Motivating Students’ STEM Learning Using Biographical Information

Abstract

Science instruction has focused on teaching students scientific content knowledge and problem-solving skills. However, even the best content instruction does not guarantee improved learning, as students’ motivation ultimately determines whether or not they will take advantage of the content. The goal of our instruction is to address the “leaky STEM pipeline” problem and retain more students in STEM fields. We designed a struggle-oriented instruction that tells stories about how even the greatest scientists struggled and failed prior to their discoveries. We describe how we have gone about designing this instruction to increase students’ motivation and better prepare them to interact and engage with content knowledge. We first discuss why we took this struggle-oriented approach to instruction by delineating the limitations of content-focused science instruction, especially from a motivational standpoint. Second, we detail how we designed and implemented this instruction in schools, outlining the factors that influenced our decisions under specific situational constraints. Finally, we discuss implications for future designers interested in utilizing this approach to instruction.

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MOTIVATING STUDENTS’ STEM LEARNING USING
BIOGRAPHICAL INFORMATION
Janet N. Ahn1, Myra Luna-Lucero1, Marianna Lamnina1, Miriam Nightingale2, Daniel Novak2, & Xiaodong Lin-Siegler1
1Columbia University; 2Columbia Secondary School for Math, Science, and Engineering
Science instruction has focused on teaching students
scientific content knowledge and problem-solving skills.
However, even the best content instruction does not guar-
antee improved learning, as students’ motivation ultimately
determines whether or not they will take advantage of the
content. The goal of our instruction is to address the “leaky
STEM pipeline” problem and retain more students in STEM
fields. We designed a struggle-oriented instruction that tells
stories about how even the greatest scientists struggled and
failed prior to their discoveries. We describe how we have
gone about designing this instruction to increase students’
motivation and better prepare them to interact and engage
with content knowledge. We first discuss why we took this
struggle-oriented approach to instruction by delineating the
limitations of content-focused science instruction, especially
from a motivational standpoint. Second, we detail how
we designed and implemented this instruction in schools,
outlining the factors that influenced our decisions under
specific situational constraints. Finally, we discuss implica-
tions for future designers interested in utilizing this approach
to instruction.
Janet N. Ahn is a postdoctoral research scientist in the department
of Human Development at Teachers College, Columbia University.
She studies motivation and goal pursuit.
Myra Luna-Lucero is a doctoral candidate in Math, Science, &
Technology at Teachers College, Columbia University. She studies
motivation and technology in STEM.
Marianna Lamnina is a Ph.D. student in Cognitive Studies at
Teachers College, Columbia University. She studies motivation and
transfer in STEM.
Miriam Nightingale is the Principal of Columbia Secondary School.
Daniel Novak is the Vice Principal of Columbia Secondary School.
Xiaodong Lin-Siegler is faculty in the department of Human
Development, Teachers College, Columbia University. She studies
motivation, instruction and STEM learning.
CREATING STORIES ABOUT SCIENTISTS’
STRUGGLES TO MOTIVATE STEM LEARNING
For decades, science instruction has focused almost exclu-
sively on teaching content. For instance, typical science
instruction teaches content, such as the structure of the
atom or the DNA molecule, as well as the scientific methods
or process that deduced protons and electrons and the data
that generated the double helix model. The goal of science
instruction that involves both the content and process is to
help students engage in scientific activities similar to the
work of a scientist in the field (Bell, Bricker, Tzou, Lee, and Van
Horne, 2012; The Next Generation Science Standards [NGSS],
2013; National Research Council [NRC], 2000). The ultimate
goal of our instruction is to address the “leaky STEM pipeline”
problem and retain more students in STEM fields.
There is no doubt that content-driven instruction is import-
ant for students to learn. However, even the best content
instruction does not guarantee that students will deeply
engage with the material. Instead, students’ motivation
ultimately prepares them to better interact with content
knowledge to improve their learning (Hong & Lin-Siegler,
2012; Lin-Siegler, Ahn, Chen, Fang, & Luna-Lucero, in press).
It is especially important to consider students’ motivation in
science, technology, engineering, and mathematics (STEM)
subjects because these subjects, in particular, are viewed as
challenging where exceptional talent is required for success.
In our recent interviews with high school students, all but
one student reported that pursuing futures in STEM is unlike-
ly because it is “too hard” or “only smart people do it.” Holding
such beliefs that high-level scientific performance requires
exceptional inborn ability is de-motivating and undermines
effort when it is most needed (Bandura, 1977, 1986, 1988;
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2016 | Volume 7, Issue 1 | Pages XX-XX

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 2
Dweck, 2000; Hong & Lin-Siegler, 2012; Murphy & Dweck,
2010; Pintrich, 2003; Rattan, Savani, Naidu & Dweck, 2012;
Stipek & Gralinski, 1996). As illustrated in Figure 1, “The STEM
Pipeline” is leaking, and it is not unreasonable to speculate
that we are losing many potential STEM majors due to these
de-motivating beliefs.
In this paper, we discuss how we have gone about designing
a story-based instruction that presents scientists as ordinary
people with limitations who struggled to achieve prior to
their scientific discoveries. We provide information about
how scientists’ values, motives, personalities, and life experi-
ences led them to sustain their effort through struggles. The
goal was to challenge students’ beliefs that unusually smart
people created scientific knowledge.
This paper is organized into four sections. The first section
describes the theoretical rationale for the approach we take
to design our instruction. We designed our instruction to
provide stories of how accomplished scientists (e.g., Albert
Einstein, Marie Curie, and Michael Faraday) struggled and
overcame challenges in their scientific endeavors. Our goal
was to confront students’ beliefs that scientific achievement
reflects ability rather than effort. This struggle-oriented
instructional approach is very much in the spirit of the
self-determination aspect of motivation theory suggesting
that basic psychological needs (e.g., needs for relatedness,
competence, and autonomy) must be met in order to
be motivated (Deci & Ryan, 1985; Ryan & Deci, 2000). The
second section narrates how we applied a user-centered
approach to design and implement this instruction in three
iterations. The first iteration describes scientists’ struggles
generally. The second iteration describes a procedural and
interactive approach to our instruction so that students can
better apply the message (that success requires struggles)
into their science learning. The third and last iteration de-
scribes a similar approach as the second iteration (procedural
and interactive) and additionally allows students to directly
experience the benefits of persisting through struggles. In
the concluding section, we summarize our instruction with
five design principles to support STEM learning for future
designers.
Theoretical Rationale for a Struggle-Oriented
Instructional Approach
Learning about science content knowledge and methods is
important but can be a depersonalized approach to science
(Eshach, 2009; Kubli, 1999; Solbes & Traver, 2003; for more
on content-based instruction see Amos, & Boohan, 2002;
Bennett, 2005; Sutman & Bruce, 1992). Depersonalized sci-
ence is less attractive to students because it is often devoid
of human endeavors, everyday contexts, and inflexible
in study routines (Cawthorn & Rowell, 1978). This lack of
“human” content in science teaching has several limitations.
