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Investigating the Cascading, Long Term Effects of Informal Science Education Experiences Report

  • Institute for Learning Innovation
  • Institute for Learning Innovation


Science educators invest their time, effort and expertise in the effort to “make science enjoyable and interesting” and “inspire a general interest in and engagement with science.” The consensus purpose for science education experiences are the long- term transformation of the learner; an individual sufficiently engaged that s/he will have the interest and tools necessary to pursue a “cascade” of experiences subsequent to the initial educational event. Support for this conclusion comes from a large-scale survey of both informal and formal science educators in the United Kingdom where 90% of science educators from across more than a dozen sectors, including museums, science centers, zoos and aquariums, print and broadcast media, outdoor facilities, libraries and schools, rated the above two educational goals as their highest priorities (Falk, et al., 2015). A laudable purpose indeed, and many dedicated professionals work to that end, but the question remains: How can the field measure whether or not any particular educational experience sets off long-term, cascades of additional learning experiences?
Investigating the Cascading,
Long Term Eects of Informal Science
Education Experiences Report
John H. Falk , Judith Koke , C. Aaron Price , Scott Pattison
Suggested citation: Falk, J. H, Koke, J., Price, C. A. & Pattison, S. (2018). Investigating the cascading,
long term effects of informal science education experiences report. Beaverton, Oregon: Institute for
Learning Innovation.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Science educators invest their time,
effort and expertise in the effort to
“make science enjoyable and inter-
esting” and “inspire a general interest
in and engagement with science.”
The consensus purpose for science
education experiences are the long-
term transformation of the learner;
an individual sufficiently engaged that
s/he will have the interest and tools
necessary to pursue a “cascade” of
experiences subsequent to the initial
educational event. Support for this
conclusion comes from a large-scale
survey of both informal and formal
science educators in the United
Kingdom where 90% of science
educators from across more than a
dozen sectors, including museums,
science centers, zoos and aquariums,
print and broadcast media, outdoor
facilities, libraries and schools, rated
the above two educational goals as
their highest priorities (Falk, et al.,
2015). A laudable purpose indeed,
and many dedicated professionals
work to that end, but the question
remains: How can the field measure
whether or not any particular educa-
tional experience sets off long-term,
cascades of additional learning
A number of major challenges limit
valid and reliable documentation of
the long-term effects of any science
education experience. Two are
particularly vexing, the complex
and cumulative nature of science
learning and the inherent limitations
of current research methods and tools.
The Complexity of Learning
It was once assumed that learning,
and science learning in particular, was
a straightforward, linear process that
primarily occurred through directed
instruction, i.e., the absorption-trans-
mission model (cf., Bransford, Brown
& Cocking, 2000; Roschelle, 1995).
However according to a recent
OECD publication (Dumont, Istance
& Benavides, 2012), the dominant
view of learning, today, is a socio-
constructivist view, in which “learning
is understood to be importantly
shaped by the context in which it is
situated and is actively constructed
through social negotiation with others”
(p.3). From this perspective, any
particular learning experience, whether
it takes place within a classroom or a
science museum, is almost certainly
influenced by a host of other learning
experiences that occurred previously
in a person’s life. Thus, the ultimate
outcome or effect of a particular
learning event is likely to be only a
partial consequence of that specific
event. A full accounting of even short-
term effects would require knowing
something about each learner’s unique
learning history prior to the event, and
then only by viewing these trajectories
in the aggregate could some under-
standing of the overall outcomes/
effects of that event be inferred
(cf., Falk, 2018).
In other words, learning is rarely, if
ever, instantaneous (Bransford, Brown
& Cocking, 2000). Individuals develop
an understanding of and appreciation
for scientific topics through an ongoing
accumulation of experiences and
understandings derived from multiple
sources (e.g., Anderson, Lucas,
Ginns, & Dierking, 2000; Barron,
2006; Bathgate, Schunn & Corenti,
2013; Bell, et al., 2013; Bransford,
Brown, & Cocking, 2000; Falk &
Needham, 2013; Ito, et al., 2013;
Lemke, 1999; NRC, 2015). For
example, an individual’s understanding
of the physics of flight might repre-
sent the cumulative experiences of
completing a classroom assignment
on Bernouli’s principle, reading a
book on the Wright brothers, visiting
a science center exhibit on lift and
drag, and watching a television
program on birds. For the individual,
all of these experiences are combined,
often seamlessly, as they construct
a personal understanding of flight;
no one source is sufficient to create
understanding, nor one single institu-
tion solely responsible. In the above
scenario, when did this individual
learn about flight and what experi-
ences most contributed to learning?
And how could one specifically
identify and attribute the pieces
learned while at, for example, the
science center as opposed to the
pieces learned in school, reading,
or television? In summary, science
learning is neither linear nor easily
isolated in time and space.
Methodological Issues
This leads to the second major set
of challenges in measuring the
cascading events that constitute
science learning—methodological
challenges. Historically, the vast
majority of efforts designed to
measure the consequences of a
science education event were limited
in both duration and scope. The most
common measures, in both informal
and formal contexts, utilized some kind
of pre-post design which measured
changes in understanding, attitude,
Science learning is neither
linear nor easily isolated in
time and space.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
etc., based on responses to some
kind of test administered immediately
preceding and immediately following
a particular educational event. The
assumptions of this approach being
that: 1) within this short timeframe
changes in understanding, interest,
etc. should emerge; and 2) any
changes that do occur, are directly
attributable to the educational inter-
ventions of that event. As should be
clear from the above review, these
assumptions may or may not be
actually true. Changes in the mental
structures, i.e., learning, often take
time to emerge and in the absence
of suitable preexisting structures and
scaffolds, as well as the presence or
absence of subsequent reinforcing
events, may not persist (Eaglemen,
In response to this first issue, a
number of investigators, including
particularly many within the informal/
free-choice realm, have attempted to
lengthen the timeline of assessment by
weeks, months and even years (e.g.,
Adelman, Falk & James, 2000; Bell,
et al., 2013; Falk & Dierking, 2014;
Falk, et al., 2004; Flagg, 2005; Fraser,
et al., 2012; Peterman, Pressman
& Goodman, 2007). Naturally, the
longer the timeframe, the greater
the challenge in maintaining contact
with individuals and the potential for
introducing other types of biases
into the data. For example, panel/
longitudinal designs are notorious for
becoming less representative over
time as the population changes and
as panel members drop out (Groves,
1989; Taplan, 2005). Longitudinal
designs may also be prone to certain
forms of measurement error, such
as “conditioning” and “seam” bias
(cf., Groves, 1989; Lavrakas, 2008).
Another problem is that virtually all of
these longitudinal studies, including
those cited above, utilized in whole or
in part self-report data; an approach
long viewed with skepticism with the
social science community (cf., Baer,
Renaldo & Berry, 2003). Although a
number of studies from various disci-
plines have established that self-report
data, though not perfect, are actually
reasonable surrogates for more direct
measures, especially when using
survey data (Chan, 2009; Gonyea,
2005; Vaske, 2008), finding alternative
or at least additional measures to
support the validity of changes
would seem important.
Equally problematic, and potentially
even more intractable, has been the
challenge of attribution. Given the
inherently incremental and distributed
nature of science learning, how can
one be certain that any observable
changes in an individual’s knowledge,
interest or behavior are actually attrib-
utable to the experience under study?
Very few studies have seriously dealt
with this issue.
Responding to the Challenge
A group of 12 researchers gathered
at MSI to discuss issues surrounding
the critical but challenging area of how
to measure the long-term effects or
impacts of ISE experiences, chaired
by Dr. John Falk, Oregon State
University and Institute for Learning
Innovation, and hosted by Dr. Aaron
Price, Museum of Science & Industry,
Chicago, (MSI) on July 18, 2018. All
the invitees had expertise and expe-
rience in this area, resulting in a rich
conversation based equally in theory
and practice. The goal of the discus-
sion was to build on the collective
experience of the group to identify and
address key challenges in this area
of research and to propose potential
solutions for advancing the field. This
whitepaper provides a summary of
those deliberations.
