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Playful programming, Social Resilience, and Persistent Actions as Drivers of Preservice Early Childhood Teachers’ Engagement in Computer Science

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In this qualitative study, preservice early childhood education teachers created block-based code to control robots and used the robots in field experience at local preschools. The study is grounded in a conceptual framework that weaves together playful programing and resilience, interlocking concepts that can explain sustained engagement during times of challenge. We investigated how and why preservice early childhood teachers exhibit resilience during collaborative programing. We analyzed their debugging processes, reflections, and interviews using a phenomenological lens. We conducted open and axial coding and analysis of discourse and actions during debugging episodes. Results suggest that teachers exhibited resilience due to the following three reasons: through playful coding, preservice early childhood teachers (a) learned that computer science is approachable and fun, (b) engaged in adaptive attribution, and (c) engaged in joint celebration when they observed each other’s successes during collaborative tinkering. These findings provide potential insights for teacher learning of computing but also for novices learning to program.
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Original Research
SAGE Open
October-December 2024: 1–18
ÓThe Author(s) 2024
DOI: 10.1177/21582440241284488
journals.sagepub.com/home/sgo
Playful programming, Social Resilience,
and Persistent Actions as Drivers of
Preservice Early Childhood Teachers’
Engagement in Computer Science
ChanMin Kim
1
, Brian R. Belland
1
, Lucas Vasconcelos
2
,
and Roger B. Hill
3
Abstract
In this qualitative study, preservice early childhood education teachers created block-based code to control robots and used
the robots in field experience at local preschools. The study is grounded in a conceptual framework that weaves together
playful programing and resilience, interlocking concepts that can explain sustained engagement during times of challenge. We
investigated how and why preservice early childhood teachers exhibit resilience during collaborative programing. We ana-
lyzed their debugging processes, reflections, and interviews using a phenomenological lens. We conducted open and axial
coding and analysis of discourse and actions during debugging episodes. Results suggest that teachers exhibited resilience due
to the following three reasons: through playful coding, preservice early childhood teachers (a) learned that computer science
is approachable and fun, (b) engaged in adaptive attribution, and (c) engaged in joint celebration when they observed each
other’s successes during collaborative tinkering. These findings provide potential insights for teacher learning of computing
but also for novices learning to program.
Keywords
collaborative programing, playfulness, dramatic play, resilience, stereotypical conception, early childhood teacher education
Introduction
Robotics has been successfully integrated into early
childhood education (ECE) contexts, including preschool
(Di Lieto et al., 2017; Elkin et al., 2016; Kandlhofer
et al., 2014), kindergarten (Bers, 2018; Bers & Ettinger,
2012; Kazakoff & Bers, 2012), and elementary
(Blanchard et al., 2010; Francis & Poscente, 2016;
Ribeiro et al., 2011) settings. Key to the success is using
robotics not as an end in itself, but rather as a vehicle to
help children learn computer science (CS) concepts and
procedures, consistent with the K-12 CS framework (K-
12 Computer Science Framework, 2016). First, by using
robotics, early grades students can see tangible results
from creating computer code (usually block coding).
This may enhance children’s situational interest, and
contribute to enhancement of individual interest related
to CS and engineering over time (Hidi, 2006; Hidi &
Harackiewicz, 2000; Linnenbrink-Garcia et al., 2010;
Schraw & Lehman, 2001). This, in turn, could lead to
such children actively pursuing opportunities to learn
more about CS as they proceed through schooling, and
also in out-of-school contexts. For this to happen, teach-
ers need programing and robotics skills and student-
centered teaching dispositions to help their students suc-
ceed in this context. Developing skill in programing and
robotics requires that teacher candidates struggle to
1
The Pennsylvania State University, University Park, PA, USA
2
University of South Carolina, Columbia, SC, USA
3
University of Georgia, Athens, GA, USA
Corresponding Author:
ChanMin Kim, Learning, Design, and Technology, Educational Psychology,
College of Education, The Pennsylvania State University, 314D Keller
Building, University Park, PA 16802-1503, USA.
Email: cmk604@psu.edu
Data Availability Statement included at the end of the article
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of
the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).
overcome challenges (Fegely et al., 2024; Killen et al.,
2023). And just as they themselves experience struggle as
they learn to program and engage with robots, so too
will their future students, and it is critical that teachers
take a student-centered approach to helping children
overcome challenges. In this study, we examined how
and why pre-service early childhood teachers displayed
resilience while learning to debug block-based coding.