According to self-determination theory, basic human
psychological needs must be met in order to foster self-mo-
tivation so people can persist longer on tasks, apply more
self-regulated learning strategies, exhibit higher intrinsic
motivation, and perform better despite adversity (Deci &
Ryan, 1985; Ryan & Deci, 2000). These needs are defined as
needs for relatedness (Baumeister & Leary, 1995; Reis, 1994),
competence (Harter, 1978; White, 1963), and autonomy (De
The STEM Pipeline
FIGURE 1. An illustration of the “leaking” STEM pipeline showing how more and more students do not end up pursuing STEM careers
despite initial interest. Reprinted from Engage to excel: Producing one million additional college graduates with degrees in science,
technology, engineering, and mathematics, Table C-6, The STEM pipeline [Online image], (2012). Retrieved from http://www.achieve.org.
Copyright [2012] Washington, DC: President’s Council of Advisors on Science and Technology. Reprinted with permission.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 3
Charms, 1968; Deci, 1976). From a motivational standpoint,
a depersonalized approach to science learning forestalls
natural processes of self-motivation, which is essential to
improve in science learning (and learning in general).
In the following section, we detail the limitations of a de-
personalized approach to science learning and explain how
providing scientists’ struggles addressed these limitations by
nurturing these basic human needs.
Limitations of Depersonalized Science Instruction
Stereotypes of scientists
Depersonalized instruction may lead students to develop
stereotypical images of science and scientists. Students view
scientists as unusually smart people who are divorced from
reality, since they are disinclined to pursue mundane things,
and instead prefer to pursue scientific wonders and esoteric
knowledge that only a chosen few could comprehend
(Chambers, 1983; Good, Rattan & Dweck, 2012; Mead &
Metraux, 1957; Ward, 1977). As illustrated in Figure 2, when
students believe that scientists are always smart people or
geniuses who use little effort to solve scientific problems,
they are more likely to perceive their failure as an indication
of their lack of exceptional talent to do well in science
(Dweck, 2010a, 2010b; Gladwell, 2008; Hong & Lin-Siegler,
2012).
Holding such stereotypical views of scientists perpetuates
the disconnect that we observe between adolescent stu-
dents and their understanding of scientists’ work. Research
has already shown that when people are viewed as very
dissimilar from the self and common ground cannot be
established, one tends to not associate with those dissimilar
others (Tajfel & Turner, 1979). In fact, when relatedness to
others is not felt, one distances from dissimilar others, even
derogating and antagonizing them (Dovidio, 2001; Gaertner,
Mann, Murrell, & Dovidio, 1989). Thus, depersonalized science
instruction often fails to engage and motivate students in
deep learning of the content.
In contrast, telling stories about how scientists struggled
and even failed during their process of experimental work
levers felt connectedness between students and scientists.
Using this connection as a lever can lead to improvement in
students’ feelings of relatedness with the scientists, which in
turn benefit their motivation to persist in their own studies
and overcome hurdles when they occur (Hong & Lin-Siegler,
2012, Lin-Siegler et al., in press).
Lacking scientific procedural knowledge
Another limitation of depersonalized science instruction
is that it conveys a static view of scientific discovery as an
outcome, rather than a dynamic process where humans
struggle to overcome obstacles prior to achieving their
goals. Students in schools often work with declarative
knowledge, or factual information about a specific domain.
In order to apply the factual information, students need to
learn procedural knowledge, or knowledge about how to
do something. For instance, we can teach people the theory
behind driving a car without actually showing them how
to drive one. Such an approach does not guarantee that
anyone will learn how to drive a car because truly knowing
involves seeing and practicing. In parallel to science learn-
ing, depersonalized science underemphasizes procedural
knowledge (Anderson, 1990, 2013, 2014).
When students believe that science does not involve an
active and dynamic process, this belief bolsters the idea that
they are not competent enough to skillfully master chal-
lenges in their environment (Deci & Ryan, 1985; Ryan & Deci,
2000), especially when students fail. That is, when students
fail or encounter challenges in science and hold onto the
belief that science does not involve a process of struggling,
they might be prone to think that their struggling is indica-
tive of their lack of competence. And, when an individual’s
competence is undermined, he/she is less likely to engage in
actions in pursuit of the desired outcome, and even if he/she
does, he/she will not invest 100% effort and persist (Dweck
& Leggett, 1988; Oyserman, Bybee, & Terry, 2006). Learning
about scientists’ struggles makes explicit the process of
scientific discovery, which counteracts students’ beliefs that
they are not competent because they have to work hard
when solving problems.
Only
geniuses
can do
science
Geniuses
do not
need to
work hard
If you have
to work hard,
then you’re
not a genius
If you’re not
a genius,
then you
shouldn’t do
science or
math
FIGURE 2. An illustration of the cycle of demotivating beliefs
that steers students away from persisting in science learning.
Reprinted from “Fear of failure prevents students to learn
STEM,” by X. Lin-Siegler, (2015). Paper presented at the meeting
of the American Educational Research Association (AERA)
Presidential Invited Address, Chicago, IL. Copyright [2015] by X.
Lin-Siegler. Reprinted with permission.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 4
Decreased interest in science
Depersonalized instruction may unintentionally hamper
student’s engagement and interest in science learning.
According to Hidi and Anderson (1992), there are two kinds
of interests—individual and situational interests. Individual
interest is interest that students bring to the learning
environment. Some students come to a science classroom
already interested in the subject matter, whereas others
do not (Mitchell, 1993). In contrast, situational interest is
acquired by participating in the learning environment. For
example, some learning environments are more motivating
than others. Both types of interests enhance science learn-
ing, but individual interest usually develops slowly and tends
to be long-lasting, whereas situational interest can develop
quickly, but is often transitory (Hidi & Anderson, 1992).
Instructional designers tend to focus on stimulating
situational interest by improving the appeal of textbooks
or increasing the comprehensibility or readability of the
texts (see, e.g., Graesser, León, & Otero, 2002; Otero, León, &
Graesser, 2002) rather than enhancing individual interest. The
lack of considering individual interest in instructional design
undercuts students’ sense of autonomy, which is the degree
one feels that one’s activities and goals are concordant with
intrinsic interests and values (Deci & Ryan, 1985; Kasser &
Ryan, 1996). For example, a student who lacks autonomy
is assigned the chapter readings and does not take notes,
participate in discussions, or ask questions. In contrast, a
student who has autonomy sets a goal for himself/herself
to read one chapter of a science textbook per night, actively
takes notes, and asks questions when he/she does not
understand the content. Presenting stories about scientists,
their work, and their lives can inspire individual interest in
science learning and enhance students’ autonomy.
