MIND, BRAIN, AND EDUCATION
The Effects of Arts Integration
on Long-Term Retention
of Academic Content
Mariale Hardiman1, Luke Rinne1, and Julia Yarmolinskaya1
ABSTRACT— Previous correlational and quasi-
experimental studies of arts integration—the pedagogical
practice of ‘‘teaching through the arts’’—suggest its value for
enhancing cognitive, academic, and social skills. This study
reports the results of a small, preliminary classroom-based
experiment that tested effects of arts integration on long-term
retention of content. We designed matched arts-integrated
(AI) and conventional science units in astronomy and
ecology. Four randomized groups of 5th graders in one school
completed one unit in the treatment (AI) condition and the
other in the control (conventional) condition. To control
for teacher effects, four teachers taught the same subject
to different groups in each condition. We administered
curriculum-based assessments before, immediately after, and
2 months after each unit to measure initial learning and
retention. Results showed no differences in initial learning,
but signiﬁcantly better retention in the AI condition. Increases
in retention were greatest for students at the lowest levels of
Scarce resources in public education and a focus on the
‘‘basics’’ have led to the well-documented narrowing of the
curriculum, often resulting in a reduced role for the arts
in schools (Mishook & Kornhaber, 2006). However, in-
school artistic activity may be valuable not only ‘‘for arts’
sake’’ but also because the arts may improve learning and
student outcomes more broadly. A number of researchers have
proposed that knowledge and skills gained uniquely through
the arts correlate with success in other academic domains
(Catterall, 2002; Deasy, 2002; Fiske, 1999; Spelke, 2008;
Wandall, Dougherty, Ben-Shachar, Deutsch, & Tsang, 2008).
1Johns Hopkins University
Address correspondence to Mariale Hardiman, School of Education, Johns
Hopkins University, 2800 N. Charles St., Baltimore, MD 21218; e-mail:
Still others argue that the arts contribute to the development
of more general thinking skills and dispositions that beneﬁt
performance (Hetland, Winner, Veenema, & Sheridan, 2007).
Arts integration, deﬁned here as the infusion of visual and
performing arts activities into instruction in nonarts subjects,
is also thought to enhance content learning (Burnaford, Brown,
Doherty, & McLaughlin, 2007; Martin et al., 2013). Several
quasi-experimental studies reported signiﬁcant differences in
academic outcomes in schools that instituted arts-integrated
Dumais, & Hampden-Thompson, 2012; Phillips, Harper, Lee,
& Boone, 2013; Scripp, Burnaford, Vazquez, Paradis, &
Sienkiewicz, 2013). Yet, to our knowledge, no randomized
controlled trials have tested the effectiveness of arts-integrated
(AI) curricula. Such experiments are important because
schools and/or teachers generally self-select AI instruction,
raising the possibility that differences in student outcomes
may be the result of selection bias. In this article, we report
the results of a small, preliminary randomized experiment
that investigated the effects of AI ﬁfth-grade science units on
long-term retention of content.
Arts Integration and Long-Term Retention of Content
On the basis of the idea that rehearsal of information
consolidates memories for long-term storage (Kandel, 2006),
Hardiman (2003, 2010) posited that AI instruction improves
retention by prompting students to rehearse content through
the use of various visual and performing arts activities which
may enhance student engagement (e.g., Smithrim & Upitis,
2005). Further, Rinne, Gregory, Yarmolinskaya, and Hardiman
(2011) argue that the arts may engage learners in thinking
about new information in ways that improve retention, for
example through semantic elaboration (e.g., Craik & Tulving,
1975), generation of information from a cue (e.g., Slamecka &
Graf, 1978), enactment (e.g., Mohr, Engelkamp, & Zimmer,
1989), oral production (MacLeod, Gopie, Hourihan, Neary,
& Ozubko, 2010), ‘‘effort after meaning’’ (e.g., Zaromb &
144 ©2014 International Mind, Brain, and Education Society and Wiley Periodicals, Inc. Volume 8—Number 3
Mariale Hardiman et al.