Conceptual Framework
The core grounding of our conceptual framework in the
present study is playful programing. We envisioned play
during programing that can invite participants to consider
CS to be approachable and fun. Play in our framework
is two fold. One is dramatic play (with robots) whose role
is essential within early childhood education. The other
is play with (robot) programing in which tinkering is nat-
ural in debugging and lesson design for preschoolers.
Our framework is grounded in literatures not only on
play (e.g., Ashiabi, 2007; Hurtado-Mazeyra et al., 2022;
Vygotsky, 1978) and collaborative programing (e.g.,
Dawson et al., 2018; Echeverrı
´a et al., 2017; Nagappan
et al., 2003), but also on resilience (e.g., Heljakka, 2023;
Luthar & Cicchetti, 2000; Southwick et al., 2014), As
such, it justifies the potential role of play in collaborative
programing that can invite early childhood teachers to
exhibit resilience in the face of challenges. We explain the
framework in the following two sections labeled ‘‘playful
programing’ and ‘‘resilience.’
Playful Programming
Many studies indicate that non-CS majors often learn
programing most effectively in the context of collabora-
tive learning projects (Dawson et al., 2018; Echeverrı
´a
et al., 2017; Nagappan et al., 2003). Extensive research
also indicates that collaborative programing boosts aca-
demic achievement and interest (e.g., Wu et al., 2019).
Furthermore, the present study theorized that collabora-
tive programing could counter stereotypical conceptions
of CS as solitary, non-interpersonal work that is only for
certain groups of people. Play, a unique element of the
study framework, was expected to generate collective
playfulness. Play with robots was essential to program
robots and also to design lessons for young children’s
play with robots. At the same time, this study’s concep-
tual framework helped to frame children’s dramatic play
with robots in an imaginary situation and roles to fill fol-
lowing associated rules during field experience in pre-
schools (Bodrova & Leong, 2003; Hostettler Scharer,
2017; Vygotsky, 1978).
Play-based activities have the potential to be highly
motivating among higher education learners (Boysen
et al., 2022; Jørgensen et al., 2023). One argument is that
exposing preservice, early childhood teachers to play and
playfulness in the design of their teacher education class
activities can give them an enhanced appreciation of the
benefits of play and playfulness in teaching and an under-
standing of how such approaches may be experienced by
learners (Boysen et al., 2022; Galbraith, 2022; Kemple
et al., 2015). Notably, play is critical to cognitive and
socio-emotional development in early childhood (Ashiabi,
2007; Hurtado-Mazeyra et al., 2022; Vygotsky, 1978).
Play contributes to such development by encouraging
children to engage in semiotic mediation and by inviting
children to adopt/use rules inherent to the culture to
which the children belong (Duncan & Tarulli, 2003;
Vygotsky, 1978). Within sociodramatic play, semiotic
mediation is inherent in the object substitution by which
children use a stuffed animal to refer to a person, or a
building block to represent a phone (Guo & Mackenzie,
2015; Ma, 2014). That is, children use cultural knowledge
to imagine a possible world, the entities within that world,
and what can be used to represent the different entities. In
this way, they begin to engage in the type of symbolic rep-
resentation that is inherent to logic. They also need to
negotiate their imagined world and the semiotic media-
tion used to create that world with their peers. Play-based
activities also tend to be more intrinsically motivating
than other activities for early learners (Duncan & Tarulli,
2003). As such, play is central to early childhood educa-
tion. Preservice early childhood teachers are prepared to
base curricula and lesson plans around play; indeed, early
childhood is often called the play years (Galbraith, 2022).
Still, not all early childhood education is entirely play-
based; pressure from parents, policy and standards often
leads many early childhood educators to choose a more
didactic approach (Bubikova-Moan et al., 2019; Stipek &
Byler, 1997). Some research indicates that with an
enhanced appreciation and understanding of play, preser-
vice early childhood teachers are more likely to commit to
integrating play within their teaching (Jung & Jin, 2015).
Embedding collaborative play in collaborative pro-
graming could invite new conceptions of robots and
robot programing. Considering that ‘how people and the
things that they create help to shape the ways in which
they and others view the world’ (Smagorinsky, 2007, p.
62), it was expected that teachers’ thinking about robots
for children’s collaborative play would shape robots and
robot programing. Besides, play is essential to young
children’s learning, but also integrating robots as co-
players in imagined situations that include roles and rules
is aligned with dramatic play (Bodrova & Leong, 2003;
Hostettler Scharer, 2017; Vygotsky, 1978).