There are other ways that science content can be made
more relevant. For example, emphasizing the benefits of
scientific endeavor – better treatments for cancer, better
screenings for early detection of cancer – can be highly
motivating for students with family members impacted
by cancer. However, emphasizing the benefits might not
be sufficient in challenging students’ beliefs about success
in science. An important aspect of our struggle-oriented
instruction is that it emphasizes the process of scientific
discovery by normalizing struggle as a part of science (and
learning in general). Doing so not only humanizes science
content but also challenges students’ beliefs that only
unusually smart people succeed in science.
In summary, depersonalized instruction reduces students’
interest and motivation to learn science because an exclu-
sive focus on content knowledge undermines basic human
psychological needs for relatedness, competence, and
autonomy. When these needs are not fostered in the learn-
ing context, students are deterred from science learning.
Exposure to scientists’ struggles can aid science learning by
fostering these innate needs.
For the remainder of the paper, we discuss how we designed
our story-based instruction for schools, the factors that
influenced our design, and how our instruction was imple-
mented. Finally, we consider the implications our instruction
has for instructional design and research.
THE EVOLUTION OF OUR STRUGGLE
ORIENTED INSTRUCTION
In general, the format of our instruction was as follows:
During the first week, students received various pre-test
measures that assessed their beliefs about intelligence (how
malleable vs. fixed), stereotypes they held about science and
scientists, and perceptions about their own ability to suc-
ceed in STEM areas. During the next 2-3 weeks, students read
at least two stories about how famous scientists struggled
prior to their discoveries (one story a week). In the final week,
students filled out the same measures as they did during the
pre-test to assess if there were any changes in their beliefs
post-intervention.
A user-centered design and development approach required
that our instruction meet three situational constraints: First,
everything had to be comprehensible and understand-
able for our target population (8th-10th graders in urban
schools). Second, the instruction had to fit into four (or five)
45-minute regular class periods (mainly during students’
advisory classes). Teachers from all subjects (science, math,
social studies, and English) led these advisory classes, which
focused on lessons regarding academic and social/emotion-
al issues and incorporating the messages of our instruction.
Third, because not every student had access to a computer
and most schools had unreliable Internet connections, our
materials had to be text-based. Within these constraints, we
designed our instruction.
The three main goals of our instruction were based on
research on self-determination theory and intrinsic motiva-
tion (see Deci & Ryan, 2000). These goals were to: (a) improve
students’ felt and perceived relatedness to scientists, (b)
confront students’ beliefs about their competence and ability
in science, and (c) increase students’ sense of autonomy over
their science learning. Our instruction attempted to foster
these needs for relatedness, competence, and autonomy,
so that students could better interact and engage with the
content knowledge they learned in school. Therefore, it is
important to note that our instruction was not designed
for any particular science content knowledge. Instead, the
goal was to enhance students’ motivation so they are better
prepared to learn the science content.
The way we implemented our instructional goals was
guided by David McClelland’s seminal work on achievement

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 5
motivation. His work emphasized that teaching students
how to think, talk, and behave as a motivated person would
incite motivated actions (McClelland, 1969, 1972, 1987). A
motivated person demonstrates actions such as vigorous
enactment toward goal attainment, persistence in the face
of obstacles, and resumption after disruption (Heckhausen,
1991; Lewin, 1926; Wicklund & Gollwitzer, 1982). Therefore,
in the different iterations of our instruction, we progressively
modeled for students how to stay motivated through
challenges and persist through obstacles by detailing how
scientists have similarly gone through struggles. Although
we created several iterations of our instruction, we believe
there are three main iterations that best capture our design
principles (see Table 1 for an overview of these iterations;
also see Appendix A for an example of the instruction that
students received).
Iteration #1: Descriptive Instruction
The goal of the first iteration of our instruction was to
present a general message of struggle (i.e., success in science
requires effort more than ability) as a normal part of scientific
achievement.
Content of the story
The instruction first began by introducing students to the
scientists, providing basic biographical information about
the scientist (e.g., birthplace, ethnicity, gender, etc.) and
shifting to information about their research (e.g., “Marie Curie
conducted experiments to help us understand radioactive
energy”). Students also read about the struggles that the
scientists encountered in the process of their scientific
discoveries (e.g., multiple failed experiments). Then, they
read motivational messages that exceptional talent is not
required for success in science. For example: “How was Marie
Curie so successful? Many think Curie was a genius who
was born that way, but effort was needed to achieve her
accomplishments. She realized that in order to succeed you
have to try things over and over again even when you make
mistakes or fail.” Moreover, we focused on the key scientific
discoveries that the scientists made and how those discov-
eries impacted the world: “Curie’s determination resulted in
changing both physics and chemistry.”
Instructional approach
We presented general information about scientists’ struggles
to confront students’ beliefs about succeeding in science.
For example, Marie Curie’s success was not a result of her
exceptional ability, but of her hard work.
Implementation of the instruction
The first iteration was largely teacher-led instruction where
the teachers instructed students to read the stories and
answer questions about their perceptions. This means that
the instruction was designed to reflect closely what students
would typically experience in a classroom.
Iteration #2: Procedural and Interactive Instruction
A field-testing of this general struggle-oriented instruction
points to a rather serious weakness. Students reported
that the stories were interesting and engaging, yet, they
had a difficult time understanding concretely why these
MOTIVATIONAL GOAL STORY CONTENT INSTRUCTIONAL APPROACH
ITERATION 1
• Present information that
scientists struggled.
• Present a general message
about struggle.
• Present biographical
information.
• Story-based Instruction
ITERATION 2
• Explain scientists’ goals.
• Provide inspiring actions
scientists took during the
process of struggling.
• Emphasize the process of
struggle.
• Show the strategies used by
scientists.
• Interactive Stories
ITERATION 3
• Highlight scientists’ failures.
• Have students experience
the benefits of struggling and
persisting.
• Highlight the specific types of
failures scientists faced.
• Show the specific strategies
scientists used to overcome
those failures.
• Enable students to experience
the benefit of persisting
through a task.
• Interactive Stories,
• along with practicing
persistence using
supplementary activities.
TABLE 1. A conceptual summary of the three iterations of our instruction. This table depicts the three main iterations through which
our instruction evolved. Included in this table are the goals behind each evolution, the story content, and the instructional approach
employed.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 6
scientists struggled and the specific strategies scientists used
to overcome their struggles were not clear. For instance,
students did not see any goals that the scientists were trying
to accomplish or strategies they employed to reach those
goals. If the goals for struggling are not made explicit and
are not proceduralized, then students will have a difficult
time modeling after the scientists’ behaviors. This notion was
addressed in the subsequent iterations of our instruction.
Accordingly, in the second iteration we: (a) proceduralized
the process of struggling, (b) had the scientists model
useable strategies, and (c) encouraged students to imagine
themselves as struggling scientists.