The day was divided into three
time blocks. The early morning was
devoted to large group discussions
about long-term learning research and
its inherent issues and challenges,
but the bulk of the day was spent
in three smaller working and writing
sub-groups; each group focused on
one of three key issues:
1. Timelines and slopes (how to
determine when to collect “long-term”
measures; what constitutes long-term;
what are the “slopes” of effects?)
2. Attribution (effects vs. impacts;
how to determine causal relationships)
3. Accommodating changes in context
(e.g., how to deal with the impact
of major shifts in culture and society
that occur in the midst of long-term
measures) and whose concept or
definition of “success” should one
measure (the public’s, ISE staff’s, ISE
A designated facilitator and writer led
each sub-group. After both a morning
and afternoon of intensive conversa-
tions, the leaders shared a summary
of the conversations with the larger
group for one final whole-group,
reflection session.
Each of the small group facilitators
compiled a written summary of their
group’s discussion that was then
circulated to their group members
for comment and approval. Every
effort was made to ensure that these
summaries equitably represented the
contributions of all group members.
The sections below are the products
of these efforts.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Measuring change over time is a
fundamental challenge to almost all
sciences (Duncan & Duncan, 2009).
In education and many other social
sciences, intervention goals are often
focused on achieving long term and/
or permanent outcomes, although
most measurements of success are
usually very short-term. In informal/
free-choice settings, this dual reality
is equally true. Interventions in such
settings are often of relatively short
duration, creating a greater dilemma
than in many other educational
contexts. For example, an after-school
program may work with children for a
few hours a week over the course of
a year or two. And a museum exhibit
may gather the attention of a guest
for just a few minutes. What kind of
long-term outcomes can one expect
from such short interventions? Happily,
an abundance of research has shown
that even these very brief experiences
can have very positive effects (see
reviews by Anderson, Storksdieck
& Spock, 2009; Falk & Dierking
2018; NRC, 2009). However, despite
considerable research and support
for the existence of some kinds of
positive long-term effects for informal/
free-choice learning experiences, the
specific nature of these long-term
effects is less well known. In particular,
questions remain about the general
duration (e.g., do effects last days,
weeks, months, years?) and character
(e.g., do effects wax and wane or do
they just happen?) of such learning.
The challenge to both researchers and
education programmers is how to best
use their limited resources to construct
the most rigorous methodology or
intervention, yielding the best results.
In other words, what is lacking is a
robust model of change. Having such
a model or set of best practices would
go a long way in helping researchers
and practitioners design long-term
studies/experiences within informal/
free-choice learning settings.
Education is not the only domain to
have been concerned with this topic.
We could learn from the many other
fields that are interested in long-term
outcomes. Medicine comes to mind
first with its emphasis on long-term
impact of both interventions and
side effects. When it comes to drug
interventions, long term effects are
often described using dose-response
relationships (Farinde, 2017). The
relationships are described using
key factors such as potency (of the
intervention/drug), slope (change of
effect over time) and maximal efficacy
(time and significance of peak impact).
In the social sciences, analysis of
long-term change is common through
the use of latent growth models (LGM)
(McArdle & Epstein, 1987; Duncan &
Duncan, 2009; Isiordia & Ferrer, 2018).
Long-term effects are also used as
a component of structural equation
modeling. These flexible statistical
models are used to describe and
predict growth and change over time
in a manner that allows the researcher
to include many of the complexities of
social science data (such as multiple
contextual variables or correlated
responses between participants).
One way to visualize long-term
outcomes are with slopes of effect
(hereafter: SoE). Similar to growth
curves and dose-response relation-
ships, the slopes show change over
time from a baseline level and with
an intervention moment. However,
they do not assume positive, linear
or sustained growth. Typically, the
dependent variable would be displayed
on the vertical axis and time on the
horizontal. Considering that human
nature loves to work in logarithms
(Dehaene, Izard, Spelke & Pica, 2008),
Question 1: Timelines and Slopes
C. Aaron Price, John Falk, Gail Jones
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
one may want to begin with an inverse logarithmic function
as the basis for a typical SoE. But the more complex the
model becomes, the more degrees of freedom are needed
to characterize it. Figures 1–4 show four hypothetical learn-
ing-based SoEs.
Implications and Recommendations:
Researchers designing long-term studies may want to draw
hypothetical SoEs as a thought experiment to help them
determine when to measure outcomes and, combined with
power analysis, how much data to collect at each point
along the time continuum. As one can imagine, the more
complicated the slope the more sampling points across time
are needed to fully measure it and, as always, the less steep
the slope, the more data required.
Lines Remembered
16 20 24 28 32 36
Figure 1: Forgetfulness
Figure 1: A hypothetical inverse logarithmic decline. In this case, a student
memorized the lines to Lewis Carroll’s Jabberwocky for a class project, then
forgot them over time.
Lesson Learned
Figure 2: Hot Stoves Hurt
Figure 2: A child touches a hot stove and learns a valuable lesson which
they never forget.
Lesson Learned
10 20
Visit 1 Visit 2 Visit 3
30 40 50 60 70 80
Figure 3: Short Museum Visits
Figure 3: Someone visits a museum as a child and learns about butterflies
in an exhibit. They slowly forget most of what they learn only to have it
reinforced when they visit again with their own children. This time, they recall
more than before, but still forget some content until they visit again—this
time with grandchildren. At this point, they remember the core information.
Points Per Game
12 14 16 18 20 22 24 26 28 30 32 34 36
Figure 4: Long Term Engagements
Figure 4: A child starts playing basketball at school, six months per year
annually. Every year, they get a little better during the season. Then, after
college they only play recreationally. They still improve, but at a slower pace.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Serious issues surround the question
of attribution of causal relationships
related to measuring the long-term
impact and influence of informal/free-
choice learning experiences. In other
words, to what extent can measured
outcomes and effects that are found to
relate to specific informal/free-choice
learning experiences be claimed to be
directly or indirectly caused by these
experiences? This issue is central to
research, policy, and practice in the
field. A long-standing question for
researchers studying learning inside
and outside of school has been the
extent and degree to which informal/
free-choice learning experiences and
institutions contribute to learning
outcomes for children and adults, as
opposed to these outcomes being
a result of selection bias or other
confounding factors (e.g., Falk &
Dierking, 1997; Falk, Dierking, et al.,
2016; Falk & Needham, 2013; Falk,
Pattison, Meier, Bibas, & Livingston,
2018; Tai, Liu, Maltese, & Fan, 2006).
The answer to this question has
profound implications for not only
educators working in these spaces,
who have advocated for the impor-
tance of informal/free-choice learning
(National Research Council, 2009), but
also policy makers and funders, who
historically have been skeptical about
investing significantly in education
initiatives outside the formal school
system (Falk & Dierking, 2010).
In our discussion, three themes
emerged that were further supported
and informed by the pre-workshop
readings and other resources shared
by participants:
1) Studying questions of attribution
and causality is important for the field.
2) Attribution and causality are more
complex than assumed by many
traditional models, such as pre/post
experimental studies.
3) Testing and supporting claims of
attribution and causality is an ongoing
process, beyond a single study.
The group also identified communi-
cation during and after the research
process as a cross cutting topic that
was relevant to each of the themes.
Finally, the group discussed implica-
tions and recommendations related to
this emergent understanding of attri-
bution for understanding the long-term
impact of informal/free-choice learning
The Importance of Attribution
Many scholars have highlighted the
challenges of making definitive claims
about the causal impacts of informal/
free-choice learning experiences on
participant outcomes or of assessing
how the contributions of these
experiences relate to the ongoing,
cumulative nature of learning across
a person’s life, inside and outside of
school (Falk, Dierking, & Foutz, 2007;
National Research Council, 2009;
Pattison & Shagott, 2015). Some
have even suggested that the focus
on simple causality and attribution
is misguided or antithetical to the
idiosyncratic, free-choice nature of
informal learning experiences and
institutions (cf., Falk, et al., 2016a).
The group was unanimous in agreeing
that it is critical for researchers to
study these causal relationships and
test hypotheses about the direct
and indirect impacts of informal/
free-choice learning experiences
on long-term participant outcomes.