Playful programing can also help women engage more
fully within programing (Regal et al., 2024; Tellhed
et al., 2023). This is critical as the vast majority of early
2SAGE Open
childhood teachers are women (Saluja et al., 2002; Van
Laere et al., 2014). When engaged in collaborative pro-
graming tasks, women often assume roles that are stereo-
typically associated with females, such as documentation
and project management (Fowler & Su, 2018). Engaging
in such aspects does not positively predict persistence in
CS and information technology majors (Weston et al.,
2019). But when workload is evenly distributed, colla-
borative programing can be helpful for women program-
mers (Werner et al., 2004). One way this can help is by
inviting women to see programing as a collaborative
activity, thereby countering the stereotype of CS as a
solitary profession (Werner et al., 2004). Collaborative
programing can also lead to less programing errors
(Bravo et al., 2013) and higher quality projects
(McDowell et al., 2002; Nagappan et al., 2003; Preston,
2005) than solo programing. This is especially important
among women students who often attribute struggles
with CS tasks to lack of ability (Koch et al., 2008).
Collaborative programing is often seen as more enjoy-
able than solo programing, especially among non-CS
majors (Boyer et al., 2008; Nagappan et al., 2003).
Resilience
It is also important to consider the role of playfulness on
cognitive and motivational outcomes within teacher edu-
cation courses themselves. Resilience in the face of
adversity is a critical attribute among university students
(Brewer et al., 2019; Chua et al., 2023; Turner et al.,
2017), and global incidence of low resilience among uni-
versity students was estimated to be 36% in a systematic
review (Chua et al., 2023). Resilience can be defined as
positive adaptation in the face of adversity (Luthar &
Cicchetti, 2000; Southwick et al., 2014), and can explain
mental health and academic outcomes (Luthar &
Cicchetti, 2000; Turner et al., 2017). Playfulness can con-
tribute to both resilience and creativity (Heljakka, 2023).
Self-efficacy also plays a key role in explaining/predicting
resilience (Beltman et al., 2011; Yada et al., 2021). When
non-CS majors learn to program, they often face fears
regarding their abilities to understand and master the
content and to learn to program competently (Hogan
et al., 2023; Shell & Soh, 2013). This can result from
struggles producing adequate code to solve a problem,
and struggles in identifying and solving bugs (Gorson &
O’Rourke, 2019). These struggles can lead such learners
to have low programing self-efficacy, especially consider-
ing that mastery experience is the strongest contributor
to self-efficacy (Bandura, 1997; Usher & Pajares, 2008).
The vast majority of preservice early childhood teach-
ers are women (Saluja et al., 2002; Van Laere et al.,
2014); stereotypes about computing (e.g., that computer
scientists are almost always men who work long hours
alone) can turn women away from computing (Cheryan
& Markus, 2020; Koch et al., 2008; Main & Schimpf,
2017). Women are likely to have faced lacks in curricu-
lum and encouragement to pursue CS throughout their
education (Ryoo, 2019). Resilience in the face of fear and
lack of confidence is especially critical among women of
color who are learning to program in ‘White-male domi-
nated spaces’ (Williams et al., 2024, p. 21:3).
Incorporating play within university courses may be a
promising method to foster playfulness and resilience
among university students (Magnuson & Barnett, 2013).
This is important because while didactic resilience train-
ing focused on psychological constructs (e.g., mindful-
ness, positive psychology) exists (Brewer et al., 2019;
Kunzler et al., 2020), evidence for effectiveness of such
interventions on resilience or mental health improvement
as assessed by meta-analytic methods is mixed (Kunzler
et al., 2020). Play may allow teacher education students
to engage in imaginal coping; for example, to sing instead
of feel distress, frustration or despair (Clark, 2016).
Especially as they are learning computer science, many
challenges can emerge, as content being learned is novel
and bugs prevent successful execution of code (McCauley
et al., 2008). Singing can lead to physiological changes
that contribute to enhance resilience (Kang et al., 2018).
Research Question
How and why do preservice early childhood teachers
exhibit resilience during collaborative programing?
Method
Research Design
This case study (Stake, 1978) was designed to elicit an in-
depth understanding of participants’ programing and
debugging experiences. The unit of analysis was a class in
which early childhood education (ECE) preservice teach-
ers learned to program robots over the course of a 3-week
unit. We used a phenomenological lens to ask ‘[w]hat is
the nature or essence of the experience of learning’ of this
particular group of programing learners (Van Manen,
1990,p.10).Wewerespecificallyinterestedinthemean-
ings they ascribed to their lived processes of collaborative
programing and reacting to challenges. Data included a
pre-survey about motivation and experience related to
STEM, robotics and programing, classroom videos,
reflections, and interviews. Analysis strategies included
open and axial coding and conversation analysis.
