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Visual Environment, Attention Allocation, and Learning in Young Children: When Too Much of a Good Thing May Be Bad


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A large body of evidence supports the importance of focused attention for encoding and task performance. Yet young children with immature regulation of focused attention are often placed in elementary-school classrooms containing many displays that are not relevant to ongoing instruction. We investigated whether such displays can affect children's ability to maintain focused attention during instruction and to learn the lesson content. We placed kindergarten children in a laboratory classroom for six introductory science lessons, and we experimentally manipulated the visual environment in the classroom. Children were more distracted by the visual environment, spent more time off task, and demonstrated smaller learning gains when the walls were highly decorated than when the decorations were removed.
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Psychological Science
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DOI: 10.1177/0956797614533801
published online 21 May 2014Psychological Science
Anna V. Fisher, Karrie E. Godwin and Howard Seltman
Thing May Be Bad
Visual Environment, Attention Allocation, and Learning in Young Children: When Too Much of a Good
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DOI: 10.1177/0956797614533801
Research Article
Many researchers have suggested that focused attention
is crucially important for learning throughout life—from
the crib to the classroom and beyond (e.g., Fisher,
Thiessen, Godwin, Kloos, & Dickerson, 2013; Gaertner,
Spinrad, & Eisenberg, 2008; McKinney, Mason, Perkerson,
& Clifford, 1975; Oakes, Kannass, & Shaddy, 2002; Ruff &
Rothbart, 2001; Yu & Smith, 2012): “If attention were con-
stantly reoriented to every new event, it would be diffi-
cult . . . to learn about any single object or event” (Oakes
et al., 2002, p. 1644). With respect to student achieve-
ment, this idea has been formalized in the time-on-task
hypothesis: All else being equal, the more opportunities
one has to learn (i.e., the longer one focuses on an activ-
ity), the better the learning outcomes (Bloom, 1976;
Carroll, 1963).
In this study, we investigated whether the classroom
visual environment can affect attention allocation and
thereby affect learning in kindergarten children. It is well
documented that distractibility decreases markedly with
age (DeMarie-Dreblow & Miller, 1988; Higgins & Turnure,
1984; Humphrey, 1982; Ruff & Capozzoli, 2003). For
example, the presence of extraneous stimulation during
discrimination or memory tasks has been shown to
impair performance of preschoolers and first and second
graders, but not that of sixth graders (Higgins & Turnure,
1984; Humphrey, 1982). Furthermore, decrements in per-
formance occur whether extraneous stimulation is audi-
tory (e.g., noise in the hallway) or visual (e.g., a large
mirror placed next to a child’s desk; Higgins & Turnure,
Developmental decrease in distractibility is commonly
attributed to developmental improvements in inhibitory
control and working memory. Specifically, maintaining
focused attention requires inhibition of orienting to irrel-
evant objects and events (Colombo & Cheatham, 2006;
Kane & Engle, 2002; Ruff & Rothbart, 2001) and active
maintenance of task goals (Colombo & Cheatham, 2006;
Kane & Engle, 2002). Both of these processes have been
shown to undergo protracted development extending
into late adolescence and early adulthood (for reviews,
see Diamond, 2006; Luna, 2009).
Therefore, maintaining focused attention in classroom
environments that contain extraneous visual displays
533801PSSXXX10.1177/0956797614533801Fisher et al.Visual Environment, Attention Allocation, and Learning
Corresponding Author:
Anna V. Fisher, Carnegie Mellon University, Department of
Psychology, 335-I Baker Hall, 5000 Forbes Ave., Pittsburgh, PA 15213
Visual Environment, Attention Allocation,
and Learning in Young Children: When
Too Much of a Good Thing May Be Bad
Anna V. Fisher1, Karrie E. Godwin1, and Howard Seltman2
1Department of Psychology and 2Department of Statistics, Carnegie Mellon University
A large body of evidence supports the importance of focused attention for encoding and task performance. Yet young
children with immature regulation of focused attention are often placed in elementary-school classrooms containing
many displays that are not relevant to ongoing instruction. We investigated whether such displays can affect children’s
ability to maintain focused attention during instruction and to learn the lesson content. We placed kindergarten
children in a laboratory classroom for six introductory science lessons, and we experimentally manipulated the visual
environment in the classroom. Children were more distracted by the visual environment, spent more time off task, and
demonstrated smaller learning gains when the walls were highly decorated than when the decorations were removed.
attention, learning, cognitive development, self-control, educational psychology, open data
Received 12/4/13; Revision accepted 3/12/14
Psychological Science OnlineFirst, published on May 21, 2014 as doi:10.1177/0956797614533801
at CARNEGIE MELLON UNIV LIBRARY on May 28, 2014pss.sagepub.comDownloaded from
2 Fisher et al.
may be particularly challenging for young children
because visual features in the classroom may tax their
still-developing and fragile ability to actively maintain
task goals and ignore distractions. In fact, the relationship
between student age and typical classroom design is
somewhat paradoxical: Younger learners (e.g., kinder-
garten and elementary school students) are often placed
in learning environments that are rich with potential
sources of distraction (e.g., colorful educational materials
and other visual displays). This problem is compounded
by the fact that kindergarten and elementary school stu-
dents typically receive the majority of instruction in a
single classroom (i.e., they do not have a specialized
classroom for each subject area as is common in middle
and high school). Therefore, students are exposed to
large amounts of visual materials that are not relevant for
the ongoing instruction. Consider this description of a
typical elementary school classroom:
One spring day, as I observed a student teach a
science lesson to a group of 25 first-graders, my
gaze wandered around the room. From a small
chair in a corner, I counted 19 different, decorated,
scalloped borders segmenting portions of the
bulletin boards lining the walls. The boards were
filled with words: a word wall, class rules, calendar,
alphabets, numbers, shapes, and colors, and a
plethora of cartoon people and animals . . . . St.