Researchers in this field have priori-
tized research-practice connections
and often work directly with and
alongside educators and policy makers
to support and enhance learning
outside of school. These educators
and policy makers are, in turn, focused
on using research to make decisions
about what types of experiences and
practices lead to positive learning
effects over the long term and how
funding and other resources can best
be invested to support learning. This
includes decisions about the relative
focus on traditional education institu-
tions, such as schools, and emerging
institutions and systems outside of
school or across formal and informal
education settings. At the core, these
are questions about causal relation-
ships, even if the causal mechanisms
and processes are not always simple,
direct, or immediate. Thus, the group
agreed that it is not enough for the
field to assess correlational relation-
ships or provide anecdotal evidence
of impact (although those types of
studies can provide important contri-
butions to causal questions). Instead,
researchers must work with educators
and institutions to find innovative ways
to test assertions about the causal
relationship between informal/free-
choice learning experiences and the
long-term effect on participants.
Question 2: Attribution
Scott Pattison, Lynn Dierking, Robert Tai, Jim Kisiel
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
The Complexity of Attribution
Although the group agreed that
focusing on causal relationships is
important, much of the discussion
focused on how approaches to
studying, describing, and commu-
nicating these relationships need to
become much more nuanced and
sophisticated in order to account
for the complexity of attribution and
causality related to long-term learning
outcomes. Traditional perspectives
on causality and attribution focus on
the classic formulation of establishing
temporal order, measuring correlation,
and accounting for confounding
factors or alternative explanations
(Campbell & Stanley, 1967; Fu,
Kannan, Shavelson, Peterson, &
Kurpius, 2016; Shadish, Cook, &
Campbell, 2001). However, many
other perspectives on causality and
approaches to studying causal rela-
tionships have emerged that recognize
the complexity of learning in the real
world (Gates & Dyson, 2017; Lemke,
Lecusay, Cole, & Michalchik, 2015).
For example, some perspectives
emphasize feedback loops and
emergent properties within complex
systems, rather than linear relation-
ships, or highlight the importance of
participant narratives of causal chains.
In the field of evaluation, there has
been a growing focus on contribution
rather than attribution, recognizing
that the impact of any single program
or initiative will be influenced by
the variety of other experiences in
a person’s life, before, during, and
after the program takes place (Gates
& Dyson, 2017). And research in
informal/free-choice learning settings
has consistently highlighted the
important influence of what individual
participants bring with them to the
experience and what happens to them
afterwards. Even within a traditional
framework of thinking about causality,
causal relationships almost always
represent averages or probabilities,
rather than universals, certainties, or
inevitabilities. Modern statistical tech-
niques allow researchers to model and
test complex causal chains, multiple
contributing factors, and mediating
and moderating relationships, which
often reveal the nuances underlying
simple relationships between experi-
ence and outcome.
Overall, the group recommended
that researchers seek to study
and communicate more nuanced
hypotheses about causality that reflect
the situated and contingent nature
of these relationships. For example:
These types of experiences will likely
lead to these types of outcomes for
these groups of participants in these
particular circumstances. As discussed
more below, this formulation of a
causal relationship makes clear the
ongoing work, beyond a single study,
that is needed to provide evidence
for how the relationship generalizes
to different contexts and different
participants and the contextual factors
and contingencies that influence the
strength or probability of that relation-
ship. Similarly, the group discussed the
interplay between internal validity, or
the strength of the causal relationship
between cause and effect as concep-
tualized in a traditional experimental
study, and other related issues such
as external validity (i.e., is the causal
claim relevant to contexts beyond the
research study) and generalizability
(i.e., does the causal relationship hold
true for other participants, contexts,
programs, etc.). Although clarifying the
limitations of such studies is critical
for the field, helping decision-makers
avoid blind acceptance or overly
critical rejection of findings becomes
an equally important role for those
researchers exploring long-term
The Ongoing Study of
Because causal relationships are
complex, situated, and contingent,
the group discussed the importance
of moving beyond a focus on single
studies and instead developing bodies
of research that cumulatively test and
explore the limits of hypothesized
causal relationships. A statement of
a causal relationship is in essence
an argument about a hypothesized
connection between one or more
causal factors and one or more
outcome measures. Any single study
can only provide partial and imperfect
evidence to support, or contradict,
that causal argument. Researchers,
therefore, must clearly understand
and communicate the strengths and
limitations of the evidence within a
given study and strive to test and
explore those strengths and limitations
with other study findings, different data
sets, and further investigations. The
group agreed that it is the responsibility
of the researcher to be: transparent
about methods and their connection
to evidence and claims; frame claims
appropriately given the level of
evidence and limitations of a particular
study; describe the study context in
which those claims are situated; use
language about causal relationships
carefully and appropriately; and help
outline for the field the next steps
needed to continue investigation of
the underlying causes and attributions
motivating the study.
Similarly, it is the responsibility of the
field and consumers of research to
ask critical questions and foster a
professional culture of respectful and
productive debate about evidence,
claims, and methods. Researchers can
model these expectations by inviting
and responding productively to study
questions and critiques. Ideally, the
group discussed how this ongoing
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
process of testing causal attribution
should be informed by direct studies of
causal relationships as well as explo-
rations of the processes and mecha-
nisms underlying those relationships,
which can shed light on or help nuance
the understanding of the relationships
themselves (Shadish et al., 2001). In
this process, quantitative, qualitative,
and mixed-method approaches are all
important for contributing to a deeper
understanding of questions about
cause and attribution.
The Communication of
During the group discussion, commu-
nication emerged as a cross-cutting
topic relevant to all three of the themes
described above. This communication
involved how researchers describe
and share study methods and findings,
how these messages are tailored
to different audiences, and how
researchers collaborate with educators
and policy makers.
As noted, the group extensively
discussed the responsibility of
researchers in carefully articulating their
causal claims, study methods, level of
evidence provided by the data, limita-
tions and alternative explanations, and
questions for future work. However,
this type of technical language is often
not appropriate or understandable
by non-researcher audiences, such
as educators and policy makers. The
group noted that the allure of simple
causal arguments and immediately
actionable findings is strong for many
stakeholders, including funders, which
can make it difficult for researchers to
situate the limits of a single study or
set of data appropriately. Addressing
this issue involves not only finding new
approaches to communication and
ways of collaborating with educators
and policy makers, but also new
efforts to develop shared understand-
ings about the scientific process. All
stakeholders interested in the effects
of informal/free-choice learning need
to understand that the development of
knowledge through scientific research
is incremental, ongoing, and imper-
fect; such is the nature of science.
Understanding the limitations of how
science and research work (i.e., claims
are based only on evidence, evidence
may be interpreted in different ways,
and absolute certainty is impossible
due to the fact that new information
may require revisions to claims) is
important when making sense of
these investigations and the reported
outcomes. The strengths, weaknesses
and limitations of studies that examine
these complex interactions must be
made clear to stakeholders, who in
turn must examine such work with a
critical yet informed perspective.
In addition to communicating the
limitations of particular studies or
findings, the group agreed that
progress on understanding causal
relationships related to the long-
term effects of informal/free-choice
learning experiences are only possible
through ongoing communication and
collaboration between researchers and
practitioners. Educators and practi-
tioners working directly with programs
and participants can help researchers
understand the complexities of those
experiences, what causal relationships
and pathways are worth studying, and
how those might be captured through
research methods, measures, and
analyses. Similarly, researchers can
help practitioners develop program
models and theories of change
that account for the complexity of
long-term impacts and can provide
evidence to help guide decisions
about the educational strategies and
programs that are likely to lead to
those impacts. Both researchers and
educators can work together to pursue
approaches to supporting learning
that recognize rather than ignore the
individual, situated, and contingent
nature of informal/free-choice learning,
such as allowing for personalization
and creating ongoing support beyond
a particular program or experience.
Challenges and Opportunities
for Studying Attribution
In addition to the ideas above, the
group discussed other challenges
and opportunities inherent to studying
attribution and causality related to
the long-term effects of informal/free-
choice learning experiences:
• Designing for complexity including
methods and analytic approaches that
capture the complexity of factors influ-
encing long-term effects and creating
broader program initiatives that
influence or strategically align multiple
experiences for greater impact.
• Understanding variation across
informal/free-choice learning contexts
related to attribution, such as different
levels and types of possible outcomes
from a single museum visit or a year-
long afterschool program.