Study Context and Participants
Participants were 14 preservice, ECE majors enrolled in
an arts-based early childhood education course in a large
Kim et al. 3
university in the southeastern United States. They
worked in pairs during a 3-week programing unit. All
were female. The average age was 19.79 years. Two were
African Americans, one was Asian American, one was
multiracial, and the rest were Caucasians. Nine indicated
no to low prior programing knowledge and five indicated
intermediate. Six were video-recorded and five partici-
pated in individual interviews. Table 1 lists demographic
information and data completion. All participant names
have been changed to pseudonyms.
Robot Programming Learning Unit
The overall design of the unit was grounded in the con-
ceptual framework of the study. Collaborative play and
collaborative programing were embedded throughout the
unit. Collaborative teams consisted of teaching partners
who went to preschools together for field experience.
Robots were portrayed as co-players in dramatic play
not only in programing activities but also in lesson design
activities for their field experience preschool classrooms.
The robot programing unit was comprised of three 3-
hour classes and two sessions of field experience within a
preschool. In Class 1, participants were presented with
an overview of the history of educational robotics and
learned basics of coding using Hour of Code and
Ozoblockly. They also learned in pairs to teach with a
sample lesson that involved dramatic play; in it, robots
represented vegetables and needed to navigate through a
supermarket to get to the section that is consistent with
the type of vegetables (i.e., leafy green for the spinach
bot or root vegetable for the carrot bot). The sample les-
son included a song that they could sing based off of the
muffin man song: ‘Do you know the carrot bot, the car-
rot bot, the carrot bot, do you know the carrot bot who
lives in the root vegetable section? Yes, I know the carrot
bot, the carrot bot, the carrot bot, yes I know the carrot
bot who lives in the root vegetable section.’’ They were
provided with the supermarket map and Ozoblockly
code that programed the robot to get to its correct sec-
tion (by skating past the frozen food section because it
was freezing cold, speeding up past the snack section to
Table 1. Participant Data Summary.
Team Pseudonym
Video
recording Interview Age Race/ethnicity
Computer programing
knowledge
Prior robot programing
experience (context)
1 Zoey Yes Yes 19 Caucasian No knowledge Yes
(Undergrad ed tech class:
Ozobot)
1 Joy Yes Yes 20 Caucasian Intermediate Yes
(Undergrad ed tech classes:
Scratch, Makey Makey, Ozobot)
2 Mia Yes Yes 19 African American Intermediate Yes
(Middle school: Lego
Mindstorms, robotics class,
engineering program)
2 Ellen Yes No 20 Hispanic and
Caucasian
No knowledge No
3 Luna Yes No 20 Caucasian Low No
3 Liz Yes No 20 African American Low No
4 Pam No Yes 19 Asian No knowledge No
4 Ava No No 19 Caucasian No knowledge No
5 Haley No No 20 Caucasian Intermediate Yes
(Undergrad ed tech classes)
5 Belle No No 20 Caucasian Low Yes
(Undergrad ed tech class)
6 Lucia No Yes 21 Caucasian Intermediate Yes
(Undergrad ed tech classes:
Scratch, Makey Makey, Ozobot)
6 Stella No No 21 Caucasian Low No
7 Cara No No 20 Caucasian Low No
8 Moira No No 19 Caucasian Intermediate No
Note. Teams with only one row mean that the participant’s partner in that team was a non-participant. Programing knowledge is self-assessed prior to the
robot programing unit began in the present study. The undergraduate ed tech course title is blinded. Based on participants’ other data sources, the listed
Ozobot experiences were from using color marker coding, not from block-based coding. There is no other data about Scratch and Makey Makey
experiences (e.g., if each indeed included a robot element).
4SAGE Open
avoid unhealthy food, and spinning and displaying fire-
works in the root vegetable or leafy green vegetable sec-
tions; see Figure 1). They learned to code their robots
and run them through the supermarket map. They prac-
ticed the lesson and then taught it in their field experi-
ence preschool classroom with their carrot and spinach
bots during Week 1.