Patrick’s Day mobiles created from brightly painted
rainbows and black-line masters hung from the
ceiling just above the children’s heads . . . . (Tarr,
2004, p. 1)
Such visual environments have been described as
“visual bombardment” (Bullard, 2010, p. 110) and a
“cacophony of imagery” (Tarr, 2004, p. 1).1 Some educa-
tors warn against excessive use of sensory stimulation in
primary-grade classrooms (e.g., Tarr, 2004; Thompson &
Raisor, 2013). Montessori (1949) encouraged teachers of
young children to have sparsely decorated classrooms, a
practice that continues in Montessori classrooms today.
However, no studies have yet directly examined the pos-
sible effects of the classroom visual environment on
young children’s attention allocation and learning out-
comes. Our goal in the present study was to address the
following research questions. First, do irrelevant visual
displays affect attention allocation in kindergarten stu-
dents? Second, do irrelevant visual displays affect kinder-
garteners’ learning? On the basis of literature suggesting
protracted maturation of the ability to inhibit distracters
and maintain task goals (e.g., Diamond, 2006; Luna, 2009),
we predicted that visual displays not relevant to the ongo-
ing instruction may decrease focused attention and
thereby decrease learning gains in kindergarten students.
The research reported here was conducted in a labo-
ratory adjacent to a university laboratory school.
Kindergarten children were brought into the laboratory
classroom for six lessons. Instruction consisted of a short
period in which the teacher read aloud from a book
(hereafter called a read-aloud), which is a common
instructional activity in kindergarten. Children received
three lessons in the decorated-classroom condition and
three lessons in the sparse-classroom condition.
Immediately after each lesson, paper-and-pencil assess-
ments were administered to measure learning of the les-
son content.
All lessons were videotaped for coding episodes in
which children became distracted (off-task behavior) and
episodes in which they were engaged with the teacher or
learning materials (i.e., the book; on-task behavior).
Engagement was determined by the direction of gaze.
Eye gaze is a common measure of visual attention (for
reviews, see Henderson & Ferreira, 2004; Just & Carpenter,
1976) and has also been used as a measure of auditory
attention (e.g., Reisberg, 1978; Saffran, Aslin, & Newport,
1996). Although it is possible that students listen to the
teacher while looking elsewhere, doing so by definition
constitutes divided attention rather than focused atten-
tion. Therefore, in instructional contexts that involve
visual materials, direction of eye gaze is a reasonable
(albeit imperfect) measure of focused attention and on-
task behavior.
Participants were 24 kindergarten students (12 girls, 12
boys; mean age = 5.37 years). To prevent overcrowding
in the laboratory classroom, we used stratified random
assignment to create two groups matched on age and
gender (Group 1: n = 12; 6 girls, 6 boys; mean age = 5.37
years; Group 2: n = 12; 6 girls, 6 boys; mean age = 5.39
years). One child was absent during four of the six les-
sons; consequently, this child’s data were excluded from
our analyses.
All participants were recruited from the same kinder-
garten classroom in a laboratory school on the campus of
a private university in a Midwestern city in the United
States. Participants were predominantly White (74%
White, 26% minority) and predominantly from house-
holds with high socioeconomic status.
The study took place in a research laboratory modified to
look like a classroom. In the decorated-classroom condi-
tion, the laboratory classroom was furnished with
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Visual Environment, Attention Allocation, and Learning 3
potential sources of visual distraction commonly found in
primary classrooms (e.g., science posters, maps, the chil-
dren’s own artwork provided by their teacher; Fig. 1a).
All commercial materials were purchased from educa-
tional supply stores. In the sparse-classroom condition,
all materials irrelevant to ongoing instruction were
removed (Fig. 1b). The classroom visual environment
was a within-subjects factor. Dependent measures were
the amount of time children spent off task and the learn-
ing outcomes as determined by the paper-and-pencil
assessments. The order of testing in the sparse and deco-
rated classrooms was alternated to mitigate temporal
confounds: The first lesson was in the sparse-classroom
condition, and the order of conditions was alternated
Both groups participated in five familiarization sessions.
We used the familiarization sessions (a) to acquaint par-
ticipants with the teacher (i.e., research assistant) and
assessment procedures (i.e., paper-and-pencil tests) and
(b) to administer pretest assessments. During the famil-
iarization sessions, the laboratory classroom contained a
moderate amount of irrelevant visual materials. After the
familiarization sessions, children participated in six
experimental sessions (i.e., the six science lessons).
Lessons. Children participated in six lessons over a
2-week period. Lessons consisted of 5- to 7-min
read-alouds. Lesson topics were plate tectonics, stone
tools, volcanoes, solar system, bugs, and flight (see Fig.
2a for sample lesson content). The children’s kindergar-
ten teacher was interviewed before the study to ensure
that participants had not received formal instruction on
any of these topics during the current school year. The
lesson topics were selected to align with the major
domains covered in elementary science education and
the academic standards for elementary science education
specified by the Pennsylvania Department of Education
(for details about the lessons and books, see Lesson Con-
tent and Creation of Custom-Made Books in the Supple-
mental Material available online).
During the lessons, the children sat on carpet squares
in a semicircle facing the teacher. The seating arrange-
ment was randomly assigned at the beginning of the
study and remained constant throughout (i.e., the seating
arrangement was similar to the stable seating arrange-
ment participants experienced in their own kindergarten
classroom). All lessons were conducted by a female
researcher who was blind to the hypotheses and who
had prior experience with early childhood education. As
is typical for read-alouds with children of this age, the
researcher sat on the floor facing the children and read
from a book. After reading each two-page spread, she
showed the children the illustrations in the book by mov-
ing the book slowly across the semicircle. A book was
created for each of the six lesson topics. The researcher
was instructed to conduct the lessons the way she typi-
cally would for children of this age.
Fig. 1. Panoramic view of the laboratory classroom in (a) the decorated-classroom condition and (b) the sparse-classroom condition.