• Training and supporting researchers
and evaluators in the field to use a
variety of tools and perspectives,
including approaches from other fields
(e.g., Baer, 1988; Fivush, McDermott
Sales, Goldberg, Bahrick, & Parker,
2004; Howell, 2014; Peterson, 2002),
to pursue questions about causality
and attribution.
• Balancing the roles of advocate and
researcher, especially related to the
honest and transparent communica-
tion of study limitations, the nuanced
and careful communication of causal
claims, and the ongoing debate
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
about the relative value of informal/
free-choice and formal learning
• Creating mechanisms to support
ongoing dialogue about study findings,
methods, and causal claims that
advance the field’s understanding of
attribution and approaches to studying
long-term impacts.
• Finding sufficient time and resources
to conduct this type of rigorous
research and implement programmatic
and educational strategies that reflect
complex ideas about attribution and
Implications and
How can informal/free-choice learning
scholars and institutions grapple
with the complexities and challenges
described above and make headway
in testing the effects of out-of-school
experiences on long-term learning
experiences? Affirming that it is no
longer enough for the community to
dismiss causality as an impossible
goal, the group discussed three strate-
gies for moving forward:
1) Focus and commit to values
and effects—As discussed more
extensively in other sections of
the workshop, a key challenge to
measuring long-term effects is defining
and operationalizing what effects or
outcomes should be prioritized. By
focusing and articulating priorities,
this group argued that institutions and
programs can greatly increase the
likelihood that educational strategies
will be designed to effectively achieve
outcomes and that the assessment of
those outcomes will be aligned with
the effects that the program or institu-
tion is best positioned to support.
2) Find methods for accounting for the
complexity of attribution—Because
of the variety of factors and complex-
ities potentially influencing how and
whether a particular learning experi-
ence will lead to long-term learning
outcomes, the group agreed that it is
important for researchers and educa-
tors to collaborate to find creative and
innovate methods for accounting for
this complexity when developing and
testing models of long-term effect.
For example, understanding the initial
motivations of visitors to a museum
can reveal how the outcomes of those
experiences differ by participant group
(Falk, 2009; Falk & Storksdieck, 2005).
This may be especially important when
the experience or program is relatively
brief and the long-term effects will
likely vary greatly across individuals
based on prior and subsequent expe-
riences. One way of thinking about this
strategy is measuring and accounting
for the “noise” within a learning system
in order to better identify the “signal”
showing the causal relationships
or pathways between an informal/
free-choice learning experience and
long-term learning outcomes.
3) Design initiatives and partnerships
to influence the complexity of attribu-
tion—A different approach to tackling
the complexity of attribution is to
design educational systems and part-
nerships that have a broader influence
on the many learning contexts and
experiences across a person’s life and
thus exert greater, more synergistic
influence on long-term learning effects
(e.g., Falk, et al., 2016b; Pattison et al.,
2017). This is aligned with the growing
emphasis within the field to think about
and support the learning ecologies of
children and adults and help learners
build on experiences across contexts
and institutions (National Research
Council, 2009, 2015). Beyond simply
account for the “noise” within the
system, this approach focuses on
designing support structures so that
the “noise” becomes part of the
“signal” that increases the likelihood of
achieving long-term impacts that can
be attributed to specific educational
programs and experiences.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
The purpose of researching longer-
term effects is usually to attempt to
understand intervention impacts and
outcomes that take time to take place.
Rather than measuring immediate
changes in knowledge, interest or
attitude, a longer-term investigation
usually focuses on the persistence of
these changes, and on any resulting
consequences, e.g., behaviors, of
these changes.
In shorter term program evaluation/
research, the metrics of outcomes
are very program-specific, which
often excludes findings related to
organizational mission, community
impacts, or individual outcomes not
defined in advance. The move to
outcome evaluation in the field, while a
significant step forward, has significant
shortcomings in that they:
• Make assumptions about accessi-
bility and equity, e.g., assuming that
everyone has the same access to an
experience and failing to acknowl-
edge issues of equity in determining
outcomes (For example: an equitable
assessment of graduation rates in
STEM would require acknowledging
that SES and parental support differ-
ences are as important in determining
outcomes as are what happens within
the K-12 grade system.)
• Require that “we” inside the field
disproportionately define what is
important to measure, usually in a
prescriptive manner, rather than in
collaborative way that recognizes indi-
vidual, cultural and community defined
needs and priorities.
• Provide a barrier to performing open
ended research focused on under-
standing what’s important to measure.
• Often skew towards the funders’
interests, which may or may not
necessarily be fully aligned with what
is deemed most valuable or interesting
by the program organizers or the
program’s participants.
Given these shortcomings, consider-
ably more thought needs to be given
to identifying and capturing not only
the intended goals of the institution
but equally the needs and goals of
the intended audiences, including and
specifically the likelihood that due to
cultural and experiential differences,
different audience constituencies may
have different needs and goals.
Accordingly, it is important to
consider that:
• Participant reflections on an experi-
ence overall may not be positive, but
there can still be positive gains/effects/
perceived moments of value;
• Some program participants can
be doing meaningful things that are
valuable to them, but the program/
intervention/research is disrupting what
they see as the best use of their time;
• In some investigations, and with
some outcomes (e.g., behavior,
interest), it will be important to make
room for/acknowledge that cultural
differences will likely effect outcomes;
Question 3: Accommodating Context
Judith Koke, Kate Livingston, Ali Mroczkowski, Rabiah Mayas
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
• The scientific enterprise has its own
set of discrete norms and cultures
which directly influence how research
is done even though there may situa-
tions and circumstances where it may
not always be appropriate for these
norms and cultures to be privileged;
• It is important that the field begin
moving towards more individualized
approaches to measuring impacts,
pathways, and learning; all while
maintaining high standards of research
validity and reliability;
• Project timelines impose a tremen-
dous constraint on validly measuring
long-term effects; particularly given the
significant time it takes to build trusted
relationships necessary for successful
research within many, particularly
underserved communities;
• Research models might want to
include within measures of effect how
educational experiences contribute
to the wellness of a community/
family (as defined by the learners, not
• Research designs/approaches
need to include measures that capture
multiple levels of effect—effects at the
level of the individual, the program and
the community (cf., NRC, 2015);
• Some long-term metrics need to
be open-ended so that learners them-
selves can have agency in defining
where and how impacts have occurred
in their lives; and finally
• The field still strongly privileges quan-
titative research over qualitative work,
yet investigating longer-term, cultural-
ly-specific individual effects are likely
to be most readily discernable through
data-rich, qualitative approaches.
Implications and
The group makes the following
suggestions for how to positively move
the field forward. There is a need for
the field to:
1. Place greater emphasis on reflexive
practice—identifying specific moments
in the process to review and modify
the research/evaluation process in
response to investigator learning
(cf., Michael Quinn Patton (2016)
Developmental Evaluation process).
2. Practice greater cultural humility. As
our field begins to increase attention
to and skill with culturally sensitive
research we need to move away from
prescriptive, linear approaches and
open-up the research process to be
more inclusive of its subjects. More
qualitative, open explorations of the
journey and the definition of benefits
from the participants’ perspectives
should be encouraged. We must
consider what an engaged, inclusive
meaningful role for the participants
could be. Can we involve subjects
in shaping the questions and/or in
responding to the interpretation of
3. Demonstrate greater professional
humility: Considering the increasing
perception that conducting long-term
investigations is a necessity rather
than a nicety, the research questions
inherent in valid and reliable long-
term investigations must drive the
methods—not the reverse.
However, we perceive significant
barriers or challenges to moving
informal science education research in
this direction, in particular:
1. Quantitative research is more highly
valued than qualitative research—yet
this work requires the inclusion of
qualitative approaches.
2. More inclusive approaches are very
time consuming—and hence more
3. Is our field actually open to change,
or will practitioners, funders and even
some researchers find it threatening?
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
This highly productive meeting clearly
identified some key challenges and
issues inherent in investigating the
long-term effects of informal science
education experiences. As summa-
rized below, it also generated some
basic recommendations for both
practice and policy.