The lesson used in Week 2 was designed by the parti-
cipants in pairs. Each pair created their own lesson, pro-
gramed their robot’s movements needed to perform
dramatic play with preschoolers within the lesson
together, and taught the lesson with the programed
robots in field experience. In Class 2, before designing
their own lesson, they worked on a series of programing
practice activities. For example, one activity was to pro-
gram the robot to move at different speeds per its top
light color. Programing practice activities were done in
association with potential classroom activities for chil-
dren to play with robots. This way, robot programing
involved dramatic play not only between participants
and their robot but also within their preschool class-
room. Participants were invited to look up example code
(e.g., Figure 2) when needed, and create alternative code
if they looked up the code. The design of this series of
programing practice activities was also to create natural
opportunities for tinkering and playful exploration but
also for their design for play among preschoolers in field
experience. In Class 3, participants were introduced to
other educational robots such as Lego Mindstorms,
Lego WeDo, Ozobot Evo, Dash & Dot, and Roborobo
for them to play with.
Data Collection
At the beginning of the unit, all participants took a pre-
survey covering motivation and experience related to
STEM, robots, and programing. The survey was used in
prior research (Belland et al., 2021), subscales of the sur-
vey received Cronbach’s alphas ranging from 0.7 to 0.95.
Three paired groups were video-recorded in three classes,
and five participants were interviewed after the unit
ended (see Table 1). All participants responded to reflec-
tion prompts. Reflection prompts were designed to guide
participants’ reflection-in-action and reflection-on-action
in each class (Umutlu & Kim, 2020; Scho
¨n, 1987).
Prompts for reflection-in-action were given in the middle
of programing and those for reflection-on-action about
programing were given at the end of each class. Three
sets of prompts were given in Class 1 and Class 2. Two
sets of prompts were given in Class 3. Three to five
prompts were given per set. Responses were collected
individually so even when the pair was attempting to
debug the same robot together during collaborative pro-
graming, their responses were not the same. Reflection
prompts included ‘What do you like about working with
ozobots? Why?’ ‘‘What do you find frustrating about
working with ozobots? Why?’’ ‘What did you find the
most challenging about working with ozobots today?
Why?’’ and ‘Explain what you did to address the chal-
lenges.’ Prompts for reflection-on-action about their
Figure 1. Example code (left) that programed the robot to navigate through the supermarket map (right).
Figure 2. Programing practice activity example code.
Kim et al. 5
field experience teaching were given in the beginning of
Class 2 and Class 3.
The length of interviews ranged from 16 to 24min-
utes. The semi-structured interview questions asked par-
ticipants to talk about the processes of collaborative
programing, play, and teaching, and the challenges that
they encountered and resolved. Depending on specificity
of responses, further questions were asked. Example
questions are ‘What problems did you encounter when
programing your robot?’ ‘‘How did you handle the
problems that you ran into?’ ‘‘How did you know that
was the problem?’ and ‘‘How did you find the cause of
the problem?’
Data Analysis
Interviews and classroom videos were transcribed, and
imported into NVivo 12 along with reflections for analy-
sis. An initial coding scheme was constructed by the first
author based on her open coding of transcripts and liter-
ature related to collaborative programing, debugging,
and tinkering (e.g., Kim et al., 2018; McCauley et al.,
2008), attributional theories including foci on resilience
and emotions such as playfulness and joy (e.g., Kim &
Pekrun, 2014; Weiner, 1985), and stereotypical concep-
tions about CS and other disciplines (e.g., Cheryan,
Meltzoff, & Kim, 2011; Cheryan, Siy et al., 2011). The
coding scheme was revised based on discussions between
the first, second, and third authors. Then, the third
author used the revised coding scheme to code two inter-
view transcripts, three reflections, and one classroom
video. The first and second authors reviewed the third
author’s coding independently, and then discussed the
coding and revised the coding scheme with the third
author. The third author applied the re-revised coding
scheme to another set of an interview transcript, reflec-
tions, and a classroom video, and the three authors dis-
cussed the process once more and finalized the coding
scheme. Sample coding scheme nodes are listed in Table
2. Then, the third author coded all classroom videos,
interviews, and reflections, and the first author reviewed
all coded data to reconcile disagreements with the third
author. The second author randomly selected 5% of the
coded data and reviewed it independently to check for
agreement. The three authors met again to reach consen-
sus, and the third author updated his coding using nodes
based on the last consensus discussion. After completion
of coding, the first, second, and third authors went
through multiple rounds of meta-coding of coded data.
That is, a list of salient observations was created upon
the reviews of NVivo documents produced per coding
scheme node and the list was discussed. The list was
revised until a set of themes emerged. Themes were con-
tinuously refined while writing the present paper and
revising the coded data. This process involved cognizant,
constant efforts in describing the participants’ lived expe-
rience with collaborative programing through a phenom-
enological lens (Van Manen, 1990).
