis.
Rather, it is meant to demonstrate that, regardless of one’s
expertise, it is possible to evaluate apps from a Science
of Learning perspective.
Profiles and pedigrees
Using the four pillars within a context, as suggested by
the Science of Learning, we can begin to analyze apps
with respect to profiles—how they rate on the four pil-
lars—and their pedigree, or whether they are for entertain-
ment, education, or both. In what follows, we first examine
the apps with respect to the profile analysis. Where might
an app fall on each of the four pillars and the context
when examined in the aggregate? We then supplement
this analysis with talk about the apps’ pedigree. In what
cases might a profile signal that the app is truly educa-
tional at its core rather than merely using the label?
Exemplar analysis. We now look at a few exemplars
to illustrate how the four pillars can be used to start an
analysis of children’s app experiences. Alien Assignment1
is an app designed to encourage problem solving and
discovery for young children by engaging them in a scav-
enger hunt with the device’s camera and facilitating par-
ent-child interactions that scaffold learning. It has a
narrative structure involving a family of green aliens who
get marooned on Earth and request help to fix their
spaceship. The aliens ask children to take photos of
household objects (e.g., “light switch”) or categories of
such objects (e.g., “something that turns”) related to their
spaceship repair needs (e.g., “The trunk is stuck. Take a
picture of something you can open”). Children are
prompted to show the photos to a “grown-up,” who can
evaluate the accuracy of each photo with a thumbs-up or
-down and discuss related issues with the child. After all
the child’s photos have been approved, the alien family
blasts off for home, only to have another collision that
brings them back to Earth, which presents the child with
the start menu again.
Active, minds-on learning. How might the app expe-
rience prompt minds-on activity in the service of literacy
learning? Narrative is a virtually universal genre of human
communication and thought (Bruner, 1990) that engages
children’s minds (Nicolopoulou & Ilgaz, 2013). Alien
Assignment connects a series of vocabulary words and
concepts at the center of each photographic quest. The
parallel between plot points (e.g., aliens needing to open
a trunk) and children’s activity (evaluating familiar physi-
cal objects for their potential to help accomplish the task)
prompts children to imagine themselves in the story and
construct a more personal understanding of each word’s
meaning. Additionally, hearing and participating in a
story is likely to bolster children’s understanding of nar-
rative structure, itself an important component of literacy
learning.
Symbolic and sensory activity are notable in this app
experience, which focuses on oral language and visual
exploration of a physical setting. The narrative is told
primarily through recorded voice, with supporting illus-
trations and some incidental on-screen text (e.g., a sign
marked “Earth”). The finished photos provide a prompt
for parent-child conversation. For example, when chil-
dren take a picture of objects that open (e.g., drawers,
doors, and covers), parents can expand the topic to
include new vocabulary (e.g., in, out, pull, push, hinge,
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Putting Education in “Educational” Apps 23
lid) or explore related concepts (e.g., the potentially use-
ful or dangerous fact that objects placed near a door
hinge can be bent or broken, and related physical forces
of leverage and torque). Deciding on the subject of each
photo involves thinking about the nearby physical envi-
ronment, evaluating potential subjects, visually scanning,
and ultimately framing and taking a photograph. This is
likely to be a whole-body experience as children move
through their homes (or another location) in search of
suitable subjects, thinking about what to photograph that
fits the task. A more advanced version of the app could
also address literacy topics such as phonological aware-
ness or word recognition in the context of the story;
however, the current focus on oral language offers age-
appropriate opportunities for learning vocabulary and
story structure.
Engagement in the learning process. The focus and
duration of children’s attention while playing this app
could vary tremendously, depending on the particular
individuals and settings involved. The app affords both
extrinsic and intrinsic motivations, offers explicit feed-
back, and allows for a host of highly responsive, contin-
gent interactions with a parent. Ideally, children would
be motivated by the extrinsic feedback from their parent
and their own intrinsic motivation to creatively explore
their environment. Unfortunately, children may also be
distracted during the first photo quest and not return to
the app, or fail to be engaged by the narrative in the first
place. Realistically, most children’s experiences will likely
fall somewhere between these two extremes.
Perhaps most notable is that Alien Assignment attempts
to create an educational context by prompting engage-
ment with the physical and social setting. Although the
app affords an educational experience, it cannot control
it beyond prompting parents and children to take turns
with the device and providing opportunities to discuss
the photos. Depth of engagement will depend on chil-
dren’s ability to maintain their focus on quests, their par-
ent’s availability and pedagogical skills, and the presence
of potential distractions in the immediate environment.