Content of the story
Different from the first iteration, we prompted students
in the second iteration to immerse themselves into the
struggle stories by taking the perspective of the scientist:
“Imagine yourself as a scientist.” This was done so that they
could mentally simulate the struggles that the scientists had
to go through.
Additionally, the second iteration made explicit the process
of scientific discovery and explained what motivated the
scientists to persist and work hard. For example, “As a young
scientist in France, [Marie Curie] observed a very strange
phenomenon. If pitchblende, a dark and heavy mineral, was
placed next to a piece of film, a dark image in the shape of
the mineral would appear on the film.” The story then vividly
conveyed how Curie experimented with pitchblende. She
experimented with others minerals, checked her calculations
multiple times, and repeated her experiments over and over:
“[Marie Curie] tried dozens of different combinations of rock,
chemicals, and water to separate out the element that she
believed was hidden inside the pitchblende.” These examples
emphasize how scientific experiments require an iterative
process that takes persistence and effort.
Moreover, students received detailed descriptions of the
hurdles and obstacles that the scientists (in this case Marie
Curie) had to overcome: “Given that there was unfair sexism
toward women scientists at that time, she had to convince
her male colleagues to take her work seriously.” Importantly,
students even learned the strategies that the scientists used
to overcome these challenges. They read that Curie met with
30 scientists individually and solicited feedback from them
to improve her work: “In these meetings, she presented her
work and then listened to each scientist’s critique. With every
meeting, she incorporated the new feedback she was given.
As a result, she improved her presentation skills, learning to
focus on the main points of her scientific research and the
importance of her discoveries. Because of these efforts, she
became widely respected in her field.”
Instructional approach
We shifted from a more passive reading of the stories with
a general message about struggle (first iteration), to a more
interactive and action-inspiring instruction that modeled for
students how to overcome struggle. First, the instruction was
interactive and allowed students to openly discuss the expe-
riences of scientists: “Describe the struggles and successes
that Albert Einstein experienced in your own words.”
Second, we introduced a “learning contract” so students
could (a) set their own learning goals, and (b) develop strat-
egies to reach those goals. The purpose of making their own
contracts was to urge students to apply what they learned
from our story-based instruction to improve learning in their
own science classes. They were given the following prompts:
“During the next week, I will improve my science classes by
doing the following two activities: (Be as specific as possible)”
and “The actions that I chose relate to the scientists’ stories in
this way.” Students were encouraged to avoid writing general
phrases like “try harder” and instead write specific actions.
For example, they could consult a teacher, complete practice
problems, and ask questions in class. In creating these
contracts, students’ sense of autonomy is enhanced because
they are able to declare when and how they would take
control of their science learning in the near future.
Implementation of the instruction
Whereas the first iteration of our instruction was what we
described as “teacher-led” and “teacher-incorporated,” the
second iteration is what we describe as “student-initiated.”
We intentionally moved our instructional activities beyond
solitary, self-paced reading towards active engagement,
open dialogue, cross-talk in small groups, personalization,
etc.). Additionally, we worked very closely and intimately
with the teachers and principals of the schools to decide
how to best deliver our instruction. Based on teacher and
student suggestions, our instruction became part of an
advisory class woven into a normal class day. Delivering our
instruction in this manner made it easier for students to
apply the messages and lessons learned into their own lives.
Iteration #3: Procedural, Interactive Instruction, Plus
Experiencing Persistence
In the third iteration of our instruction, we tried more
decisively to foster all three psychological needs (needs
for relatedness, competence, and autonomy) by fulfilling
these goals: proceduralize failures, model specific strategies,
and (new in this iteration) give students the opportunity to
experience the benefits of persistence. Exposing students
to scientists’ struggles in general (the first iteration) is not
sufficient to truly enhance students’ motivation, nor is it
enough to emphasize the process of struggling to inspire
action (second iteration). Students need the opportunity
to act out their persistence and feel the resulting reward

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 7
to incite motivated behaviors (see McClelland, 1969, 1972,
1987). In doing so, their sense of autonomy and competence
are enhanced.
In addition, the first two iterations of the instruction em-
phasized how scientists struggled through their difficulties,
while failures were less emphasized. Without vivid depictions
of how scientists failed and how they overcame failure (i.e.,
responding to failure with specific strategies), it makes it
difficult for students to model after the scientists’ behaviors.
Therefore, we proceduralized the process of failure more
explicitly for students, as well as detailed specific strategies
used by the scientists.
Particular to this iteration, we encountered contextual
constraints. First, experiencing the benefits of persistence re-
quires time, which we often do not have in schools. Second,
selecting an appropriate task where students can persist in
a meaningful way is also challenging because students vary
in goal pursuits they deem to be desirable or feasible, and
these factors typically affect how motivated a person will be
(Bargh, Gollwitzer, & Oettingen, 2010; Gollwitzer & Oettingen,
2012; McClelland, 1978; Touré-Tillery & Fishbach, 2014). To
best meet these constraints, we chose two tasks – reading
a challenging science excerpt and working on a number
combination task (detailed below in subsequent sections)
– because these tasks met the practical challenges imposed
on us, albeit not perfectly.
Content of the story
Unlike the previous iterations, the third iteration of the
instruction began by having the experimenter share his/her
struggle story. We hoped that beginning the instruction in
this manner would increase the felt connectedness with the
experimenter that would then transfer to the scientists.
The stories in this instruction pinpointed the exact failure
that the scientists encountered and detailed the specific
strategies they used to overcome the failure. For example,
students read about how Marie Curie tried to disentangle
the radioactive elements in pitchblende that would be most
useful to her discovery. Students read the specific strategies
and actions that Curie took to overcome this particular
challenge: (a) she persisted, “After 1,000 experiments and
an entire ton of pitchblende...”; (b) she stuck it out for a long
time, “She didn’t take any shortcuts or skip over any steps.
Even a tiny miscalculation would ruin her experiment, so she
made sure her measurements were accurate multiple times.
She ran hundreds of experiments and kept a detailed record
of what she did”; and (c) she sought feedback from others,
“she met with nearly 30 important scientists one by one be-
fore the big meeting to receive feedback on her talk.” Seeing
the specific ways that scientists responded to the challenges
provides students with a crystal clear template of how they
could apply such strategies into their own lives.
Instructional approach
Similar to the second iteration of our instruction, the stu-
dents engaged in various discussions with the experiment-
ers regarding what they read and then created individual
learning contracts.
The key element of the third iteration that was different from
the others is that it gave students the opportunity to expe-
rience the benefit of persisting. They were asked to practice
persistence in two activities (reading a challenging excerpt
from a popular science magazine1 and working on a number
combination task). For example, in reading the challenging
science article, students were told they could stop reading
whenever they wanted. However, they were encouraged to
read as much as they could. This task allows students to push
themselves a little more and stick through challenges just a
little longer. Students can see that the more they read, the
more they can understand (similar to how Curie persisted in
her experiments and eventually saw the benefit of staying
on tasks longer).