1. Researchers designing long-term
studies should consider illustrating the
presumed learning process through
a hypothetical Slope of Effect. This
demonstration will work to help
determine when to measure outcomes
and, combined with power analysis,
will help to clarify how much data to
collect at each point along the time
2. It is important for researchers
and educators to collaborate to find
creative and innovate methods for
accounting for complexity when devel-
oping and testing models of long-term
impact. As well, researchers and
educators must define and operation-
alize what effects or outcomes are to
be prioritized thus increasing the likeli-
hood that educational strategies will be
designed to effectively achieve those
outcomes, and that the assessment
of those outcomes will be aligned with
the effects that the program or institu-
tion is best positioned to support
3. The field must confront the
complexity of attribution by designing
educational systems and partnerships
that have a broader influence on the
many learning contexts and experi-
ences across a person’s life and thus
exert greater, more synergistic influ-
ence on long-term learning impacts.
4. Researchers must identify specific
moments in the process to review and
modify the process in response to the
researcher’s own learning, including
newly identified cultural biases.
5. As the field builds improved
culturally sensitivity in its research
designs and practice, it will necessarily
move away from prescriptive, linear
approaches and, indeed, open the
research process up to be more inclu-
sive of its subjects.
These five recommendations represent
important first steps towards achieving
the goal of more validly and reliably
measuring the long-term, cascading
effects of any particular educational
experience. However, further thought
and experimentation on this topic are
clearly needed. Additional efforts might
• Using these initial findings as a
foundation for publications or presen-
tations, and as a vehicle for generating
further discussion;
• Encouraging other investigators to
add to these initial reviews and collec-
tively generate a more comprehensive
review of existing literature, with partic-
ular attention to identifying key gaps in
• Convening of a larger, follow-up
conference on this issue that includes
invitations to those performing long-
term research in health and wellness,
human development, and other rele-
vant social science areas; and
• The specific earmarking of funding
for support of efforts to validly and
reliably study long-term effects.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
We gratefully acknowledge that this work was supported in part by the National Science Foundation (Pathways Grant
Adelman, L.M., Falk, J.H., & James, S. (2000). Assessing
the National Aquarium in Baltimore’s impact on visitor’s
conservation knowledge, attitudes and behaviors.
Curator, 43(1), 33–62.
Ahmed, S. & Palermo, A-G. (2010). Community engagement
in research: Frameworks for education and peer review,
American Journal of Public Health, 100(8), 1380–1387.
Anderson, D., Lucas, K., Ginns, I., & Dierking, L. (2000).
Development of knowledge about electricity and
magnetism during a visit to a science museum and
related post-visit activities. Science Education, 84(5),
Anderson, D., Storksdieck, M. & Spock, M. (2006). Long-
term impacts of museum experiences. In J. Falk, L.
Dierking and S. Foutz (eds.) In Principle, In Practice,
(pp. 197–215), Lanham, MD: AltaMira Press.
Baer, J. M. (1988). Long-term effects of creativity
training with middle school students. The
Journal of Early Adolescence, 8(2), 183–193.
Baer, R. A., Rinaldo, J. C., & Berry, D. T. G (2003). Response
distortions in self-report assessment. In R. Fernandez-
Ballesteros (Ed.), Encyclopedia of psychological
assessment. London: Sage.
Barron, B. (2006). Interest and self-sustained learning
as catalysts of development: A learning ecology
perspective. Human Development, 49(4), 153–224.
Bathgate, M.E., Schunn, C.D. & Correnti, R. (2013).
Children’s motivation toward science across contexts,
Manner of interaction, and topic. Science Education,
97, 1–28.
Bell, P., Bricker, L., Reeve, S., Toomey Zimmerman, H.
& Tzou, C. (2013). Discovering and Supporting
Successful Learning Pathways of Youth In and Out
of School: Accounting for the Development of Everyday
Expertise Across Settings. LOST Opportunities,
Springer Netherlands, pp. 119–140.
Bransford, J.D., Brown, A.L., & Cocking, R.R. (Eds.) (2000).
How people learn. Washington, DC: National Research
Campbell, D. T., & Stanley, J. C. (1967). Experimental and
quasi-experimental designs for research (2nd ed.).
Boston, MA: Houghton Mifflin Company.
Chan, D. (2009). So why ask me? Are self-report data
really that bad? In C. E. Lance & R. J. Vandenberg
(Eds.), Statistical and methodological myths and
urban legends: Doctrine, verity and fable in the
organizational and social sciences (pp. 309–335).
New York, NY: Routledge.
Dehaene, S., Izard, V., Spelke, E., & Pica, P. (2008). Log
or linear? Distinct intuitions of the number scale
in Western and Amazonian indigene cultures.
Science, 320(5880), 1217–1220.
References and Resources
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Dumont, H., Istance, D. & Benavides, F. (Eds.) (2012).
How can the learning science inform the design of
21st century learning environments? Centre for
Educational Research and Innovation. OECD
ceri/50300814.pdf Retrieved May 14, 2018.
Duncan, T. E., & Duncan, S. C. (2009). The ABC’s of
LGM: an introductory guide to latent variable growth
curve modeling. Social and personality psychology
compass, 3(6), 979–991.
Eaglemen, D. (2015). The brain. New York: Pantheon.
Falk, J. H. (2009). Identity and the museum visitor
experience. Walnut Creek, CA: Left Coast Press.
Falk, J.H. (2018). Born to Choose: Evolution, self and
well-being. London: Routledge.
Falk, J. H., & Dierking, L. D. (1997). School field
trips: Assessing their long-term impact. Curator:
The Museum Journal, 40(3), 211–218. https://doi.
Falk, J. H., & Dierking, L. D. (2010). The 95 percent
solution: School is not where most Americans learn
most of their science. American Scientist, 98(6),
Falk, J.H. & Dierking, L.D. (2014). The Museum Experience
Revisited. Walnut Creek, CA: Left Coast Press.
Falk, J.H. & Dierking, L.D. (2018). Learning from Museums.
Lanham, MD: Rowman & Littlefield.
Falk, J. H., Dierking, L. D., & Foutz, S. (Eds.). (2007).
In principle, in practice: Museums as learning
institutions. Lanham, MD: AltaMira.
Falk, J.H., Dierking, L.D., Osborne, J., Wenger, M.,
Dawson, E. & Wong, B. (2015). Analyzing science
education in the U.K.: Taking a system-wide approach.
Science Education, 99(1), 145–173.
Falk, J. H., Dierking, L. D., Swanger, L. P., Staus, N.,
Back, M., Barriault, C., … Verheyden, P. (2016a).
Correlating science center use with adult science
literacy: An international, cross-institutional study.
Science Education, 100(5), 849–876. https://doi.
org/10.1002/ sce.21225
Falk, J. H., & Needham, M. D. (2013). Factors contributing
to adult knowledge of science and technology. Journal
of Research in Science Teaching, 50(4), 431–452.
Falk, J. H., Pattison, S. A., Meier, D., Bibas, D., & Livingston,
K. (2018). The contribution of science-rich resources
to public science interest. Journal of Research in
Science Teaching, 55(3), 422–445. https://doi.
Falk, J.H., Scott, C., Dierking, L.D., Rennie, L.J., & Cohen
Jones, M. (2004). Interactives and visitor learning.
Curator, 47(2), 171–198.
Falk, J. H., Staus, N., Dierking, L. D., Penuel, W.,
Wyld, J., & Bailey, D. (2016b). Understanding youth
STEM interest pathways within a single community:
The Synergies project. International Journal of Science
Education, Part B, 6(4), 369–384.
Falk, J. H., & Storksdieck, M. (2005). Using the contextual
model of learning to understand visitor learning from
a science center exhibition. Science Education, 89(5),
Farinde, A. (2017). Dose-Response Relationships.
Flagg, B. (2005). Beyond entertainment: Educational
impact of films and companion materials. Big Frame,
22 (2), 50–56.
Fivush, R., McDermott Sales, J., Goldberg, A., Bahrick, L.,
& Parker, J. (2004). Weathering the storm: Children’s
long-term recall of Hurricane Andrew. Memory,
12(1), 104–118.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Fraser, J., Heimlich, J.E., Jacobson, J., Yocco, V., Sickler,
J., Kisiel, J., Nucci, M., Ford Jones, L. & Stahl, J.