By moving the user interaction well beyond the screen,
the app affords opportunities for both deep, sustained
learning and powerful distractions that compromise
engagement. Therefore, we conclude that this open con-
textual learning design is both the app’s strength and its
weakness in terms of learning potential.
Meaningful learning. What meaning might the child
get from engagement with an alien species? Ideally,
players would construct significant personal meaning
from their experience with each vocabulary word and
concept as they find, photograph, and discuss objects in
their own home. In the process, this learning would
have interpersonal meaning as a shared parent-child
experience, potentially connected to previous and sub-
sequent family activities. In any case, the learning goals
embodied in this app are an important part of young
children’s foundational knowledge in the domain of lit-
eracy. Although the app addresses only a very small
segment of this foundation, the resulting learning expe-
rience is potentially meaningful to children on multiple
levels.
To illustrate these levels of meaning, consider the
example of “something you can open.” Imagine a young
child photographing a cardboard box that she has been
playing with on her living room floor, focusing on the
four overlapping flaps that open and close on top. She
has a rich sensory understanding of how each one pivots,
but is struggling with how to open all four together to
create an imaginary boat that she can sit in and sail with
her favorite animal toys. This personal meaning of “open”
expands as she looks at the photo with her father and
they discuss the box. Together, they devise a plan for
experimenting with the flaps to see what will work best
to keep them open. This interaction builds a deeper
interpersonal meaning of “open” as a part of problem-
solving and building activities with her dad. The meaning
of “open” further expands when she explores a similar
box with friends at preschool and her teacher documents
their process with a photograph and a caption that says
“opening and closing the boat hatch.” The shared experi-
ence of reading the caption further deepens the meaning
of “open” to include her school community and the wider
domain of literacy.
Social interaction. Literacy learning with Alien Assign-
ment is strongly supported through face-to-face social
interactions between children and parents (or other
adults). Not only does it afford a shared experience of
viewing and discussing the photographs, it structures the
interaction with prompts for sharing the device and
assessing the accuracy of the photographs (with a
thumbs-up or thumbs-down). While the parent-child
interaction is guided toward this sort of assessment, the
specifics of each conversation are up to the individuals
involved. This flexibility allows parents to tailor their
feedback based on their awareness of their child’s knowl-
edge, experience, and interests. Likewise, children can
respond as they wish or remain silent while continuing to
play the game.
Learning may also be supported indirectly through
parasocial relationships with the story characters though
the characters are largely unfamiliar. The alien children
speak directly to the app users as they explain each
repair needed and make a specific request for a photo to
solve the problem. Indeed, the goal of participating in
the story line is to help the family escape Earth and return
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24 Hirsh-Pasek et al.
to their home planet. So, to the extent that the narrative
compels children to photograph real objects and learn
related vocabulary, it is because they accept the pretense
of an alien family. Children help because they care about
the characters.
Scaffolded exploration toward a learning goal. The
learning goal for this activity is clear in that the designers
hoped to have parents and children talking, thereby
increasing vocabulary, language, and preliteracy skills.
Because it relies on parents to achieve the goals, it is a
heavily scaffolded program—assuming that the parents
actually engage. The app itself does not allow for differ-
ent levels of difficulty, other than adjustments to the num-
ber of clues to solve in a play session.
How might these analyses create a profile for Alien
Assignment? We can graph the learning experience
according to the four pillars and the context in which
they are presented and can then create a visual of the
attributes in a figurative profile like the one portrayed in
Figure 1 by using a relative Likert scale to evaluate the
app.
The profile for Alien Assignment would receive the
highest score for providing a cognitively active, meaning-
ful, and socially interactive experience but a slightly
lower score for engagement because of the unknown
qualities of the physical and social setting in which the
app is played. Its literacy goals would give it a high score
for context, but it would fall short of the highest score
because of the same unknown aspects mentioned above.
Note that different apps will have very different pro-
files according to this schematic. For example, Toca Hair
Salon Me, a game that allows children to cut and style
virtual hair, is active, engaging, and meaningful, and it
can be socially interactive, but it does not have an explicit
learning goal. In contrast, an app like Toddler Teasers has
a learning goal but is not very active, engaging, meaning-
ful, or socially interactive and presents a very different
profile than Alien Assignment. Children using Toddler
Teasers merely point to squares and hexagons or to
boxes of different colors and receive stock applause after
correct responses. Figures 2 and 3 show profiles for Toca
Hair Salon Me and Toddler Teasers, respectively.
These profiles provide an at-a-glance way of contrast-
ing the educational potential of the various so-called
“educational apps.” This can be done across a range of
app types.