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4 Fisher et al.
Assessments. Pretest assessments were administered dur-
ing the five familiarization sessions. Children were given an
assessment workbook, and the teacher read each question
aloud and asked the children to select the correct answer
from four pictorial response options. Seventy questions
were administered during the pretests (13 to 15 questions
during each of the five familiarization sessions). The pre-
sentation order of the pretest questions was randomized.
Thirty-six pretest questions (six per science lesson)
were selected for the posttest assessments. Item selection
was based on pretest accuracy: For each lesson, the six
items with the lowest rate of accuracy on the pretest
were included in the posttest. The mean pretest accuracy
for the selected questions was 22.7%, not different from
chance, one-sample t(22) = 1.10, n.s.
To equalize pretest performance between the sparse-
classroom condition and the decorated-classroom
condition, we divided the six science lessons into two
groups on the basis of pretest accuracy (i.e., higher vs.
lower pretest accuracy). We then pseudo-randomly
assigned lessons from the two groups into the two
experimental conditions, with the restriction that a single
condition could not contain all lessons with higher or
lower pretest accuracy scores. Using this procedure, the
following three lessons were assigned to the sparse-
classroom condition: plate tectonics, volcanoes, and
bugs. The remaining three lessons—stone tools, solar
system, and flight—were assigned to the decorated-
classroom condition.
A posttest assessment was administered at the end of
each lesson. Each assessment workbook included six
questions pertaining to the completed lesson. The proce-
dure for administering the posttest assessments was iden-
tical to that used in the pretests. Specifically, the children
were asked to select the correct answer from four picto-
rial response options (one correct answer and three
lures). All response options were novel (i.e., four pictures
the children had not seen during the lesson) to ensure
that the children were not merely selecting a familiar
answer (see Fig. 2b for a sample assessment item).
Coding. Four coders who were blind to the hypotheses
were trained by the second author using vignettes, video-
tapes, and live observations. An event-sampling strategy
was used to code children’s behavior during the lessons:
Coders first classified behavior as on or off task on the
basis of the direction of the children’s eye gaze. On-task
behavior was defined as looking at the teacher or the
instructional materials. If the child was classified as exhib-
iting off-task behavior, the distraction source was identi-
fied using a coding scheme developed in pilot
observations in a kindergarten classroom. Distractions
were categorized as follows: self-distraction (i.e., engage-
ment with one’s own body or clothing), peer distraction
(i.e., engagement with another child), environmental dis-
traction (i.e., engagement with classroom materials irrel-
evant for ongoing instruction), or other (i.e., off-task
behaviors that did not clearly align with any other cate-
gory). If the child was engaged in simultaneous off-task
behaviors, the primary distraction source was determined
by direction of eye gaze.
The coders marked the onset and cessation of each
off-task behavior to determine the frequency and dura-
tion of off-task behaviors. To estimate interrater reliabil-
ity, we calculated Cohen’s kappa (Cohen, 1960) for a
subset (20%) of observations. Kappa was .74, a level of
reliability that was in line with past observations of class-
room off-task behavior and that approached the .75
threshold considered excellent in the field of classroom
observations (Fleiss, 1981).
Fig. 2. Sample (a) content and (b) assessment question from the bug
lesson. The story content was obtained in part from Rockwell and
Jenkins (2001). All text was presented verbally by the teacher.
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Visual Environment, Attention Allocation, and Learning 5
Effect of classroom type on time
spent off task
In the two experimental conditions combined, children
were on task for 66.5% of instructional time. This per-
centage of time on task is in line with that reported in
previous research (i.e., children were on task for between
57% and 88% of instructional time; Karweit & Slavin,
1981). Therefore, in the present experiment, the teacher
successfully engaged the children in the learning activity.
At the same time, the rate of off-task behavior varied as a
function of condition. The overall percentage of instruc-
tional time spent off task was significantly greater when
children were in the decorated classroom (M = 38.58%,
SD = 10.49) than when they were in the sparse classroom
(M = 28.42%, SD = 13.19), paired-sample t(22) = 4.90, p <
.0001; this effect was large, Cohen’s d = 0.85.
To examine the possibility that the effects reported
here were driven by a minority of participants who were
particularly sensitive to visual displays, we calculated a
difference score for each child by subtracting the per-
centage of time spent attending to the environment in the
decorated classroom from the percentage of time spent
attending to the environment in the sparse classroom.
Difference scores ranged from 2% to 36%, with a mean of
17% (SD = 9). Few children (n = 3, 13% of the sample)
had difference scores below 10% and thus presumably
were minimally affected by the visual displays in the dec-
orated classroom. However, most children (n = 20, 87%
of the sample) had difference scores that exceeded 10%,
which is contrary to the possibility that the observed
effects were driven by a minority of participants.
Next, we examined whether the duration of time allo-
cated to each distraction subtype differed as a function of
condition. In the sparse-classroom condition, the chil-
dren spent only 3.21% of instructional time engaged in
environmental distractions, whereas in the decorated-
classroom condition, they spent 20.56% of instructional
time engaged in environmental distractions; this differ-
ence was significant, paired-sample t(22) = 8.78, p <
.0001, and the effect was large, Cohen’s d = −2.60. In
contrast, the children spent significantly more time
engaging in self-distraction and peer distraction in the
sparse classroom than they did in the decorated class-
room, both paired-sample ts(22) > 2.75, ps < .012 (Fig. 3).
In both conditions, the children spent less than 1% of
instructional time engaged in other distractions.
Effect of classroom type on learning
Pretest accuracy was statistically equivalent in the sparse-
classroom condition (M = 22%) and the decorated-class-
room condition (M = 23%), paired-samples t(22) < 1, and
accuracy in both conditions was not different from
chance, both one-sample ts (22) < 1.3, ps > .21. The chil-
dren’s posttest scores were significantly higher than their
pretest scores in both experimental conditions, both
paired-samples ts(22) > 4.72, ps .0001 (Fig. 4). Therefore,
in both experimental conditions, the children success-
fully learned from the instruction. However, their learn-
ing scores were higher in the sparse-classroom condition
(M = 55%) than in the decorated-classroom condition
(M = 42%), paired-samples t(22) = 2.95, p = .007; this
effect was of medium size, Cohen’s d = 0.65.