Additionally, in the number combination task, students were
shown the following numbers: 1, 2, 3, 4, and asked to arrange
them in various combinations without repeating any order.
This task was loosely based on Inhelder & Piaget’s (1958)
combinatorial reasoning task that examined whether young
children are able to engage in scientific reasoning. In this
task, students were able to develop a combinatorial system
and draw further insights the longer they were able to stay
with the task. Once students figured out a “system,” they
were able to complete the task.
Implementation of the instruction
Similar to the second iteration of the instruction, the third
iteration was also “student-initiated.” Students were given
more opportunities to engage with the material through
open dialogue and cross-talk in small groups.
CONCLUSION
Summary
In this paper, we discussed how content-based instruction
primarily focuses on teaching students scientific content
knowledge and skills. However, even the best content-based
instruction does not guarantee improved learning, as
students’ motivation ultimately determines whether or
not they take advantage of the instruction. We designed a
struggle-oriented instruction to enhance students’ moti-
vation so they are better prepared to engage with content
knowledge. Our instruction tells stories about how even
great scientists struggled and failed prior to their scientific
1 The science excerpt was not related to any science content that the
scientists in the stories engaged in (i.e., about radioactive materials that
Curie worked on) nor was it related to any content that was currently being
taught in students’ science classes.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 8
discoveries. We described how we have gone about design-
ing our instruction and implementing it in schools, outlining
the factors that influenced our decisions under situational
constraints. With each evolution of the three iterations, our
instruction progressively evolved to better foster the three
psychological needs (needs for relatedness, competence,
and autonomy) and modeled with precision how students
could stay motivated.
Lessons Learned for the Project Team
There are important lessons we learned from designing
our instruction. First, it is questionable whether the two
supplemental activities used in the last iteration (i.e., reading
through a challenging science excerpt and working on
a number combination task) were ideal tasks to use. We
are currently in the process of analyzing the data to assess
whether these tasks were a good fit and continuing to
brainstorm new alternative tasks to employ. As designers, we
are constantly updating and revamping our instruction to
better improve it in every way we can.
Additionally, based on preliminary data analysis, new ques-
tions have emerged such as whether having ethnic matches
with the scientists might have a more potent intervention
effect. Although we are in the early stages of analysis, we can
only speculate that this might be the case, and we plan on
doing further research to address this concern.
Finally, all the iterations of our instruction did not integrate
science content. As stated, we kept content separate from
our instruction because the goal was to enhance students’
motivation to improve their own science learning. We
acknowledge that researchers have demonstrated that
integrating intervention methods and content materials en-
hanced students’ performance and learning more than just
providing the intervention alone (Bernacki, Nokes-Malach,
Richey, & Belenky, 2014; Han & Black, 2011; Slavin, Madden, &
Wasek, 1996). However, we wanted to create an instruction
that was not tied too closely to any one type of science
content. Instead, our goal was to create an instruction that
could flexibly support any science content.
Design Principles of the Project Team
We have been working toward design principles in strug-
gle-oriented instruction that are needed to affect students’
motivation in science learning. There are many possible
principles to which we could adhere, but we highlight the
primary ones that we derived from the preceding discussion.
They are:
1. Humanize content knowledge by providing the stories
behind the product.
2. Reveal the inner and external struggles an individual
(e.g., a scientist) went through.
3. Make the learning process vivid with explicit actions and
strategies.
4. Portray the outcome benefits of struggling that are
relevant to the individual’s life.
5. Act out motivated actions and embody the model’s
actions.
Principle 1: Humanize content knowledge by providing the
stories behind the product
Content-based instruction can be a depersonalized ap-
proach to science teaching. And, a depersonalized approach
to science can lead to forming stereotypes about scientists
(e.g., geniuses do not work hard), which can perpetuate the
disconnect that we observe between students and their
understanding of scientists’ work. Infusing science content
with personal biographies about how even famous scientists
struggled and failed prior to their discoveries serves to
bridge the gap between how students perceive scientists
and scientists’ work (see Lin & Bransford, 2010). Thus, we
showed how great scientists (such as Albert Einstein) have
failed prior to their achievements, thereby challenging
students’ beliefs that only unusually smart people succeed in
science.
Principle 2: Reveal the inner and external struggles a scientist
went through
Exposing scientists’ vulnerabilities can increase the felt
connectedness between students and the scientists. In our
stories, we made clear both the personal and academic
struggles that scientists experienced that made their jour-
neys very difficult (e.g., both Albert Einstein and Marie Curie
grew up in poverty and their families struggled financially).
When students can visualize how scientists have gone
through their struggles, this imagery challenges students’
beliefs that only unusually smart people can succeed in
science. When students’ beliefs are confronted, their moti-
vation to pursue STEM fields might increase because of the
felt connectedness to the scientists, thereby enhancing their
willingness to persist.
Principle 3: Make the learning process vivid with explicit
actions and strategies
Confronting students’ beliefs that exceptional ability is
required to succeed in science might enhance their motiva-
tion to do better in their STEM classes, but this is not enough
to motivate actions to pursue their goals. People have good
intentions to pursue goals but often fail in executing the
appropriate actions to fulfill these goals because of various
external distractions (temptations) and internal self-regula-
tory failure (Gollwitzer, 1993, 1999). We may know what we
need to do, but we fail in knowing how to do it (Gollwitzer,
1990, 1993, 1999; Gollwitzer & Oettingen, 2012).
In our instruction, we proceduralized struggles and failures
by making explicit the types of problems the scientists

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 9
encountered and the specific strategies they employed to
overcome those problems. By doing so, students learn how
to directly model after scientists’ behavior when encounter-
ing similar struggles and failures in science learning.
Principle 4: Portray the outcome benefits of struggling that
are relevant to the individual’s life
Emphasizing the outcome benefits of struggling is import-
ant in keeping people motivated. If the outcomes are not
clear, then students do not know why they should work hard
and persist through difficulties (see literature on perceived
short-term and long-term outcome benefits of activities;
Ainslie, 1992; Loewenstein, 1996; Metcalfe & Mischel, 1999;
Mischel, 1974; Mischel, Shoda, & Peake, 1988; Rachlin, 1995,
1996, 1997; Shoda, Mischel, & Peake, 1990; Trope & Fishbach,
2000). Thus, in our stories we mention the end goal for
persisting. For example, Marie Curie worked hard to discover
radioactive elements that ultimately led to her goal of help-
ing people with illnesses: “After years of meticulous research
and an entire ton of pitchblende, her hard work paid off
when she managed to separate out not just one, but two
new radioactive elements, which she named Radium and
Polonium, after her home country of Poland. She reached
her goal! Not only had she unlocked the mystery of pitch-
blende, she had discovered elements that could be used to
create X-rays to diagnose illness.”