Pedigrees: App categories
Finally, an alternative way to think about the educational
value of the app is by looking at the qualitative category
that it might fit into. Is it an entertaining or an educational
app? We suggest a 2 × 2 grid that reflects whether an app
has learning goals (yes or no) and whether the relative
summation of the pillar scores yields a high or a low score
(as shown in Fig. 4). Note that we are well aware that this
schematic representation of the profiles and pedigree rep-
resents merely a starting point in what we hope will
Fig. 1. A profile of Alien Assignment showing how the app rates on
each of the four pillars from the Science of Learning and how it rates
on furthering learning goals.
Fig. 2. A profile of Toca Hair Salon Me showing how the app rates on
each of the four pillars from the Science of Learning and how it rates
on furthering learning goals.
Fig. 3. A profile of Toddler Teasers showing how the app rates on
each of the four pillars from the Science of Learning and how it rates
on furthering learning goals.
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Putting Education in “Educational” Apps 25
become a more stringent evaluation. Even at this prelimi-
nary level, however, these caricatures begin to offer some
insights about just how educational an app might be.
Looking at the grid, we find that in the upper right-hand
corner—with high learning goals and high summation of
the pillars (highly active, engaging, meaningful, and
socially interactive)—we meet the true, evidence-based
educational app. An app falling into this quadrant is likely
to result in deep learning, as it is designed in a way that is
concordant with our Science of Learning–inspired pillars.
In the upper left quadrant, we find an app that is low
on learning goals but high in the summation of the pillar
scores—what we might call a pure entertainment app.
This kind of app, like the Toca Hair Salon Me app that we
have described, may be fun to play and might lead to
some ancillary learning, but it is largely noneducational
in flavor (as the CEO of Toca Boca suggested).
Moving to the lower right-hand corner, we find those
apps that are high in learning goals but low in the sum-
mation of the pillar scores. These are “educational” apps
that do not align well with the Science of Learning and
that are not likely to lead to deep learning in the children
who use them. These apps were created when designers
translated existing materials onto tablets without addi-
tional thought as to how to prompt true learning. Many
of these apps have pitfalls we describe below.
Finally, there are apps that fall into the quadrant that
is low both in learning goals and in the summation of the
pillar scores. These apps might be those that are neither
educational nor entertaining. They are not likely to keep
a child’s interest for any length of time and will not likely
result in learning.
The grid presented here allows us to evaluate any app
currently on the market for children. Designers too would
profit from understanding the psychological foundation
that can support the development of educational apps.
Furthermore, if an app focuses on particular content
areas such as reading, math, or spatial development, it is
critical for developers to consult the literature on how to
best frame the content so that it is consistent with scien-
tific evidence.
The first wave of apps, revisited
The first wave of apps have offered an exciting inroad to
a new technology that can be used both for entertainment
value and for education. As developers design new prod-
ucts, and as parents and teachers evaluate these products,
there are also pitfalls that they will want to avoid. Some of
these pitfalls are discussed below.
The fire-alarm syndrome. Imagine you are reading a
traditional book to a child and the fire alarm goes off.
How much of the story do you think the child will recall?
While this is an extreme example, its effects are not far
from those of the bells and whistles included in many
first-wave apps. App developers and parents should be
mindful of the activities within an app: Do the enhance-
ments actually add value and increase engagement, or do
they cause distraction?
The too-many-choices trap. Much like the importance
of content being age-appropriate, it is also crucial for
apps to offer children the appropriate level of decision
making. Adults can be overwhelmed by trying to decide
among 30 varieties of peanut butter in the supermarket
aisle. Similarly, one common pitfall of apps is to over-
whelm children with too many choices. While having a
few choices may help children to stay minds-on by
involving them in the narrative of the app, having too
many choices presents the opportunity for distraction
and lack of engagement increases.
The masquerading “educational” app. Looking at
the bottom right quadrant in comparison to the top right
quadrant, we can now plainly see a difference in what
might be a true educational app and one that simply
bears that name. One of the easiest ways for app devel-
opers to make the claim that an app is educational is to
make sure that it includes “educational” content such as
numbers and letters. Rote memorization of numbers and
letters, however, is not sufficient for deep learning. Young
children need to understand the underlying number prin-
ciples of cardinality, the order-irrelevance principle, and
so on, and the app should touch base with what is known
in the Science of Learning to include ideas from research
on how number knowledge develops (Cross, Woods, &
Schweingruber, 2009).
Empty calories. Some apps are very well designed, with
careful attention paid to maintaining user engagement,
Fig. 4. A grid for determining the pedigree of an app.
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26 Hirsh-Pasek et al.
but little or no emphasis placed on educational content.