Analysis of gain scores corroborated the results of the
analysis of the posttest scores. Gain scores were calcu-
lated by subtracting each participant’s pretest score from
his or her posttest score. Pairwise comparisons indicated
that the children’s learning gains were higher in the
sparse-classroom condition (M = 33%, SD = 22) than in
the decorated-classroom condition (M = 18%, SD = 19),
paired-sample t(22) = 3.49, p = .002, Cohen’s d = 0.73.
The relation between time spent off
task and learning
First, we examined the relation between time spent off
task and learning outcomes. For this analysis, we
Percentage of Time
Fig. 3. Percentage of time spent off task as a function of condition and
distraction subtype. Error bars represent ±1 SEM.
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6 Fisher et al.
averaged each child’s time off task and posttest scores
across the decorated-classroom and sparse-classroom
conditions to derive measures of total time off task and
total learning. These scores were negatively correlated:
Children who spent more time off task tended to have
lower learning scores, r = −.500, p = .015 (Fig. 5).
Next, we conducted a mediation analysis to examine
the relations among classroom environment, time spent
off task, and learning outcomes. This analysis indicated
that time spent off task significantly mediated the relation
between classroom condition (decorated vs. sparse) and
learning scores, p = .011 (see Details of the Mediation
Analysis in the Supplemental Material). This finding sug-
gests that the observed effects stem from the following
pathway: Classroom type affected the children’s attention
allocation (they spent more time off task when the class-
room was highly decorated than when it was not
decorated), and time off task reduced learning of the les-
son content.
The present study yielded several novel findings. First, the
pattern of focused attention in kindergarten children
changed as a function of the classroom visual environ-
ment. When placed in a decorated classroom, the children
were more likely to be distracted by the visual environ-
ment, and when placed in a sparse classroom, they were
more likely to be distracted by themselves or by peers.
Second, the classroom visual environment affected the
overall amount of time the children spent off task: They
spent significantly more instructional time off task in the
decorated-classroom condition than in the sparse-classroom
condition. More than 85% of the children in the sample
showed this effect. Third, although the children learned
from the instruction in both conditions, learning scores were
higher in the sparse-classroom condition than in the deco-
rated-classroom condition. Fourth, there was a negative
relationship between the total amount of time the children
spent off task and learning: The more time a given child
spent off task, the less that child learned. Finally, time off
task mediated the relationship between classroom type
and learning: The decorated classroom led to greater time
off task than the sparse classroom, and greater time off
task in turn led to reduced learning.
Although previous research has established a relation-
ship between focused attention and task performance
(e.g., Craik, Govoni, Naveh-Benjamin, & Anderson, 1996;
Kannass & Colombo, 2007), our study is the first (to our
knowledge) to experimentally induce lower or higher
Sparse-Classroom Condition Decorated-Classroom Condition
Percentage Correct
Pretest Posttest
Fig. 4. Percentage of correct answers on the pretest and the posttest as a function of experimental
condition. Error bars represent ±1 SEM. Asterisks indicate significant differences (p < .01).
0% 20% 40%60% 80%
Total Time Off Task
Total Learning Score
Fig. 5. Scatter plot showing total time off task as a function of total
learning score.
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Visual Environment, Attention Allocation, and Learning 7
levels of off-task behavior in the same group of children
and to reveal corresponding changes in learning out-
comes in a naturalistic setting. Therefore, these findings
provide support to the time-on-task theory of learning in
academic settings (Carroll, 1963). Although a large num-
ber of correlational studies have provided evidence for
this theory (e.g., Frederick & Walberg, 1980; Roberge,
Rojas, & Baker, 2012), the present findings provide the
first supporting experimental evidence.
Results from the present study suggest that the visual
environment plays a role in how young children allocate
their attention during instruction. Susceptibility to envi-
ronmental distractors is probably affected by individual
characteristics of the learner. For example, children with
less-developed inhibitory control may be particularly sus-
ceptible to environmental distractors. Further research is
necessary to explore the relations among individual dif-
ferences, time on task, and learning. We are not advocat-
ing sterilizing the learning environments of young
children by removing all decorations, artwork, or educa-
tional displays. The reported results serve as a proof of
concept that the classroom visual environment can
induce changes in attention allocation and learning out-
comes in kindergarten children. However, further
research is needed to examine the optimal level of visual
stimulation in primary-grade classrooms to develop evi-
dence-based guidelines for classroom design.
In contemplating the possible practical implications of
the reported findings, it is important to consider several
issues. First, how robust are the reported effects? This
article builds on our prior work, which yielded similar
findings (Godwin & Fisher, 2011). In both studies, we
observed an increase in total time off task in the deco-
rated classroom compared with the sparse classroom and
higher learning scores in the sparse classroom compared
with the decorated classroom. These similar results were
obtained despite methodological differences between the
two studies. For instance, in the present study, lessons
were assigned to conditions on the basis of pretest accu-
racy rates. In contrast, in our prior study, the same les-
sons were completed in both classroom environments by
different groups of children (e.g., half of the children
completed the stone-tools lesson in the decorated-class-
room condition and the remaining children completed
the stone-tools lesson in the sparse-classroom condition).
Therefore, the current findings are sufficiently robust to
have been observed across two separate studies that var-
ied whether a lesson was completed by all children in the
same type of classroom (the present study) or was com-
pleted in both classroom types (sparse and decorated)
but by different groups of children.