Findings and Discussion
We found that preservice early childhood teachers exhib-
ited resilience during collaborative programing due to the
following three reasons:
1. Through playful coding, preservice early child-
hood teachers learned that computer science is
approachable and fun
2. Preservice, early childhood teachers engaged in
adaptive attribution
3. Preservice, early childhood teachers engaged in
joint celebration when they observed each other’s
successes during collaborative tinkering
In the remainder of this section, we explain, provide
evidence for, and discuss each theme.
Theme 1: Through Playful Coding, Preservice Early
Childhood Teachers Learned That Computer Science
is Approachable and Fun
Zoey began the programing unit believing that computer
science would be too difficult for her. Such a conception
seems to have resulted from no early exposure to com-
puter science. As she explained during the interview, ‘I
grew up not doing any of that [coding] and I just thought
coding was really difficult and that I would never be able
to do it.’’ Later her stereotypical conception about com-
puter science seemed to have disappeared, as hinted in
her other interview comments below:
It [coding] was fun for me because, well, first it’s easy. When
you hear the word coding, I had never had much experience
with it before. And you see the long game, like codes that
don’t even look like English. But with Ozoblockly it’s just
block coding, you’re just dragging blocks and it’s a lot easier
than what people think. That’s why I liked it and it’s fun to
be able to see the robot do what you program it to do. And
I also like it because it’s easy for kids and fun for kids.
This elimination of her stereotypical conceptions about
computer science was observed also with Zoey’s
improved self-efficacy. Her reflections depict progres-
sively more positive sentiment as she succeeded in colla-
borative debugging in class. While challenges were
reported in all her reflections (note: reflection prompts
asked about the challenge that they were experiencing at
the moment and those they experienced in each class),
her excitement about observed success in her team robot
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was recorded at the end of each class. For example, as
listed in Table 3, Zoey noted the challenge of inconsis-
tency in robot behaviors in her reflection-in-action (i.e.,
reflection prompts were given in the middle of debug-
ging) in both Class 1 and Class 2 but her notes contained
less frustration in Class 2 than Class 1. She also engaged
Table 3. Excerpts from Zoey’s Reflections.
Class 1
Reflection-in-action
Class 1
Reflection-on-action
Class 2
Reflection-in-action
Class 2
Reflection-on-action
‘‘Another challenge was keeping
it from veering off the page
and staying on the path.’’
‘‘It was most challenging to
figure out which movements
to change and keep.’’
‘‘We first struggled making the
ozobot respond to colored
lines.’’
‘‘The biggest challenge was
keeping the robot from
moving and pushing the puzzle
pieces.’’
‘‘It was frustrating having to
keep re-load[ing] the ozobot,
and it wasn’t always
consistent.’’
‘‘The most exciting moment was
finally getting the ozobot to
follow the right path.’
‘‘The ozobots can sometimes be
inconsistent.’’
‘‘It was exciting to see the robot
complete the shapes that we
made.’’
Table 2. Sample Coding Scheme Nodes and Sample Excerpts.
Framework components Sample nodes Sample excerpts
Play and playfulness Collaborative playful
exploration
Mia: We’re getting closer. This is good. This is happening. This is
real. This is me.
Ellen: Exactly.
.
Ellen: Boom.
Mia: We’re killing it.
Ellen: We’re geniuses.
Mia: Obviously. Oh, that was good!
Playful fun Zoey: It [coding] was fun for me .it’s fun to be able to see the
robot do what you program it to do. And I also like it because
it’s easy for kids and fun for kids.
Collaborative programing,
debugging, and tinkering
Looked at the robot immediately after
noticing the problem
Lucia: ‘‘When it didn’t do exactly, I think we first looked at the
robot just because we were, oh, something’s wrong with this.
But then we would go on to realize, okay, well maybe it’s in the
coding, maybe there’s something in the coding that we can fix.’
Reviewed only the suspected area(s) in
the code
Zoey: ‘‘We would look at its [the robot’s] movements first and
then just based on where it was going or how far it was
turning. That’s what would make us decide to change it to a
different movement or different turn [in the code].’’
Resilience Persistent behaviors or utterance Mia: ‘‘Okay. Yes. Then you turn. Yes. Yes, yes. Skate around a
little bit. Turn left. No! Ah, I see what the issue is.See this
whole skating thing is so inconsistent. I don’t know what to do
about this stuff. I’m gonna try this again.’
Non-persistent behaviors or utterance Ellen: ‘‘Maybe she’s not picking up that it’s red. And I don’t know
how. I’m giving up. We did the code right and it’s just not
working.’’