Apps designed primarily to entertain may fall into this
category, in the upper left-hand quadrant. For example,
the game Candy Crush has been noted as being very
addictive for players, with short levels, attractive produc-
tion design, and positive reinforcement used to maintain
player engagement (Pentchoukov, 2014). Success in the
game is often (but not always) determined by random
luck rather than mastery of subject knowledge. Even apps
with empty calories can be useful to educational-app
developers in the future if they consider adapting and
integrating features of popular, noneducational apps to
improve the quality of their own apps. Perhaps intermit-
tent reinforcement will raise the level of engagement and
more strongly support learning.
The attention-deficit design. Some apps attempt to
keep children engaged by constantly changing what is
presented. For example, switching from screens designed
to teach shapes to screens designed to teach mathematics
to screens designed to teach early literacy promotes dis-
traction and does not allow the depth of processing
required for solid learning. Science of Learning research
has revealed that children need repetition to learn best.
Encountering the same content repeatedly, and in multi-
ple contexts, reinforces learning, especially for younger
children.
Heralding the Second Wave:
Conclusions
A recent YouTube video put out by the New America
Foundation (2014) begins, “Many parents and teachers
think screen time is something to be avoided. . . . That’s
often the right response. But new research shows that not
all screen interactions are created equal.” The video con-
tinues with examples of when screen time can benefit chil-
dren—when they talk to a grandparent over a chat
program, are exposed to math concepts, work with teach-
ers and parents to learn new things in many domains, and
even learn new words. This is exactly the position we have
taken in this article: Not all screen time is bad. Data indi-
cate that screens are becoming increasingly ubiquitous
(Heggestuen, 2013) around the world. We have argued
that creating screen-based experiences for children that
capitalize on the Science of Learning can only make apps
better and children’s exposure to them more profitable.
We live in the first wave of app development, when
apps are often just migrations of games and learning sce-
narios that already exist in nondigital form. In this “Wild
West” of discovery and change (Guernsey, Levine,
Chiong, & Severns, 2012), app production is largely
unregulated (with the exception of efforts like the
Children’s Online Privacy Protection Act, which is meant
to protect children’s personal information). Research
from the Science of Learning can help guide developers
and inform consumers about best practices for the second
wave of app development.
How can we foster digital experiences that are cogni-
tively active, deeply engaging, meaningful, and socially
interactive within the context of a learning goal? With the
Science of Learning as a foundation, we are in a position
to take a proactive approach to the development and eval-
uation of educational apps. This new framework invites
dialog from the research community, app developers and
evaluators, and teachers and parents. It is a first step in
considering the advantages and drawbacks of this medium.
Apps offer a digital doorway between the physical
world in which children and families live and the rapidly
growing digital cloud. The second wave of educational
apps should be designed and used with this broad poten-
tial in mind, rather than simply mimicking and extending
older media like books, worksheets, television, or even
video games. The design of educational apps should cre-
atively combine principles from the Science of Learning
with the affordances of this versatile medium.
Acknowledgments
K. Hirsh-Pasek and J. M. Zosh contributed equally to this manu-
script and should be considered as joint first authors. We would
like to thank Dr. Stan Lefkowitz and Debby Kaplan for their
continued support. Thanks also to Seeta Pai, Brittany Huber,
Sandra Tate, and Brenna Hassinger-Das, who added inspiration
and editorial help.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Note
1. Note that author Michael B. Robb is Director of Education
and Research at the Fred Rogers Center for Early Learning
and Children’s Media and is an executive producer for this
app. The app exemplar analysis presented here does not
reflect an endorsement but is used solely to illustrate the
way the pillars presented here may be applied to any app.
James H. Gray received financial compensation for writing
an article on imaginative play that was published on the
Toca Boca website in December 2014, after this article was
sent to press, and had no influence on the authors’ choice
to mention Toca Boca apps.
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... Ainsi, certains chercheurs suggèrent d'analyser les théories de l'apprentissage pour comprendre comment elles peuvent tenir compte des contextes d'apprentissage sur supports numériques (Hirsh-Pasek et al., 2015;Legros, Pembroke, & Talbi, 2001;Mayer, 2005;Zosh, Lytle, Golinkoff, & Hirsh-Pasek, 2017). Avant tout, nous devons nous départir de l'approche béhavioriste de l'apprentissage, qui s'intéresse exclusivement au comportement produit en réponse à un stimulus et met de côté le système cognitif qui a permis la production de ce comportement. ...
... Alors qu'on assiste à une prolifération des applications numériques d'aide à l'apprentissage, les enfants se retrouvent au coeur d'une vaste expérience, non planifiée et non contrôlée, d'usage de technologies (Hirsh-Pasek et al., 2015). En effet, le développement des applications numériques est rarement contrôlé, ou même seulement dirigé, par des spécialistes de l'éducation. ...
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