Second, in the present study, we alternated the deco-
rated- and sparse-classroom conditions to mitigate tem-
poral confounds. However, this design may have led the
children to be surprised at the frequent changes in the
classroom visual environment, thus amplifying the pos-
sible detrimental effect of the decorated-classroom con-
dition on attention allocation and learning. A related
concern is that in real classrooms, students are exposed
to the same visual environment every day and may there-
fore habituate to the visual environment. In a recent
study, we videotaped groups of kindergarten children
who received instruction in a decorated classroom every
day for 2 weeks (Godwin & Fisher, 2012). Before receiv-
ing instruction in the decorated classroom, the children
participated in a weeklong series of lessons in a sparse
classroom, which served as a baseline. In the sparse
classroom, the children spent 29% of instructional time
off task. Off-task behavior in the decorated classroom
was significantly greater in both Week 1 (48% of time
spent off task) and Week 2 (46% of time spent off task).
It is possible that greater habituation might occur with
longer exposure. Nevertheless, these findings indicate
that young children often find visual displays distracting,
even after multiple consecutive exposures.
Finally, it is unclear whether our findings can be gen-
eralized to children in grades beyond kindergarten. There
is some indirect evidence that they can. Barrett, Zhang,
Moffat, and Kobbacy (2013) examined the relationship
between several environmental factors and student
achievement in primary school students in the United
Kingdom; most students in the study were between the
ages of 3 and 11 years. The researchers found that a num-
ber of school- and classroom-level factors were related to
children’s achievement scores, even when controlling for
the contribution of socioeconomic status. For example,
achievement scores were positively associated with the
amount of natural light and air quality in classrooms. The
authors initially hypothesized that greater amounts of
color in a classroom should correspond to better achieve-
ment, which would be consistent with the pervasive belief
that primary classrooms need to provide sensory stimula-
tion. In contrast, Barrett et al. found that classroom color
ratings were negatively related to achievement scores.
The present findings provide a theoretical framework for
interpreting this surprising outcome. Specifically, colorful
visual displays may promote off-task behavior in young
children, resulting in reduced learning opportunities and
achievement. It remains to be assessed experimentally
whether our current findings generalize to older children.
Nevertheless, our findings and those reported by Barrett
et al. suggest that the classroom visual environment can
be optimized to promote on-task behavior and to improve
learning outcomes in elementary school students.
Author Contributions
A. V. Fisher was responsible for the conceptualization of the
study and contributed to study design and data analyses. K. E.
Godwin contributed to the conceptualization and design of the
study and to data analyses, and she created the study materials
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8 Fisher et al.
and trained the coders. H. Seltman conducted the mediation
analysis. All authors contributed to the writing of the
We thank Malika Sinha, Megan Petroccia, Jessica Meeks, Laura
Pacilio, Amy Barrett, Helen Kim, and Jae-Won Kim for their
help collecting and coding video data. We thank Peter Scupelli
and Kevin Kan for taking the photographs presented in Figure
1. We thank Paulo Carvalho for his feedback on the reported
study. We thank the children, parents, teachers, and administra-
tors of the Children’s School at Carnegie Mellon University who
made this project possible.
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.
This work was supported by Grant R305A110444 from the
Department of Education, Institute of Education Sciences, and
by Graduate Training Grant R305B090023, awarded to Carnegie
Mellon University by the Department of Education. The opin-
ions expressed are those of the authors and do not represent
views of the Institute of Education Sciences or the Department
of Education.
Supplemental Material
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... Several recent studies show that aspects of the visual environment may affect selective sustained attention, learning gains, and task performance in children (Barrett, Davies, Zhang, & Barrett, 2015;Barrett, Zhang, Moffat, & Kobbacy, 2013;Fisher et al., 2014;Hanley et al., 2017;Rodrigues & Pandeirada, 2018;Stern-Ellran, Zilcha-Mano, Sebba, & Binnun, 2016). For example, Barrett and colleagues (Barrett et al., 2013;Barrett et al., 2015) examined the relationship between environmental design and student achievement. ...
... Several laboratory studies point to the mechanism by which the classroom visual environment may impact academic outcomes. A study by Fisher et al. (2014) demonstrated that visual features of the classroom environment (e.g., charts, posters, and displays) are potential sources of distraction that promote off-task behavior, hindering young children's ability to attend to instructional activities and thus reducing learning gains. In this study, kindergarten children were brought into a laboratory classroom, where they were taught six science lessons over the course of a 2-week period. ...
... Hanley et al. found that both typically developing children and children with autism showed reduced looking at the teacher delivering the lesson in the presence of classroom displays, with a greater effect of visual displays on children with autism. Consistent with the Fisher et al. (2014) findings, learning scores were higher for the lessons delivered in the absence of classroom displays than for lessons delivered in front of the displays. Additionally, time spent looking at the displays was the strongest predictor of children's learning scores. ...
Prior research suggests that visual features of the classroom environment (e.g., charts and posters) are potential sources of distraction hindering children's ability to maintain attention to instructional activities and reducing learning gains in a laboratory classroom. However, prior research only examined short‐term exposure to elements of classroom décor, and it remains unknown whether children habituate to the visual environment with repeated exposure. In study 1, we explored experimentally the possibility that children may habituate to the visual environment if the visual displays are static. We measured kindergarten children's patterns of attention allocation in a decorated classroom environment over a 2‐week period and compared the percentage of time children spent off‐task to a baseline condition in which the classroom environment was streamlined (i.e., charts, posters, and manipulatives were removed). The findings indicate that with more prolonged exposure to a static visual environment, partial habitation effects were observed: Attention to the environment declined at the end of the exposure period compared to the beginning of the study; however, the environment remained a significant source of off‐task behavior even after 2 weeks of exposure. In study 2, we extend this work by conducting a longitudinal observation of six primary classrooms in which we measured children's patterns of attention allocation in real classrooms for 15 weeks to investigate whether increasing familiarity with the classroom décor would influence attention toward the visual environment. No evidence of habituation was observed in genuine classrooms in study 2. Potential implications for classroom design and future directions are discussed.