(2012). Giant screen film and science learning in
museums. Museum Management and Curatorship,
27(2), 179–195.
Fu, A. C., Kannan, A., Shavelson, R. J., Peterson, L.,
& Kurpius, A. (2016). Room for rigor: Designs and
methods in informal science education evaluation
Visitor Studies, 19(1), 12–38.
Gates, E., & Dyson, L. (2017). Implications of the
changing conversation about causality for evaluators.
American Journal of Evaluation, 38(1), 29–46. https://
Gonyea, R. M. (2005). Survey research: Emerging issues.
New Directions for Institutional Research, 2, 73–89.
Groves, R. (1989). Survey costs and survey errors.
New York: John Wiley.
Howell, R. A. (2014). Investigating the long-term impacts
of climate change communications on individuals’
attitudes and behavior. Environment and Behavior,
46(1), 70–101.
Isiordia, M., & Ferrer, E. (2018). Curve of factors model:
a latent growth modeling approach for educational
research. Educational and Psychological Measurement,
78(2), 203–231.
Ito, M., Baumer, S., Bittanti, M., Boyd, D., Cody, R.,
Herr-Stephenson, B., Horst, H.A., Lange, P.G.,
Mahendran, D., Martinez, K.Z., Pascoe, C., Perkel,
D., Robinson, L., Sims, C. & Tripp, L. (2013). Hanging
out, messing around, and geeking out: Kids living and
learning with new media. Cambridge, MA: MIT Press.
Lavrakas, P.J. (Ed.). (2008). Encyclopedia of survey research
methods. Thousand Oaks, CA: Sage.
Lemke, J. L., Lecusay, R., Cole, M., & Michalchik, V. (2015).
Documenting and assessing learning in informal and
media-rich environments. Retrieved from http://
McArdle, J. J., & Epstein, D. (1987). Latent growth
curves within developmental structural equation
models. Child development, 110–133.
National Research Council. (2009). Learning science in
informal environments: People, places, and pursuits.
Washington, DC: National Academies Press.
National Research Council. (2015). Identifying and
supporting productive STEM programs in out-of-
school settings. Washington, DC: The National
Academies Press.
Pattison, S. A., & Shagott, T. (2015). Participant reactivity in
museum research: The effect of cueing visitors at an
interactive exhibit. Visitor Studies, 18(2), 214–232.
Pattison, S. A., Svarovsky, G. N., Gontan, I., Corrie, P.,
Benne, M., Weiss, S., … Ramos-Montañez, S.
(2017). Teachers, informal STEM educators, and
learning researchers collaborating to engage
low-income families with engineering. Connected
Science Learning, 4. Retrieved from http://csl.nsta.
Peterman, K., Pressman, E. & Goodman, I.F. (2007).
NOVA science Now science cafés evaluation.
Unpublished Technical Report. NY: Goodman
Research Group.
Peterson, C. (2002). Children’s long-term memory for
autobiographical events. Developmental Review,
22(3), 370–402.
Quinn Patten, M. (2016). Developmental Evaluation.
New York: Guilford Press.
Roschelle, J. (1995). Learning in interactive environments:
Prior knowledge and new experience. In: J. Falk &
L. Dierking (eds.) Public Institutions for Personal
Learning (pp. 37–51). Washington, DC: American
Association of Museums.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001).
Experimental and quasi-experimental designs for
generalized causal inference. Boston, MA:
Houghton Mifflin.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006).
Career choice: Planning early for careers in science.
Science, 312(5777), 1143–1144.
Taplan, S. (2005). Methodological design issues in
longitudinal studies of children and young people in
out-of-home care: A literature review. Technical Report.
NSW Centre for Parenting & Research. Sydney,
Australia: NSW Department of Community Services.
Vaske, J. J. (2008). Survey research and analysis:
Applications in parks, recreation and human
dimensions. State College, PA: Venture.
Collins et al., 2018 Community Based Research Values
and Principles (suggestions for creating an environment
for meaningful community involvement in all stages of
research process).
Harvard Adult Development Study Retrieved September 25, 2018.
Lavery, J.V. et al. (2010). Towards a framework for
community engagement in global health research
(PubMed link:
pubmed/20299285) This is a public health research
opinion in the Journal of Parasitology that outlines key
practices in effective community engagement RE:
health research, including involvement in developing
research goals and paying attention to changes in
populations over time. PDF link: https://www.dropbox.
com/s/n22a7pi8qcp5i76/ PIIS1471492210000425.
Lincoln, Y.S. & Guba, E.G. (1985) Naturalistic Inquiry.
Newbury Park, CA: Sage Publications.
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Appendix A: Participants
Lynn Dierking is Director of Strategy & Partnerships,
Institute for Learning Innovation, and Professor, Free-
Choice Learning, Oregon State University. Her research on
lifelong, out-of-school learning (after-school, home- and
community-based contexts), with youth and families,
focuses primarily on youth/families living in poverty and/or
not historically engaged in free-choice learning from cultural
institutions/organizations. Dr. Dierking is PI of a US-NSF
project, SYNERGIES: Customizing Interventions to Sustain
Youth STEM Interest and Participation Pathways, studying
youths’ STEM interest and participation longitudinally in
an under-resourced community. She also is co-PI of a
US-NSF/UK-Wellcome Trust Science Learning+ Partnership
project, Partnering for ‘Equitable STEM Pathways’ for Youth
Underrepresented in STEM. She is on Editorial Boards for
Connected Science Learning, Afterschool Matters and
Journal of Museum Management and Curatorship.
John Falk is Director of the Institute for Learning Innovation
and Emeritus Sea Grant Professor of Free-Choice Learning
at Oregon State University. He is a leading expert on
free-choice learning; the learning that occurs when people
have significant choice and control over the what, where
and when of their learning. His current research focuses
on understanding the identity/self-related reasons people
utilize free-choice learning settings during their leisure time;
studying the community impacts of museums, libraries,
zoos and aquariums, measuring the long-term interest
pathways of youth and helping cultural institutions re-think
their educational positioning in the 21st century.
Gail Jones has a PhD in Science Education from NC
State University. Dr. Jones currently serves as Alumni
Distinguished Graduate Professor of Science Education
and a Fellow at the Friday Institute for Educational Innovation
teaching preservice and in-service teachers and conducting
research on virtual reality, nanotechnology and family
learning in out-of-school contexts. Dr. Jones’ research has
been recognized by the National Association for Research
in Science Teaching, The NC Association of Research
in Education, and the Association of Supervision and
Curriculum Development. Dr. Jones has authored several
books for teachers: Nanoscale Science, Extreme Science,
and Case Studies in Biology and Engineering (in press).
Dr. Jones’ research group is currently researching new
forms of technology for teaching science and strategies to
enhance science capital and family habitus for science.
Jim Kisiel is Associate Professor of Science Education
at California State University, Long Beach. Much of his
research has examined the juxtaposition of formal and
informal environments, examining the opportunities and
constraints in collaborative activities ranging from field trips
to more formal school-museum partnerships. He has also
conducted a variety of research and evaluation studies
examining different learners in informal contexts. These
include clarifying adult museum-goers’ understanding of
science, identifying family learning behaviors, and examining
science identity development.
Judith Koke is Director, Professional Learning at the
Institute for Learning Innovation where she leads the
Institutes efforts to research, innovate and disseminate
effective methods to engage professionals in learning and
capacity building, particularly with in regard to integrating
research into practice. Previously as Senior Research
Associate at the Institute, she has a long history of research
and evaluation in the free-choice learning field. She has
also worked in senior leadership roles at the Art Gallery of
Ontario and The Nelson-Atkins Art Museum. With a career
spent in both research and museum leadership, she
understands how to assess and apply research findings
into better practice.
Kate Livingston is the founder and Principal at
ExposeYourMuseum LLC (Detroit, MI), a consulting
firm supporting arts and cultural organizations to better
understand their internal climate and culture, current and
prospective audiences, and role and potential in their
communities. Kate has 15+ years of experience designing,
developing, and executing professional research and
evaluation and 10+ years of experience designing and
implementing strategic, master, and interpretive plans.