Stereotypical conceptions Stereotypical comments Belle: ‘‘Robot makes me too anxious, I’m not good with tech’’
Lucia: ‘‘.I’d never really thought of myself as somebody that
could do programing.’’
Attribution to the success
in programing
Collaborative programing Pam: ‘‘The program itself was very simple and I had my friends
around me if I was stuck.’’
Adaptive attribution Mia: ‘‘I think if you’re more interested, you’re probably going to
be more talented. .I don’t think that there’s anything that
makes people innately better at doing programing. I just think
that if you’re patient, and willing to take the time to try and
figure it out, then you’ll be fine.’
Self-efficacy Lucia: ‘‘It [my programing] was definitely better than I expected.’’
Kim et al. 7
in reflections on mastery experience. For example, she
noted, ‘‘The most exciting moment was finally getting
the ozobot to follow the right path,’’ which suggests
growth in her self-efficacy considering that mastery expe-
rience is a critical source in building self-efficacy
(Bandura, 1997; Beatson et al., 2018).
Similarly, Lucia’s stereotypical conceptions about
computer science appeared to dissipate as her self-
efficacy improved. In her interview, she said:
I thought I did better than I expected.. Just because I’d
never really thought of myself as somebody that could do
programming. I had done a little bit in the past, so I felt con-
fident and okay, I kind of knew what ozoblockly was, but I
hadn’t had much experience. (.) But it was definitely better
than I expected just because I had low expectations. (.)I
think everybody can do a little bit of it. Some people caught
on how to exactly program it faster than others, but I think
that it’s not a skill that some definitely have and some don’t.
Everybody can somehow use ozoblockly.
Joy explained changes in her conceptions about com-
puter science during the interview:
I figured coding and programming sound really hard and
really challenging and I had never done anything like it
before other than the [name blinded, another course in
which ozobots were introduced] class. So before that, I
didn’t think that that was something that I could do. So
then I liked to use these [coding] programs that were so easy
and user friendly. It was really nice.
In the pre-survey, Joy listed Scratch, Makey Makey, and
Ozobot as what she learned from the general educational
technology course that she had taken prior to the present
study. Based on other data sources from the participants
who took the prior course (see Table 1), it is inferred that
the prior course taught students to use color marker cod-
ing without computer use to control Ozobots. For exam-
ple, Moira noted in her Class 1 reflection, ‘It’s
something I’ve never done before.’ Lucia said, ‘I had
done a little bit of programing in the past (.) I kind of
know what ozoblockly is, but I hadn’t had much experi-
ence.’ And Zoey said, ‘‘I had never had much experience
with it.’ However, it should be noted that data about
these participants’ experiences from the prior course is
limited because only three of these five participants did
the interview and the semi-structured interview questions
were not designed to ask about the prior course specifi-
cally. Consequently, relevant data were insufficient to
make full sense of the participants’ prior programing
self-assessment data which ranged no knowledge to low
and intermediate knowledge. Joy self-assessed her prior
programing knowledge as intermediate prior to her
engagement with the programing unit in the present
study. All her reflections from Class 1 to Class 3
included a notion about ease of robot programing. For
example, in Class 1, she noted ‘They are fairly easy to
program and fun to use.’
Changes in conceptions about computer science did
not seem to result simply from exposure to programing
(also considering that six participants were exposed to
block-based coding prior to the present study); rather,
the method of integrating programing into the course
seems to have played a critical role. That is, the robot
was introduced as a co-player in dramatic play in which
young children can engage. For example, the sample les-
son that the participants learned to teach in their pre-
school field experience with their robots aimed to create
a dramatic play context in which children helped the
robots navigate a supermarket as the robots looked for
their home (i.e., the carrot bot was trying to find the root
vegetable section and the spinach bot was trying to find
the leafy green vegetable section). Robot programing in
the present study embodied objects and activities such as
dramatic play that were not stereotypically associated
with computer scientists. These design elements in the
unit may have impacted our participants stereotypical
conceptions about programing, considering that learning
environments without objects that convey CS stereotypes
can positively influence CS interests of female undergrad-
uates (Cheryan, Meltzoff, & Kim, 2011). When Zoey
reported that she did better with programing than she
had expected prior to the unit, she pointed out that her
unexpectedly positive experience with robot programing
resulted from the connection of programing to preschoo-
lers’ play and learning:
I think that was the coolest part for me and that kind of sur-
passed my expectations to see how you can incorporate tech-
nology like that [robot programming] into a lesson even for
four-year-olds and then bring it into the classroom.