Principle 5: Act out motivated actions and embody the
model’s actions
Finally, to further internalize the message that exceptional
talent is not required to succeed in science, students were
asked to embody the motivated behaviors they read about.
Learning through complementary examples through
which students can directly see, feel, experience, move, and
manipulate (i.e., involve more senses) enriches the learning
experience (Black, Segal, Vitale, & Fadjo, 2012; Chan, & Black,
2006; Han & Black, 2011).
In our last iteration, students had the opportunity to
experience the benefits of persisting, but as acknowledged,
the tasks used might not have been ideal (i.e., due to time
constraints, design constraints, etc.). In the future, we will
have students create a comic book in which they react to
scenarios where they struggle and fail. By doing so, students
could act out how they can remain motivated despite failure
and apply the strategies they just learned from the scientists.
Currently, we are incorporating these principles to create
an interactive multimedia-based instruction. As of now,
our instruction has primarily relied on text-based format.
However, people learn more easily when they are presented
information in both verbal and visual form (Bransford, Brown,
& Cocking, 1999; Cowen, 1984, Salomon, 1979). To better
match the advancement of technology in students’ lives and
in our culture, we plan to deliver our instruction in movie
form since “people can learn more deeply from words and
pictures than from words alone” (Mayer, 2005, p. 1).
The ultimate goal of our instruction is to address the “leaky
STEM pipeline” problem and retain more students in STEM
fields. We will need more work to incorporate these design
principles and to adjust our instruction accordingly. All in
all, there are many ways we look forward to evolving our
instruction and many directions we can go; we will continue
to evolve our instruction so students, teachers, and designers
can all benefit.
ACKNOWLEDGMENTS
This work was supported by National Science Foundation (NSF)
Research and Evaluation on Education in Science and Engineering
(REESE) Grant Award #: DRL-1247283 to Xiaodong Lin-Siegler and
Carol Dweck. The opinions expressed in the article are those of
the authors only and do not reflect the opinions of NSF. Special
thanks for the generous support from New York City public schools
and their teachers: Doreen Conwell, Tamar Muscolino, Kecia
Hayes, Owusu Afriyie Osei, Jared Jax, Karalyne Sperling, and Mark
Erlenwein.
REFERENCES
Ainslie, G. (1992). Picoeconomics: The strategic interaction of successive
motivational states within the person. Cambridge, UK: Cambridge
University Press.
Amos, S., & Boohan, R. (2002). Teaching science in secondary schools:
A reader. Psychology Press.
Anderson, J. R. (1990). Cognitive psychology and its implications (3rd
ed.). New York, NY: Freeman.
Anderson, J. R. (2013). The architecture of cognition. New York, NY
and London, UK: Psychology Press.
Anderson, J. R. (2014). Rules of the mind. New York, NY and London,
UK: Psychology Press.
Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ:
Prentice-Hall.
Bandura, A. (1986). Social foundations of thought and action: A social
cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1988). Perceived self-efficacy: Exercise of control
through self-belief. In J. P. Dauwalder, M. Perrez, & V. Hobi (Eds.),
Annual series of European research in behavior therapy (pp. 27-59).
Amsterdam/Lisse, NL: Swets & Zeitlinger.
Bargh, J. A., Gollwitzer, P. M., & Oettingen, G. (2010). Motivation. In S.
Fiske, D. T. Gilbert, & G. Lindzay (Eds.), Handbook of social psychology
(5th ed., pp. 268-316). New York, NY: Wiley.
Baumeister, R., & Leary, M. (1995). The need to belong: Desire for
interpersonal attachments as a fundamental human motivation.
Psychological Bulletin, 117, 497-529.
Bell, P., Bricker, L. A., Tzou, C., Lee, T., & Van Horne, K. (2012). Exploring
the science framework: Engaging learners in scientific practices
related to obtaining, evaluating, and communicating information.
Science Scope, 36, 17-22.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 10
Bennett, J. (2005) Teaching and learning science: A guide to recent
research and its applications. London, UK: Continuum.
Bernacki, M., Nokes-Malach, T., Richey, J. E., & Belenky, D. M.
(2014). Science diaries: a brief writing intervention to improve
motivation to learn science. Educational Psychology. Advance online
publication. http://dx.doi.org/10.1080/01443410.2014.895293
Black, J. B., Segal, A., Vitale, J. & Fadjo, C. (2012). Embodied cognition
and learning environment design. In D. Jonassen & S. Lamb (Eds.),
Theoretical foundations of student-centered learning environments
(2nd ed., pp. 198-223). New York, NY: Routledge.
Bransford, J., Brown, A., & Cocking, R. (1999). How people learn.
Washington, DC: National Academy Press.
Cawthorn, E. R. & Rowell, J. A. (1978). Epistemology and science
education. Studies in Science Education, 5, 31-59.
Chambers, D. W. (1983). Stereotypic images of the scientist: The
draw-a-scientist test. Science Education, 67, 255-265.
Chan, M.S. & Black, J.B. (2006) Direct-manipulation animation:
Incorporating the haptic channel in the learning process to support
middle school students in science learning and mental model
acquisition. In S. Barab, K. Hay, & D. Hickey (Eds.), Proceedings of the
7th International Conference of the Learning Sciences (pp. 64-70).
Mahwah, NJ: LEA.
Cowen, P. S. (1984). Film and text: Order effects in recall and social
inferences. Educational Communications and Technology Journal, 32,
131-144.
De Charms, R. (1968). Personal causation. New York, NY: Academic
Press.
Deci, E. L. (1976). Notes on the theory and metatheory of intrinsic
motivation. Organizational Behavior and Human Performance, 15,
130-145.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-
determination in human behavior. New York, NY: Plenum.
Deci, E. L., & Ryan, R. (2000). The “what” and “why” of goal pursuits:
Human needs and the self-determination of behavior. Psychological
Inquiry, 11, 227-268.
Dovidio, J. F. (2001). On the nature of contemporary prejudice: The
third wave. Journal of Social Issues, 57, 829-849.
Dweck, C.S. (2000). Self-theories: Their role in motivation, personality,
and development. Philadelphia, PA: Psychology Press.
Dweck, C. S. (2010a). Even geniuses work hard. Educational
Leadership, 68, 16-20.
Dweck, C. S. (2010b). Mind-sets and equitable education. Principal
Leadership, 10, 26-29.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to
motivation and personality. Psychological Review, 95, 256-273.
Eshach, H. (2009). The Nobel Prize in the physics class: Science,
history, and glamour. Journal of Science & Education, 18, 1377-1393.