Roediger, 2009), emotional arousal (e.g., Cahill & McGaugh,
1995), and pictorial representation (e.g., Paivio, 1971). The
theory that arts integration improves retention of content is
derived from the notion that the arts naturally take advantage
of these strategies whereas conventional instruction typically
This study contributes to the growing body of research
on arts integration by testing the effects of AI curricula
on academic outcomes through a preliminary study using a
randomized experimental design. We developed matched AI
and conventional versions of 5th grade science units in ecology
and astronomy and tested their effects on retention of content
in four ﬁfth-grade classrooms. We hypothesized that the AI
units would lead to better retention of content on a delayed
posttest. We did not expect that the arts-focused curricula
would facilitate the learning of more content over the course
of the unit—rather, the prediction was that more of what
students had learned would be retained.
Our study was conducted at an urban elementary school that
serves over 600 students (98.5% African American), largely
from low-income families (83.5% are eligible for free and
reduced-price meals [FARM]). The faculty (65.8% African
American), are mostly classiﬁed as ‘‘highly qualiﬁed teachers’’
(85.3%) according to the school district. All participants in
our study were African American (47% female; Mage =10.8),
and 14% were identiﬁed as students with disabilities (eligible
for an individualized education plan [IEP]). All students in the
ﬁfth grade (97) at the school agreed to participate in the study.
We developed two science units in astronomy and ecology that
spanned 3 weeks of instruction. For each unit, we designed a
control version that utilized conventional forms of instruction
and a treatment, arts-integrated (AI) version that incorporated
a variety of forms of artistic activity (music, visual arts, and
performance arts) into instruction. (Interested educators can
contact us concerning access to these lessons.)
We ﬁrst mapped the content to be taught over the course
of both units and segmented this content into 15 days of
instruction. We then designed conventional lesson plans
for each day based on the 5E learning cycle model (Bybee
et al., 1989): Engage,Explore,Explain,Elaborate,andEvaluate.
One conventional instructional activity was designed for each
of the ﬁve E’s, and activity durations were estimated. We
then compiled the AI units by designing and substituting
in AI learning activities for approximately two-thirds of the
standard activities. Each AI activity was designed to address
the same science content as the standard activity it replaced
and to take the same amount of time as its matched non-
AI activity. In designing the AI activities, we controlled for
a variety of other factors that could potentially inﬂuence
retention. These included: the order in which information was
presented or addressed, the ‘‘structure of the activity’’ (e.g.,
individual, pairs, small groups, or whole class), and the use
of multimedia. In addition, we controlled for whether the
activities were student-led or teacher-led and whether the
activities produced student work, with the aim of ensuring
that any student learning effects observed would not be simply
attributable to ‘‘active learning’’ (e.g., Bonwell & Eison, 1991).
Both AI and conventional curricula were designed to be active
and engaging, differing only with respect to the integration of
arts-based activities in place of conventional activities.
Arts-integrated substitutions were designed as follows:
1. Worksheets with written responses were replaced with
activities in which students drew responses to the same
prompts. This substitution was made to take advantage
of the ‘‘picture superiority’’ memory effect (e.g., Paivio,
1971), in which pictures tend to be remembered better
than words, even when verbal tests are administered.
2. Instead of silently reading informational texts, students
in the AI condition silently read stories containing the
same content. Here we aimed to improve recall by adding
elaborative language (e.g., Craik & Tulving, 1975) and
eliciting emotional responses (e.g., Cahill & McGaugh,
3. Instead of reading text passages out loud, we designed
dramatic scripts that children were asked to perform.
4. Instead of verbal group presentations, students performed
sketches or tableaux (two forms of dramatic play acting)
that we hoped would take advantage of the enactment
effect, which posits that carrying out an action produces
better memory than simply reading or hearing about the
action (Mohr et al., 1989).
5. For vocabulary development, in control lessons students
wrote ‘‘elaborations’’ for each term by expanding on the
deﬁnition or providing an example. In the AI condition,
students created ‘‘doodles’’ (simple drawings) to elaborate
the meaning of the new term, which we hoped would
leverage the picture superiority effect (e.g., Paivio, 1971).