Her approach prioritizes making connections, facilitating
conversations, elevating communities, engaging creatively,
and strong, clear communication to inspire innovation,
inform strategy, and drive decision-making. Kate is
committed to inclusion, anti-racism, and social justice
work, and these principles are central to her work alongside
museums. From 2007-2013, Kate led the department
of Audience Insights at the Denver Museum of Nature
& Science (DMNS).
Oregon State University | Museum of Science and Industry Chicago | Institute for Learning Innovation
Rabiah Mayas is the Associate Director of Science in
Society at Northwestern University, where she leads the
development, implementation and evaluation of K-12
STEM education programs and partnerships in Chicago
and Evanston. Key areas of focus include afterschool
STEM mentoring for middle-grade youth, training of STEM
graduate students in community engagement, and NGSS
professional development for CPS high school teachers.
Prior to joining Northwestern in 2017, Rabiah was the
Director of Science and Integrated Strategies at the
Museum of Science and Industry, Chicago. Key program
areas developed or expanded under her leadership include
maker-based learning experiences, public programs of the
Black Creativity initiative, and evaluation and science learning
research. Rabiah completed her Ph.D. in biochemistry and
molecular biology at the University of Chicago.
Ali Mroczkowski is a Researcher and Project Manager at
the Museum of Science and Industry, Chicago. She earned
her Ph.D. in Community Psychology from DePaul University
in 2017. Her research is on the educational experiences of
marginalized youth. Recently, her research has focused on
the role of out-of-school time programs in supporting the
educational and career development of youth.
Scott Pattison is a researcher and evaluator at TERC,
formerly the Institute for Learning Innovation. Over the last
15 years, his work has focused on education, learning,
and interest development in free-choice and out-of-school
environments, including museums, science centers, and
everyday settings. Dr. Pattison specializes in using qualitative
and quantitative methods to investigate the processes
and mechanisms of learning in naturalistic settings. He is
committed to addressing issues of equity and inclusion in
education and has partnered with organizations across the
country to support learning for diverse communities.
Aaron Price is the Director of Research and Evaluation
at the Museum of Science and Industry, Chicago (MSI). His
team of six studies the Museum’s impact on guests and
the community. He earned his Ph.D. in science education
(learning sciences) after working for 14 years at an astro-
nomical citizen science organization.
Robert Tai is an Associate Professor of Science Education
at the University of Virginia in the Department of Curriculum,
Instruction, and Special Education. Prior to joining the
faculty at the University of Virginia, Dr. Tai taught high school
physics in Illinois and then Texas. He has served as both
a research associate and teaching fellow in the Graduate
School of Education at Harvard University. Dr. Tai is fully
involved in several grant funded research projects, the
supervision and mentoring of doctoral students, and the
production and dissemination of science education research.
... A second consideration is the dilemma of ecological validity versus experimental control. Comparing two families that visit the science museum at the same time, one engaging in an unstructured visit and the other participating in the study, these families have differences in freedom of choice regarding exhibit choice, time spent and social context that impact the ability to generalize research findings (Bamberger & Tal, 2007;Falk et al., 2018). In the current design we tried to optimize the participants' free choice within the measurement constraints (especially active consent for videotaping; Pattison et al., 2019). ...
... Engagement time is very sensitive to a research setting (in comparison to a completely free setting; Bamberger & Tal, 2007;Falk et al., 2018). Hence, we were not convinced that engagement time in our setting (the constraints that go together with recording video) would be a measure with strong external validity. ...
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Research on the impact of guided investigation, including verbal guidance strategies, has expanded in the recent decade, and the current work contributes to this line of research in a museum context. The current in‐depth study examines the impact of verbal guidance strategies on family learning at an open‐ended museum exhibit, considering participants' individual characteristics. The experiments and conversations of 104 families were observed at an object motion exhibit. Results show that families, with and without guidance, investigated in a meaningful way by performing control‐of‐variables strategy experiments, investigating a range of variables, and formulating hypotheses and causal explanations. However, the results also show that the process of learning scientific concepts could be improved. Minimal interventions of museum educators positively affected the families' learning process by reducing the number of scientifically incorrect remarks. Interestingly, in addition to discussing the phenomenon, especially the families with highly educated parents discussed the topic of reliability of their experiments, which is an under‐investigated aspect of learning through investigation. Only children's cognitive ability and not parental education was modestly, but consistently related to the families' performance.
... Despite the long-term, cumulative nature of informal science learning, most ISL research to date has been limited in both duration and scope, designed to document the results of short-term experiences such as the learning outcomes from a single science center visit or similar event, rather than documenting ISL pathways over time (National Research Council, 2015). While such research has provided valuable insights into what and how people learn during ISL experiences, it does not adequately capture the accumulation of knowledge and understandings derived from multiple sources over time both prior and subsequent to any particular educational event (Bransford, Brown, & Cocking, 2000;Falk, Koke, Price, & Pattison, 2018). ...
... However, these efforts have been significantly hampered by several methodological challenges related to the cumulative and personally unique nature of ISL learning that makes it difficult to study over significant periods of time. For example, unlike long-term studies in formal school-based environments where the curriculum is the same for all students, informal science learning takes place outside school during everyday activities and in designed spaces and programs such that learning pathways are unique to each individual, making it difficult to attribute learning outcomes to particular events or experiences (Falk, Koke, et al., 2018). In addition, while attendance is compulsory in formal spaces such as classrooms, ISL learners choose what, when and how long to participate in programs and other activities, making it difficult to follow individuals over time and increasing the chance of potential measurement issues due to attrition (Taplan, 2005). ...
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Despite the fact that most science learning takes place outside of school, little is known about how engagement in informal science learning (ISL) experiences affects learners’ knowledge, skill development, interest, or identities over long periods of time. Although substantial ISL research has documented short-term outcomes such as the learning that takes place during a science center visit, research suggests that the genuine benefits of informal experiences are long-term transformations in learners as they pursue a “cascade” of experiences subsequent to the initial educational event. However, a number of major methodological challenges have limited longitudinal research projects investigating the long-term effects of ISL experiences. In this paper we identify and address four key issues surrounding the critical but challenging area of how to study and measure the long-term effects or impacts of ISL experiences: attribution, attrition, data collection, and analytic approaches. Our objective is to provide guidance to ISL researchers wishing to engage in long-term investigations of learner outcomes and to begin a dialogue about how best to address the numerous challenges involved in this work.
... For example, it is very challenging to randomly assign who visits museums and who does not, or even who visits an exhibit within a museum and who does not. Given that the choice to visit and the freedom to individualize the museum experience are fundamental characteristics of these settings (Falk & Dierking, 2013), attempting to control this dynamic can undermine the external validity of the research (Falk, Koke, Price, & Pattison, 2018). Instead, researchers often must be creative to work within these and other constraints to eliminate as many alternative explanations of later observed differences as possible while still attending to the informal and free-choice nature of the environments they are a part of. ...
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Identifying causal relationships is an important aspect of research and evaluation in visitor studies, such as making claims about the learning outcomes of a program or exhibit. Experimental and quasi- experimental approaches are powerful tools for addressing these causal questions. However, these designs are arguably underused in visitor studies. In this article, we offer examples of the use of experimental and quasi-experimental designs in science museums to aide investigators interested in expanding their methods toolkit and increasing their ability to make strong causal claims about programmatic experiences or relationships among variables. Using three designs from recent research (fully randomized experiment, posttest only quasi-experimental design with comparison condition, and posttest with independent pretest design), we discuss challenges and tradeoffs related to feasibility, participant experience, alignment with research questions, and internal and external validity. We end the article with broader reflections on the role of experimental and quasi-experimental designs in visitor studies.
Aunque por diversas causas los Museos y Centros de Ciencias (MCC) no han cumplido a cabalidad su esperada misión de generar una cultura científica en todas las poblaciones, si han permitido describir el proceso de aprendizaje informal, que es de carácter personal, voluntario e idiosincrásico, y que equivaldría a la apropiación de los mensajes emitidos por los numerosos productos para la Comunicación Pública de la Ciencia (CPC). La educación informal en ciencias, puede o no tener la intencionalidad de producir dicho aprendizaje informal, pero en cualquier caso, se vislumbra como una posibilidad de mantener a la sociedad enterada de los avances de la ciencia y en contacto con ella.