... Dividing one's attention between the ongoing instruction and the visual environment may be particularly problematic for learning given the negative and potentially cascading effects of inattention for learning. Indeed, prior research has found that attention regulation is generally related to task performance, learning outcomes, and achievement in the laboratory and in classroom settings (e.g., Fisher, Godwin, & Seltman, 2014;Hanley et al., 2017;Karweit & Slavin, 1981;Merrell & Tymms, 2001;Razza, Martin, & Brooks-Gunn, 2012; see Godwin et al., 2021 for discussion). ...
... Children's gaze is used as a common measure of sustained attention and lapses in attention (commonly referred to as on/off-task behavior in classroom settings). In prior studies, attention has been recorded via eye-tracking technology (e.g., Hanley et al., 2017), classroom observations (e.g., Karweit & Slavin, 1981), and video recordings (e.g., Fisher et al., 2014). These measurement approaches each have strengths as well as trade-offs. ...
... Decisions regarding classroom design and decoration are likely influenced by a number of factors including (but not limited to) pedagogical beliefs, school culture, as well as social media. However, there are limited data that teachers can leverage to guide their design choices (e.g., Barrett et al., 2013;Fisher et al., 2014;Hanley et al., 2017). These results extend our understanding of how the design of classroom visual environments predicts students' attention allocation and fills a gap in the literature which has focused largely on how ambient features of the classroom predict student outcomes. ...
Prior laboratory research suggests the visual environment can be a source of distraction for children, reducing attention to instructional tasks and learning outcomes. However, systematic research examining how the visual environment relates to attention in genuine classrooms is rare. In addition, it is unknown what specific aspects of the environment pose a challenge for attention regulation. This observational study aims to (1) provide a nuanced examination of specific elements of the classroom visual environment (e.g., visual noise, display quantity, color variability) by analyzing panoramic classroom photographs (N = 58) and (2) investigate whether specific visual environment elements are related to children's rates of on‐task behavior. Results indicate on‐task behavior was lower in classrooms containing greater quantities of visual noise and color variability, and in classrooms with either relatively small or large amounts of displays (controlling for observation session, school type, student gender, grade‐level, and instructional format). Implications for creating more optimal visual learning environments are discussed. Laboratory studies suggest that highly decorated environments reduce attention to instructional activities and learning outcomes. It is unknown whether these findings extend to genuine classrooms. This observational study investigated whether specific aspects of the visual environment are related to rates of on‐task behavior in 58 elementary school classrooms in the United States. On‐task behavior was lower in classrooms containing greater visual noise and color variability and in classrooms with relatively small or large amounts of displays.
... In the last few years, educational research has begun to highlight the importance of visual crowding during learning. Although not specifically mentioning clutter, Fisher, Godwin, and Seltman (2014) claimed to be the first study that directly examined whether the visual environment of a classroom affected children's attention allocation. Children, with a mean age of 5 years, completed lessons where the classroom interchanged between sparsely decorated walls (no clutter) and heavily decorated visual displays (high clutter). ...
... For example, in Hanley et al. (2017), a broader age range of English children (5-13 years old) participated in an experiment where they watched video lessons with a teacher that presented lesson content amongst a blank background (no clutter), whereas other lessons featured a background that contained a high amount of visual display (high clutter). Using eye-tracking measures and questioning the children about the content of the lessons, the findings replicated Fisher et al. (2014) and identified that the presence of visual clutter was associated with children attending to the background more, while retaining less information (Hanley et al., 2017). Similarly, in research using a more qualitative methodology, interviews with teachers and paraeducators highlighted that decluttering classrooms made a positive effect on students' learning experiences and behaviour (McDowell & Budd, 2018). ...
... Overall, clutter increases response times and the number of errors made in search tasks (Moacdieh & Sarter, 2015); clutter in educational environments distracts learners, reduces understanding (Fisher et al., 2014) and results in children retaining less information (Hanley et al., 2017); and clutter of individual letters impairs reading (van den Boer & Hakvoort, 2015). However, little is known for whether the detrimental effects of clutter extend onto an individual's perception when reading and learning words that are surrounded by other words. ...
... When the studies on this topic are examined (Ayre, 2017;Bosworth, Ford, & Hernandez, 2011;Brkovic, Pons, & Parnell, 2015;Cole, 2014;Cutter-Mackenzie, 2009;Darmody, Smyth, & Doherty, 2010;EDUSPACES21, 2016;Fisher, 2010;Fisher, Godwin, & Seltman, 2014;Flores, 2008;Foster et al., 2006;Giraldo-Henao, 2017;Göçen, Eral & Bücük, 2020;Hanovar Research, 2011;Higgins et al, 2005;Leiringer, & Cardellino, 2011;Luna-Scott, 2015;Milo-Shussman, 2017;Nair & Fielding, 2013;O'Donnell, 2012;Rigolon & Alloway, 2011;Sigurðardottir & Hjartarson, 2011;Sutherland & Fischer, 2014), the design of the physical learning environment was considered in the context of the pedagogical foundations that guide 21st century primary schools were subsumed under two main categories of 'child-friendly design' and 'design for learning'. The design principles for the physical learning environment under these categories are adressed. ...
... For example, the classroom doors and cabinets can be designed to include different shapes or patterns to teach geometry in mathematics (Sigurðardottir & Hjartarson, 2011). However, it should be considered that using more visual stimuli in classrooms may not be associated with more learning, but rather distracting (Fisher, Godwin, & Seltman, 2014;Milo-Shussman, 2017). Another approach that takes classrooms out of their traditional use is themed learning areas. ...
... In current VR environments, the user is fully immersed in the environment he or she is learning in (Salzman et al., 1995;Fried, 2008;Winter et al., 2010;Fisher et al., 2014). Although the isolation from external disturbing cues seems to be an obvious advantage of highly-immersive VR systems, very limited evidence of empirical research is available addressing this question by comparing high and low immersive technology while varying disturbance potential of the learning environment. ...