6. At the conclusion of each unit, one additional day (Day 16)
was dedicated to review. We selected 10 matched pairs
of activities from across each unit and instructed teachers
to have students present the work they completed during
the activity. In the AI unit, this day was framed as an ‘‘art
exhibition day,’’ and in the control unit, it was simply a
For examples, in one astronomy activity in the control
condition, students traced the shapes of galaxies, wrote
descriptions in notebooks, and shared them with the class.
Volume 8—Number 3 145
The Effects of Arts Integration
In the matched AI activity, students drew shapes on posters
and used dance movements to depict galaxy shapes in small
performances. In another activity, students in the control
condition described different planets’ features by making
Venn diagrams; in the matched AI activity, students drew
advertising posters to sell real estate on different planets.
We developed multiple-choice curriculum-based assessments
to measure student learning and retention of the science con-
tent. Assessments consisted of 25 multiple-choice questions as
well as one ‘‘brief constructed response’’ (BCR short-answer)
item. We created three versions of each curriculum-based
assessment by changing question wording and/or modifying
answer choices. These measures were deliberately designed
to be difﬁcult to avoid ceiling effects and make retention
challenging. The order in which the three different forms were
administered for the pretest, posttest, and delayed posttests
was counterbalanced across participants.
Prior toimplementation,teachers receivedapproximately 10 hr
of training to review the content and activities in each unit.
To mitigate potential bias, teachers were not informed of
our theory regarding how arts integration might improve
retention. Instead, they were informed only that the study
aimed to test the effectiveness of arts-integration science
curricula versus conventional curricula.
All ﬁfth grade students in the school were randomly
assigned to one of four equally sized groups for 1 hr per day
of science instruction. To ensure that all participants received
equivalent potential beneﬁts from the treatment, students
were taught one unit in the AI condition and the other in the
control condition. After completing the ﬁrst unit, each group
then completed the second unit with a different teacher in the
alternate condition (see Table 1). Pretests were administered
the day before each unit began, posttests at the conclusion of
each unit, and delayed posttests approximately 8 weeks after
Each teacher taught one randomized group of students in
one condition (treatment or control) and then taught the
Randomized Group Assignments
Teacher 1 Teacher 2 Teacher 3 Teacher 4
Astronomy Astronomy Ecology Ecology
Period 1 Group ABCD
Condition AI Control Control AI
Period 2 Group DCBA
Condition Control AI AI Control
matched unit in the other condition to a second randomized
group. Thus, each teacher taught one content area in both
conditions, and the order of conditions was counterbalanced
across teachers. This design allowed for the control of teacher
effects. Fidelity-of-implementation observers were present in
each classroom approximately 60% of the time to ensure that
the units were taught as designed in terms of timing, content,
and activities. Observers were instructed to note any deviation
from the curriculum as well as any differences in instructional
quality across conditions.
Our analysis of student outcomes includes 82 students (of 97)
who completed all assessments and for whom we received
demographic information and standardized test scores from
the district. We calculated each student’s percent correct on
each test, measuring initial learning in terms of the difference
between the initial posttest (T2) and pretest (T1) scores
1). Meanwhile, consistent with previous studies
(Custers, 2010), retention equaled the delayed posttest (T3)
score as a proportion of the T2score (T3/T2). We identiﬁed
three outlying high retention scores (two treatments, one
control) whose scores fell more than three standard deviations
above the mean. These were winsorized to the next highest
value. Students completed the BCR (short-answer) item at the
end of each test at a very low rate (<50%), so these responses
were not analyzed.
We conducted repeated-measures ANOVAs of initial learn-
ing and retention. Factors in each model included condition
order(AIvs.Controlﬁrst),AIsubject (Astronomy vs. Ecology),
gender, FARM eligibility, IEP eligibility, and reading and math
proﬁciency levels (Proﬁcient/Advanced vs. Basic). To conserve
statistical power given our relatively small sample size, factors
that did not exhibit signiﬁcant main effects or interactions
withcondition(AI vs. Control) were removed from our models.