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Free-choice learning environments, such as museums, national parks, interpretive trails, and visitor centers, are trusted sources of information in their communities and support lifelong learning. Earthquake education in these spaces creates awareness of earthquake hazards and risk in areas where people live or visit and, in turn, may increase engagement in preparedness behavior. The ShakeAlert® Earthquake Early Warning System helps publics prepare by warning in advance of shaking from significant earthquakes along the West Coast of the United States. ShakeAlert can minimize earthquake damage by prompting automated actions (e.g., slowing trains, shutting off water valves) and prompting personal protective actions like “Drop, Cover, and Hold On” to significantly reduce damage, injury, and loss of life. Individuals and communities must have a basic understanding of earthquake hazards, as well as an awareness of ShakeAlert technology, to know how to respond if they feel shaking or receive an alert. Currently, there is a lack of contemporary scholarship on how free-choice learning environments approach earthquake education through exhibits and displays. We analyzed a sample of existing earthquake exhibits and their themes in the United States and explored how different display types are uniquely engaging. We found that most displays did not include information about how to prepare for an earthquake or associated protective actions. From this and the development of the typology, our research posits a foundational framework for how best to incorporate place-based learning on earthquakes and early warning into centers of free-choice learning which may apply to a range of other natural hazards and will enhance public awareness and safety.
This study looks at the types of awe guests feel when they leave art and science cultural institutions of various sizes and context, and how it may be related to what they remember learning. We surveyed 899 guests at the end of their visit and 550 of them again about one week later. Measures included a scale of awe-related perceptions (both positive and negative) along with questions about memories guests have about what they learned during their visit. Results show awe-related perceptions were consistent across institutions with only one significant difference, even when grouped by context (art vs. science). Guests’ memories of profound educational and emotional experiences were weakly related to the amount and types of awe they felt. This connection was strongest with memories of emotional connections and being surprised. We also found connections to social experiences and that prior knowledge was a strong, consistent predictor of positive awe.
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Throughout their lives, children from low socioeconomic backgrounds and traditionally underserved and under-resourced communities face significant barriers to engaging with engineering and science (Gershenson 2013; Orr, Ramirez, and Ohland 2011). Supporting learning and interest development in early childhood, and engaging families, preschools, and community stakeholders in this process, is a powerful way to address these issues and a critical economic investment to ensure children become productive members of their communities (Garcia et al. 2016). Although not often a focus of early childhood education, engineering and problem-solving skills are increasingly recognized as essential for children and adults to thrive in today’s society and are a growing part of the K–12 curriculum (NGSS Lead States 2013). Introducing families to the topic of engineering and sparking young children’s interest in planning, building, testing, and problem solving helps prepare them for school and life. In this article, we introduce the Head Start on Engineering (HSE) project, which was designed to address these challenges through a multicomponent, family engineering engagement program integrated with the Head Start model. Head Start is a federal program within the U.S. Department of Health and Human Services that promotes school readiness for young children (birth to age five) from low-income families through child care centers and home visiting programs in communities across the country. HSE fosters connections across preschool and family learning contexts to catalyze long-term, enduring family interests related to science and engineering—or what are often called long-term interest pathways.
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This preliminary study examined the effect that five major sources of public science education—schools, science centers, broadcast media, print media, and the Internet—had on adults’ science interest values and cognitive predispositions. Over 3,000 adults were sampled in three United States metropolitan areas: Los Angeles, California, Phoenix, Arizona, and Philadelphia, Pennsylvania. To minimize potential sampling bias, the results were weighted by current United States Census data to be comparable to demographics from each of the three jurisdictions. Participants were asked to self-report their current and early adolescent usage of these five science-related resources, the quality of their experiences with each, and their current abilities, values and cognitive predispositions relative to science. Data showed that overall, a broad cross-section of adults living in these cities engaged in a wide array of science-related activities and that large majorities did so frequently. Nearly two-thirds of all respondents self-reported currently participating in some kind of science-related activity every week and nearly half doing so daily. Results suggested that having frequent, positive science-related experiences in- and out-of-school, both early and later in life, correlated with having a strong interest in and positive perception of science as an adult. Although a diversity of positive science-related experiences correlated with current adult science interest values and cognitive predispositions, only five factors uniquely and significantly predicted adult science interest, values, and cognitive predispositions in the multivariate models: (i) early adolescent experiences visiting a science center; (ii) early adolescent experiences watching science-related television; (iii) adult visits to a science center; (iv) adults reading books and magazines about science; and (v) adults using the internet to learn more about science. Discussed are issues of self-selection, quality of experiences, and the complex and synergistic nature of the science learning ecosystem. © 2017 Wiley Periodicals, Inc. J Res Sci Teach 9999:XX–XX, 2017
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A first-order latent growth model assesses change in an unobserved construct from a single score and is commonly used across different domains of educational research. However, examining change using a set of multiple response scores (e.g., scale items) affords researchers several methodological benefits not possible when using a single score. A curve of factors (CUFFS) model assesses change in a construct from multiple response scores but its use in the social sciences has been limited. In this article, we advocate the CUFFS for analyzing a construct’s latent trajectory over time, with an emphasis on applying this model to educational research. First, we present a review of longitudinal factorial invariance, a condition necessary for ensuring that the measured construct is the same across time points. Next, we introduce the CUFFS model, followed by an illustration of testing factorial invariance and specifying a univariate and a bivariate CUFFS model to longitudinal data. To facilitate implementation, we include syntax for specifying these statistical methods using the free statistical software R.
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The authors examine methodological characteristics of summative evaluations in informal science education (ISE), asking: What are the major types of designs used in summative evaluations, and what kinds of questions can they answer? What are the types of data collection methods and measures used, and how many are self-reports or direct measures? They reviewed all summative reports from the year 2012 on, the online resources portal from the Center for Advancement of Informal Science Education. They found reliance on nonexperimental evaluation designs and heavy use of self-report instruments. If a primary function of summative evaluations is to assess impact, and impact is a causal question, then these findings are problematic; the field needs to move beyond the mostly descriptive studies found in the sample. Interviews with nine leaders in ISE and ISE evaluation help explain evaluation challenges in ISE and generate ideas for advancing the field.
Born to Choose is John H. Falk’s compelling account of why and how we make the endless set of choices we do, every second of every day of our lives. Synthesizing research from across the biological and social sciences, Falk argues that human choice-making is an evolutionarily ancient and complex process. He suggests that all our choices are influenced by very basic and early evolving needs, and that ultimately each choice is designed to support survival in the guise of perceived well-being. This engaging book breaks new intellectual ground and enhances our understanding not just of human choice-making but human behavior overall.
This international investigation was designed to determine if, and under what circumstances experiences at science centers, significantly correlated with a range of adult general public science and technology literacy measures. Given the complex and cumulative nature of science and technology learning, and the highly variable and free-choice nature of science center experiences, an epidemiological research approach was used. Quantitative surveys were administered to 6,089 adults living in 17 communities located in 13 countries; all with active science centers. Data collection and analysis protocols ensured a representative sampling based on age, education, and income from each of the 17 participating communities. Results showed that individuals who used science centers had significantly higher understanding, interest and curiosity, participation in free-choice leisure activities, and identity relative to science and technology than did individuals who did not visit; even when potential self-selection biases such as income, education level, and prior interest were taken into consideration. These findings significantly strengthen the argument that the presence of one or more healthy and active science centers within a community, region, or country represents a vital investment for fostering and maintaining a scientifically and technologically informed, engaged, and literate public.
Making causal claims is central to evaluation practice because we want to know the effects of a program, project, or policy. In the past decade, the conversation about establishing causal claims has become prominent (and problematic). In response to this changing conversation about causality, we argue that evaluators need to take up some new ways of thinking about and examining causal claims in their practices, including (1) being responsive to the situation and intervention, (2) building relevant and defensible causal arguments, (3) being literate in multiple ways of thinking about causality, (4) being familiar with a range of causal designs and methods, (5) layering theories to explain causality at multiple levels; and (6) justifying the causal approach taken to multiple audiences. Drawing on recent literature, we discuss why and how evaluators can take up each of these ideas in practice. We conclude with considerations for evaluator training and future research.