Gaertner, S. L., Mann, J., Murrell, A., & Dovidio, J. F. (1989). Reducing
intergroup bias: The benefits of recategorization. Journal of
Personality and Social Psychology, 57, 239-249.
Gladwell, M. (2008). Outliers: The story of success. New York, NY: Little,
Brown and Company.
Gollwitzer, P. M. (1990). Action phases and mind-sets. In E. T. Higgins
& R. M. Sorrentino (Eds.), The handbook of motivation and cognition:
Foundations of social behavior (Vol. 2, pp. 53-92). New York, NY:
Guilford Press.
Gollwitzer, P. M. (1993). Goal achievement: The role of intentions.
European Review of Social Psychology, 4, 141-185.
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of
simple plans. American Psychologist, 54, 93-503.
Gollwitzer, P. M. & Oettingen, G. (2012). Goal pursuit. In R. M. Ryan
(Ed.), The Oxford handbook of human motivation (pp. 208-231). New
York, NY: Oxford University Press.
Good, C., Rattan, A., & Dweck, C.S. (2012). Why do women opt out?
Sense of belonging and women’s representation in mathematics.
Journal of Personality and Social Psychology, 102, 700-717.
Graesser, A. C., León, J. A., & Otero, J. (2002). Introduction to the
psychology of science text comprehension. In J. Otero, J. A. León,
& A. C. Graesser (Eds.), The psychology of science text comprehension
(pp. 1-15). Mahwah, NJ: Erlbaum.
Han, I., & Black, J. B. (2011). Incorporating haptic feedback in
simulation for learning physics. Computers & Education, 57,
2281-2290.
Harter, S. (1978). Effectance motivation reconsidered. Toward a
developmental model. Human Development, 21, 34-64.
Heckhausen, H. (1991). Motivation and action. New York, NY:
Springer Verlag.
Hidi, S., & Anderson, V. A. (1992). Situational interest and its impact
on reading and expository writing. In K. A. Renninger, S. Hidi, & A.
Krapp (Eds.), The role of interest in learning and development (pp.
215-238). Hillsdale, NJ: Lawrence Erlbaum Associates.
Hong, H., & Lin-Siegler, X. (2012). How learning about scientists’
struggles influences students’ interest and learning in physics.
Journal of Educational Psychology, 104, 469-484.
Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from
childhood to adolescence. (A. Parsons & S. Milgram, Trans.). New York,
NY: Basic Books. (Original work published 1955).
Kasser, T., & Ryan, R. M. (1996). Further examining the American
dream: Differential correlates of intrinsic and extrinsic goals.
Personality and Social Psychology Bulletin, 22, 280-287.
Kubli, F. (1999). Historical aspects in physics teaching: Using Galileo’s
work in a new Swiss project. Science and Education, 8, 137–150.
Lewin, K. (1926). Vorsatz, Wille und Bediirfnis [Intention, will, and
need]. Psychologische Forschung, 7, 330-385.
Lin, X. D. & Bransford, J. D. (2010). Personal background knowledge
influences cross-cultural understanding. Teachers College Record, 12,
1729-1757.
Lin-Siegler, X., Ahn, J. N., Chen, J., Fang, F.A., & Luna-Lucero, M. (in
press). Even Einstein struggled: Effects of learning about great
scientists’ struggles on high school students’ motivation to learn
science. Journal of Educational Psychology.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 11
Loewenstein, G. (1996). Out of control: Visceral influences on
behavior. Organizational Behavior and Human Decision Process, 65,
272-292.
Mayer, R. E. (2005). Introduction to multimedia learning. In R. E.
Mayer (Ed.), The Cambridge handbook of multimedia learning (pp.
1-16). New York, NY: Cambridge University Press.
McClelland, D. C. (1969). The role of educational technology in
developing achievement motivation. Educational Technology, 9,
7-16.
McClelland, D. C. (1972). What is the effect of achievement
motivation training in the schools? The Teachers College Record, 74,
129-146.
McClelland, D. C. (1978). Managing motivation to expand human
freedom. American Psychologist, 33, 201-210.
McClelland, D. C. (1987). Human motivation. CUP Archive.
Mead, M., & Metraux, R. (1957). Image of the scientist among high
school students. Science, 126, 384-390.
Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay
of gratification: Dynamics of willpower. Psychological Review, 106,
3-19.
Mischel, W. (1974). Processes in delay of gratification. In L. Berkowitz
(Ed.), Advances in experimental social psychology (Vol. 7, pp. 249-292).
San Diego, CA: Academic Press.
Mischel, W., Shoda, Y., & Peake, P. K. (1988). The nature of adolescent
competencies predicted by preschool delay of gratification. Journal
of Personality and Social Psychology, 54, 687-696.
Mitchell, M. (1993). Situational interest: Its multifaceted structure
in the secondary school mathematics classroom. Journal of
Educational Psychology, 85, 424-436.
Murphy, M. C. & Dweck, C. S. (2010). A culture of genius: How an
organization’s lay theory shapes people’s cognitive, affect, and
behavior. Personality and Social Psychology Bulletin, 36, 283-296.
National Research Council (NRC). (2000). Inquiry and the national
science education standards: A guide for teaching and learning.
Washington, DC: National Academy Press.
Otero, J. C., León, J. A., & Graesser, A. C. (Eds.). (2002). The psychology
of science text comprehension. Mahwah, NJ: Lawrence Erlbaum.
Oyserman, D., Bybee, D., & Terry, K. (2006). Possible selves and
academic outcomes: How and when possible selves impel action.
Journal of Personality and Social Psychology, 91, 188-204.
Pintrich, P. R. (2003). A motivational science perspective on the role
of student motivation in learning and teaching contexts. Journal of
Educational Psychology, 95, 667-686.
Rachlin, H. (1995). Self-control: Beyond commitment. Behavioral and
Brain Sciences, 18, 109-159.
Rachlin, H. (1996). Can we leave cognition to cognitive
psychologists? Comments on an article by George Loewenstein.
Organizational Behavior and Human Decision Process, 65, 296-299.
Rachlin, H. (1997). Self and self-control. In J. G. Snodgrass & R. L.
Thompson (Eds.), The self across psychology: Self-recognition, self-
awareness, and the self concept. Annals of the New York Academy of
Sciences (Vol. 818, pp. 85-97). New York, NY: New York Academy of
Sciences.
Rattan, A., Savani, K., Naidu, N. V. R., & Dweck, C. S. (2012). Can
everyone become highly intelligent? Cultural differences in and
societal consequences of beliefs about the universal potential
for intelligence. Journal of Personality and Social Psychology, 103,
787-803.
Reis, H. T. (1994). Domains of experience: Investigating relationship
processes from three perspectives. In R. Erber & R. Gilmour (Eds.),
Theoretical frameworks for personal relationships (pp. 87-110).