None of the factors listed above was a signiﬁcant predictor
of initial learning, nor was there a signiﬁcant effect of the
treatment on initial learning (AI vs. Control), F(1, 82) =1.496,
p=.225. However, our analysis of retention revealed a signif-
icant main effect of AI, F(1, 80) =6.570, p=.012, ηp2=.076,
as well as a signiﬁcant interaction between AI and reading
proﬁciency level F(1, 80) =7.070, p=.009, ηp2=.081. Mean
retention was approximately .2 SD greater in the AI condition
(M=.912) than in the control condition (M=.866)—an effect
size that is ‘‘small’’ based on Cohen’s (1988) benchmarks, but
relatively typical of effective educational interventions (Hill,
Bloom, Black, & Lipsey, 2008). The signiﬁcant interaction
indicates that students at a ‘‘basic’’ level of reading proﬁciency
(n=16) were the primary drivers of the observed main effect.
Mean retention among these students was approximately .9
SD greater in the AI condition (M=.976) than the control
146 Volume 8—Number 3
Mariale Hardiman et al.
Mean Test Scores (Percent Correct) by Test Time, Condition (AI vs.
Control), and Reading Proﬁciency Level
proﬁciency level Pretest Posttest
AI Basic 29.0 44.0 39.0 97.6%
Proﬁcient/advanced 38.6 59.0 52.8 89.7%
Control Basic 25.0 46.3 32.5 72.0%
Proﬁcient/advanced 38.9 61.3 54.1 90.2%
condition (M=.720). Interestingly, in the AI condition, poor
readers exhibited better retention than their proﬁciently
reading peers, though it is important to note that these
students’ long-term learning gains were smaller by about 4
percentage points, meaning they acquired less knowledge
to begin with. See Table 2 for mean scores across the three
Although there was no signiﬁcant effect of arts integration
on initial learning, arts integration curricula had a signiﬁcant
effect on retention. Participants learned roughly the same
volume of science content regardless of how the units were
taught, but scores on a delayed posttest showed that students
retained what they learned signiﬁcantly better when taught
through AI instruction. These ﬁndings provide preliminary
support for the theory that arts integration naturally leads
students to interact with academic content in ways that
promote long-term retention.
An important caveat to this ﬁnding is that it was the group
of students at a ‘‘basic’’ level of proﬁciency who showed the
biggest gains in retention. In many ways, this makes sense—AI
instructionrelies less on reading and writing than does conven-
tional instruction, so students who struggle with reading and
writing may beneﬁt more from the opportunity to learn con-
tent through alternative, arts-based means. Thus, for students
who are still developing literacy skills, arts integration may
represent a useful means of improving learning. We emphasize
that we are not endorsing reductions in reading and writing.
If reading and writing activities were scaled back as part of
AI instruction, the possibility of deleterious effects on reading
and writing would need to be addressed, and educators would
presumably need to compensate for this loss elsewhere in
The generalizability of our ﬁndings is limited by our
relatively small sample of participants of the same age from a
single school. In particular, our sample included 16 basic-level
readers. Future research should attempt to replicate our results
with a larger sample. Additionally, it is possible that results
were biased by some teachers’ natural enthusiasm for the use
of the arts in instruction. Further, although teachers were told
that the study aimed to compare the effectiveness of AI and
conventional curricula, the fact that we were interested in this
comparison suggests our hypothesis that the AI curricula may
lead to better outcomes. However, the presence of observers
in the classroom during a majority of instructional time helped
to ensure that the lessons were taught as written.
Despite these limitations, we believe our study provides
important preliminary evidence that many students, partic-
ularly struggling readers, may retain academic content better
when instruction is integrated with the arts, particularly
when mastery requires possession of considerable amounts
of declarative knowledge. We hope that in the future, larger
scale studies of arts integration that utilize randomized
experimental designs will be conducted, and that these
studies would be able to disentangle the underlying causes
of the effects. Such efforts would provide for more effective
measurement of the potential beneﬁts of AI instruction, which
may eventually lead to ﬁndings that can enhance educational
practice and policy in important ways.
Acknowledgment—The authors would like to thank the Joseph
P. Drown Foundation for support of this study.
Additional supporting information may be found in the online
version of this article:
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