... VR allows for the simulation of real-life scenarios and create immersive and captivating experiences. This is why this technical environment has often been considered to be the future of teaching and training (Abulrub et al., 2011;Fisher et al., 2014). While there seems to be evidence for an increased use of VR-technology in teaching and education (e.g., Lessiter et al., 2001;Carrozzino and Bergamasco, 2010;Radianti et al., 2020) it is still not clear how the use of VR is related to learning performance and to what extent presence as underlying mechanism plays a role in this relation. ...
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The highly immersive Virtual reality (VR) headset is gaining popularity in multiple application domains. In the context of learning, it has been proposed to be beneficial by increasing presence and attention in noisy and distracting environments, both factors that are considered important for learning. Despite intensified research efforts in recent years, empirical knowledge of experimental research addressing the link between presence and learning in specific environmental contexts is still rather scarce. In this study following an experimental mixed-method approach, the link between presence and memorization as a particular form of learning is addressed by comparing memorization with a highly immersive VR headset to a less immersive system (desktop screen) in noisy and calm learning environments. Using a 2 (learning location) x 2 (learning device) between-subjects design, 63 participants interacted with one of the two devices in either of the two environments. As expected, VR headset users reported higher presence levels. While participants subjectively evaluated the VR headset as a better device for learning, the memorization test scores were higher for desktop screen users in both calm and noisy environments. Learning location did not show significant effects. Attention distraction and context-dependent learning are discussed with regard to the unexpected results, while implications for practice and future research are discussed.
... First, thought experiments typically use idealized scenarios where the attention of the learner is pointed to the relevant variables that need interpreting. Conversely, real experiments may sometimes involve manipulatives and materials that may distract children's attention from the actual problem (Fisher, Godwin, & Seltman, 2014). Another reason why thought experiments may be more effective over the long run is because they may instill a habit of thought in the learner, which is to check one's beliefs continuously and persistently against one's vast database of information via thought experimentation. ...
Conference Paper
Full-text available
As the history of science has documented, there is an important role for thought experiments in scientific progress. Yet, there is very little empirical research about whether and how children learn from thought experiments. Here, we asked that question in the context of 6-year-olds' developing theory of matter. At the outset of the study, over half of the children claimed that small pieces of matter weigh nothing at all. Children were randomly assigned to a Real (RE) and a Thought Experiment (TE) condition. The goal of each condition was to show-via demonstration in the RE and via mental simulation in the TE-that the weight of a single grain of rice can cause a card resting on a fulcrum to topple. We found that children simulated accurately in the TE, and they changed their judgments and justifications concerning the weight of small pieces equally from the TE and RE.
... Such features provide building occupants with the awareness of their surroundings, and connectivity to the external environment (Butler and Biner 1989). However, excessive dynamism defined by scale, frequency, or periodicity may negatively impact the occupants such as students in the classrooms (Fisher et al. 2014). Thus, window views that provide brief moments of interest, not overly distracting, may be good (Baird et al. 2012). ...
... In the classroom, students tend to focus on the educator when listening, and shift the focus to looking away when thinking (Riby et al., 2012), and if the visual environment is chaotic, this can disrupt thinking (Hanley et al., 2017). A study conducted by Fisher et al. (2014) found that students were approximately 17% more likely to engage with the environment rather than the teacher when the environment was highly decorated. Chaotic visual room environments are less likely to be an issue in the postsecondary environment than they are in school environments, however, the presence of multiple personal devices operating simultaneously can be extremely distracting in the tertiary classroom or lecture theatre. ...
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People vary in the way in which they perceive, process and react to environmental factors, and some are more or less sensitive than others. There is a dearth of research investigating the possible impact that environmental sensitivity has in the postsecondary education context. To address this gap in literature, the following research question was posed: What impact does environmental sensitivity have on student learning in tertiary education? To answer this question a two-stage mixed methods research project was undertaken. The first stage involved two studies which used snowball recruitment via social media, and subject inclusion criteria were current or previous postsecondary education experience. Participants completed on-line surveys. Study One is the design, development and validation of a self-report instrument measuring postsecondary students’ perceptions of their learning success, and participants completed the Perceived Success in Study Survey (PSISS) and associated demographic questions. Two phases were undertaken to check for reliability of results, n=225 and n=237. Reliability statistics found a high level of internal consistency, and principal component analysis identified five factors: Intellectual Stimulation, Generic Skills, Work-life Balance; Commitment to Learning and Learning Community. The PSISS was found to be a comprehensive measure of overall success for postsecondary learners. The participants in Study Two (n=365) completed the PSISS and the 12-item Highly Sensitive Person Scale (HSPS-12, Pluess et al., 2020) and related demographic questions. Independent T-tests, ANOVA and Tukey post-hoc calculations identified that high sensitivity is positively associated with success-promoting attitudes and strategies as identified on three of the five PSISS factors. It also found positive associations between total scores on the PSISS and the sensitivity subscales of Aesthetic Sensitivity and Ease of Excitation (Smolewska et al., 2006). This study included a response field to register interest in participation in further research. Those who responded, and who rated as highly sensitive on the HSPS-12, were invited to take part in a semi-structured interview, leading into the second stage of the project. Thirteen Zoom interviews were conducted with participants from a broad range of geographic locations and levels and fields of study in order to exemplify and elaborate on the quantitative findings. Reflexive inductive thematic analysis was employed to analyse the data, and sixteen codes and three themes were identified. Responses were written vi into a semantic narrative, accompanied by pertinent participant quotations, providing a rich and detailed description of participant experience. The results of this study confirmed that there are educational advantages contingent with high sensitivity, including the use of a broad array of metacognitive study and self-care strategies, and the prioritisation of wellbeing and work-life balance. Conversely, it also found that numerous simultaneous study demands can lead to feelings of overwhelm, however, the participants employed a comprehensive array of metacognitive coping strategies to manage these. Low sensory thresholds associated with high sensitivity can present challenges for highly sensitive students who can be negatively impacted by aspects of the physical learning environments including light, noise, indoor environmental pollutants. Additionally, participants highlighted the need for postsecondary institutions to provide education about environmental sensitivity, to allow flexibility in teaching delivery, to explore options to support students who may struggle with group-work and presentations, and to provide assessment accommodations. Overall, the project has identified a number of positive and negative associations between levels of learner sensitivity and student success and suggests that education about environmental sensitivity for students and teaching staff would be helpful for increasing awareness about the benefits and challenges of environmental sensitivity. Institutional commitment to providing optimal physical learning and social environments may enhance the learning experience for all students. Finally, recommendations for policy, practice and institutions highlight elements that will be of benefit to all students, most especially those who sit at the high end of the sensitivity spectrum.