Hillsdale, NJ: Lawrence Erlbaum.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the
facilitation of intrinsic motivation, social development, and well-
being. American Psychologist, 55, 68-78.
Salomon, G. (1979). Interaction of media, cognition, and learning. San
Francisco, CA: Jossey-Bass Publishers.
Shoda, Y., Mischel, W., & Peake, P. K. (1990). Predicting adolescent
cognitive and self-regulatory competencies from preschool delay
of gratification: Identifying diagnostic conditions. Developmental
Psychology, 26, 978-986.
Slavin, R.E., Madden, N.A., & Wasik, B.A. (1996). Roots and wings:
Universal excellence in elementary education. In S. Stringfield, S. M.
Ross, & L. Smith (Eds.), Bold plans for restructuring: The New American
Schools designs (pp. 207-231). Mahwah, NJ: Lawrence Erlbaum.
Solbes, J., & Traver, M. (2003). Against a negative image of science:
History of science and the teaching of physics and chemistry.
Science & Education, 12, 703–717.
Stipek, D., & Gralinski, J. H. (1996). Children’s beliefs about
intelligence and school performance. Journal of Educational
Psychology, 88, 397-407.
Sutman, F., & Bruce, M. (1992). Chemistry in the community –
ChemCom: A five-year evaluation. Journal of Chemical Education, 69,
564–567.
Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup
conflict. In M. A. Hogg & D. Abrams (Eds.), Intergroup relations:
Essential readings (pp. 94-109). Philadelphia, PA: Psychology Press.
The Next Generation Science Standards (NGSS) Lead States.
(2013). Next generation science standards: For states, by states.
Retrieved October 29, 2015 from http://www.nextgenscience.org/
next-generation-science-standards.
Touré-Tillery, M., & Fishbach, A. (2014). How to measure motivation:
A guide for the experimental social psychologist. Social and
Personality Psychology Compass, 8, 328- 341.
Trope, Y., & Fishbach, A. (2000). Counteractive self-control in
overcoming temptation. Journal of Personality and Social Psychology,
79, 493-506.
Ward, A. (1977). Magician in a white coat. Science Activities, 14, 6-9.
White, R. W. (1963). Sense of interpersonal competence: Two case
studies and some reflections on origins. In R. W. White (Ed.), The
study of lives (pp. 72-93). New York, NY: Prentice Hall.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 12
Wicklund, R.A., & Gollwitzer, P.M. (1982). Symbolic self-completion.
Hillsdale, NJ: Erlbaum.

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APPENDIX A
Sample of our Struggle-Oriented Instruction
Today, we will read two stories together about the difficulties the world’s greatest scientists experienced and how they overcame them.
Before beginning, please close your eyes and imagine that you are the scientist. What would you do and how would you feel in their
shoes? You are now the scientist!
*****************************************************************************
Even the Greatest Scientist Failed Before Succeeding
She grew up in Warsaw, Poland. When she was 10 years old, she lost her mother to a lung infection. There would have been
a way to save her life if doctors had the proper materials. It was her mother’s death that inspired her to study science. For her,
learning science meant to understand how things work and how things happen in our lives. She decided to deal with the grief
of losing her mother by throwing herself into her studies in order to help others like her mother in the future.
Unfortunately, the Polish universities did not accept women at that time. She left home and traveled to Paris to study science
there. To pay for her education, she took classes during the day and worked in grocery stores at night. She completed home-
work during her breaks. Her hard work paid off, she was one of the only two women who graduated with a degree in physical
sciences.
*****************************************************************************
Can you give us an example where you had a lot going on in your life while also trying to complete homework assignments and
prepare for tests?
*****************************************************************************

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 14
As a young scientist in France, she observed a very strange phenom-
enon. If pitchblende—a heavy mineral—was placed next to a piece
of film, a dark image in the shape of that mineral would appear on
the film. It seemed like the mineral developed its own picture, even
though there was no light in the room. She wondered if this material
could be used in medicine—doctors were looking for a way to take
pictures inside the human body. This material could have saved her
mother’s life.
She had a hypothesis that the unknown element contained in the
mineral was radioactive. That meant the material was so powerful
that it could release a huge amount of energy. But she had to run
many experiments to prove that she was right.
The pitchblende had many different materials inside of it and she had to discover the one element hidden in the mix that
gives the radiation. She didn’t take any shortcuts or skip over any steps. Even a tiny miscalculation would ruin her experiment
so she made sure her measurements were accurate multiple times. She ran hundreds of experiments and kept a detailed
record of what she did. But she knew the problem was too big to solve alone so she asked other scientists for feedback.
Some thought that the elements she was searching for didn’t exist, while others believed that she might find something very
important.

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 15
After 1,000 experiments and an entire ton of pitchblende, she managed to separate out not just one, but two new radioactive
elements, which she named Radium and Polonium. She reached
her goal! Not only had she unlocked the mystery of pitchblende,
she discovered elements that could be used to create X-rays to
diagnose illness.
She said, “The feeling of discouragement that came after so many
failed experiments was upsetting, but the more I understood
why I failed, the less upset I became. Each time I failed, I learned
nothing in life is to be feared; it is only to be understood. Now is
the time to understand more so that we may fear less.” With each
experiment, she learned something that made her next experi-
ment work a little better.
*****************************************************************************
Write about a situation where you did not do well in your classes at first, but you did not let yourself be beaten down. Instead, you
studied more to understand and you improved in the end.
*****************************************************************************
Scientific discoveries had to be shared in order to make a difference. Her next
challenge was to present her work in a big meeting to convince other male
scientists of her findings. Given that women scientists were not respected at that
time, she knew that she needed to be proactive in order for them to take her
work seriously. What did she do to be proactive? She met with nearly 30 import-
ant scientists one by one before the big meeting to receive feedback on her talk.
After each private meeting, she made her points sharper and clearer.
At the day of the big talk, many male scientists walked in with doubts that her
discovery was not anything useful. But as the talk progressed, they became more
and more convinced that what she discovered was truly important to our lives.
By the end of her talk, they couldn’t help but feel excited about her discovery.
They all stood up and gave her a loud applause.
*****************************************************************************
Who do you think this story was about?
*****************************************************************************

IJDL | 2016 | Volume 7, Issue 1 | Pages XX-XX 16
You may be surprised to know that this scientist is Marie Curie. Often, we talk about her success stories without mentioning
the failure that she had experienced.
Later, when her daughters asked her about all these obstacles she faced, she said, “I have never been fortunate and will never
count on luck, my highest principle is: Predict what might go wrong and take extra effort to understand what you are
doing.”
Marie Curie earned two Nobel Prizes (in chemistry and physics) and her work inspired the technology of X-ray pictures as well
as advancing the ability to diagnosis and treat cancer and other illnesses. Her work truly helped to save lives, a dream she held
since she was a child.
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What images came to mind as you were reading the story?
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