Research Findings: Shared-picture book reading can stimulate children’s mathematical development. Evidence of learning-supportive characteristics in picture books is limited in this domain. A first step is systematically analyzing the occurrence of domain-specific features in publicly available picture books. We analyzed the occurrence of general book features, features of numbers and quantitative language, and features of sets in 100 mathematical and 45 matched non-mathematical picture books written in Dutch. The results indicated variety among picture books, but more than 50% of the mathematical picture books focused on the 1–10 number range and ascending counting, and less than 20% paid attention to the counting principles and other numeracy skills. Quantitative language was included in 75% of the mathematical picture books, but infrequently across pages and with little variety in wording. Mathematical picture books with a story hardly differed from picture books without a story regarding the presence of domain-specific features. Finally, fewer non-mathematical picture books included mathematical content and generally on less pages compared to mathematical picture books. Practice or Policy: These findings stress the importance for picture book developers and teachers and parents to, respectively, design and select picture books in view of literature regarding children’s mathematical development.
Adults are known to have developed the ability to selectively focus their attention in a goal-driven (endogenous) manner but it is less clear at what stage in development (5–6 & 9–11 years) children can endogenously control their attention and whether they behave similarly to adults when managing distractions. In this study we administered a child-adapted cued visual search task to three age-groups: five- to six-year-olds (N = 45), nine- to eleven-year-olds (N = 42) and adults (N = 42). Participants were provided with a cue which either guided their attention towards or away from an upcoming target. On some trials, a singleton distracter was presented which participants needed to ignore. Participants completed three conditions where the cues were: 1) usually helpful (High Predictive), 2) usually unhelpful (Low Predictive) and 3) never helpful (Baseline) in guiding attention towards the target. We found that endogenous cue-utilisation develops with increasing age. Overall, nine- to eleven-year-olds and adults, but not five- to six-year-olds, utilised the endogenous cues in the High Predictive condition. However, all age-groups were unable to ignore the singleton distracter even when using endogenous control. Moreover, we found better cue-maintenance ability was related to poorer distracter-inhibition ability in early-childhood, but these skills were no longer related further on in development. We conclude that overall endogenous control is still developing in early-childhood, but an adult-like form of this skill has been acquired by mid-childhood. Furthermore, endogenous cue-utilisation was shown as insufficient for preventing attentional capture in both children and adults.
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Executive function, also known as cognitive control or supervisory attention, is required whenever going "on automatic". Classes of situations in which executive functions are required include novel tasks and situations that require concentration, planning, problem solving, coordination, change, conscious choices among alternatives, or overriding a strong internal or external pull. Component cognitive abilities that constitute what collectively is known as executive function include the following: inhibition, that is, the ability to ignore distraction and stay focused, and to resist making one response and instead make another; working memory, that is, the ability to hold information in mind and manipulate it; cognitive flexibility, that is, the ability to switch perspectives flexibly, focus of attention, or response mappings. These abilities are crucial to all forms of cognitive performance. The ability to inhibit attention to distractors makes possible selective and sustained attention. © 2006 by Ellen Bialystok, Fergus I.M. Craik. All rights reserved.
This book provides both a review of the literature and a theoretical framework for understanding the development of visual attention from infancy through early childhood, including the development of selective and state-related aspects in infants and young children as well as the emergence of higher controls on attention. They explore individual differences in attention and possible origins of ADHD.
Measures of instructional time are grouped into four ranges-years, days, hours, and minutes-and the strength of the association to outcomes is reviewed within each grouping. Time predicts learning outcomes at a modest level in most of the research, but with exceptions. The use of composite or precise indicators of time-on-task and the use of content-specific outcome measures seem to strengthen the correlational evidence. Complexities such as diminishing returns, the existence of optimum amounts of time, the proper unit of analysis, and the question of causality imply that true experimental designs would clarify the nature of the relationship. The need to include time as one factor in a theory of educational productivity is presented.
The current study investigates how young children allocate their attention in learning environments. Prior research has shown that elementary school students spend between 25% and 50% of instructional time off-task. However, the available research has not clearly identified the common sources of distraction, nor specified the relationship between the distraction source and learning outcomes. In this study we examined how visual features of the environment which are not relevant for on-going instruction (e.g., manipulatives, posters, artwork, maps, etc.) affect young children's ability to maintain focused attention to the content of a lesson. We addressed this question by experimentally manipulating our laboratory classroom environment (e.g., introducing or removing educational materials irrelevant to the current lesson). The effects of the manipulation on children's off-task behavior and learning were measured. Results suggested that children in the Low Visual Distraction condition spent less time off-task and obtained higher learning scores than children in the High Visual Distraction condition.
This chapter presents analytic methods for matched studies with multiple risk factors of interest. We consider matched sample designs of two types, prospective (cohort or randomized) and retrospective (case-control) studies. We discuss direct and indirect parametric modeling of matched sample data and then focus on conditional logistic regression in matched case-control studies. Next, we describe the general case for matched samples including polytomous outcomes. An illustration of matched sample case-control analysis is presented. A problem solving section appears at the end of the chapter.