In contrast, Belle appeared to retain her stereotypical
conception. In her Class 2 reflection, Belle said,
‘‘Technology stresses me out’ and in Class 3, she noted
‘‘Robot makes me too anxious, I’m not good with tech.
Also, I don’t find it interesting so I don’t think I would
teach it well.’ Despite such comments, Belle highlighted
the robots being user-friendly and cute in her Classes 1
and 2 reflections and ‘‘how excited the kids get when
they see them [robots]’ in her Class 3 reflection. Belle’s
partner, Haley, seems to have become self-efficacious.
While she self-assessed her programing knowledge prior
to the unit as intermediate, she reported her struggles
with robot programing in her Class 1 reflection and
expressed her desire to become proficient. In her Class 2
8SAGE Open
reflection, she reported no challenge by saying ‘not
really any problems, just a little bit of trial and error to
get it perfect.’ In her Class 3 reflection, she indicated
that there was nothing else she needed to learn and said,
‘‘I feel like the block programing is pretty easy to use.’
Neither Belle nor Haley was interviewed or video-
recorded so there is no other data to further discuss their
stereotypical conception. Given Belle’s reflection about
Haley’s help during debugging (‘‘I had my partner dou-
ble check my code after I fixed it’ in Class 1), it is prob-
able that their collaborative programing and play
facilitated Belle’s positive views on robot use in ECE
classrooms despite her dislike for technology.
This does not mean that programing went well all the
time. Joy encountered multiple bugs and had struggles
with them (see the episode in Theme 2, e.g.). In her Class
1 reflection, Joy noted, ‘The ozobot wouldn’t follow the
same path even with the same programing.’ She also
noted in her Class 2 reflection, ‘I tried re-coding but I
still had the same problem with it not following the same
path every time.’ During the interview, she noted tinker-
ing during debugging that had to be done as part of
programing:
Sometimes it [the robot] doesn’t do exactly what you code it
to do. So when it turns and you have it do a slight turn, it’ll
do a slight turn, but then when you have it do a left turn,
it’ll still not do a full left turn. So I had to figure out how to
code it, maybe do a turn and then a slight turn to try to get
it to do what I wanted it to do. But other than that, I mean
it worked pretty well.
As observed often in her collaborative debugging with
Zoey, much of the inconsistency in robot performance
they experienced resulted from inconsistency in where
(e.g., where exactly within the supermarket store map in
Class 1) the ozobot began its movement. This seems nat-
ural considering the inherent complexity in the robot pro-
graming unit that required the participants not only to
learn to program robots based on the given or designed
choreographies of robots but also comprehend the chor-
eography within their field experience context teaching
preschoolers. In fact, Joy expressed her desire to learn to
fully understand Ozoblockly in her reflection-on-action
during the last day of the unit.
Despite the difficulties she encountered, Joy reported
in the interview that she programed better than she had
thought and attributed her success to her ability with
technology:
I’m pretty good with technology. That was always some-
thing that my mom said, yeah, you need to go into
technology because you’re so good at it. Just figuring out
problems and stuff. So I think that could have helped.
Joy’s attributional notion is interesting in that she attrib-
uted her successful programing to her ability with tech-
nology only after the unit. She also cited verbal
persuasion received from her mom as a factor in her
assessment of technology ability (Bandura, 1997). She
said in her interview anybody can do it because it is easy.
Nonetheless, she appeared to believe that there are peo-
ple who learn more quickly than others:
It’s so straightforward. I think maybe some people would
grasp it quickly, more quickly. But I don’t think.Imean
it’s so easy to get and so straightforward that I think pretty
much anyone could do it.
Moira wrote in her Class 1 reflection that ‘‘The
thought of coding seems so difficult, but it really
wasn’t.’ Later in the unit, the class was offered the
opportunity to write their own code. Regarding this part
of the unit, Moira wrote in her Class 2 reflection that the
most exciting thing was ‘creating my own [code], even
though it was hard.’’ Moira was aware of the increasing
difficulty in coding tasks but despite the challenge, writ-
ing code from scratch was still exciting for her. She was
not concerned about difficulty as something that would
stop her from doing the activity any longer. Her changed
stereotypical conception about computer science was
related to her new self-efficacy. Changes in stereotypical
conceptions about computer science through exposure to
block-based programing have been previously observed
elsewhere (Kim et al., 2015). Likewise, most participants
in the present study became comfortable and self-
efficacious as they learned block-based programing;
thus, their stereotypical conceptions about computer sci-
ence became too obsolete to associate computer science
with special groups of people. As Pam noted in the inter-
view, even those who cannot read yet can program:
This program specifically, I think anyone can do it, espe-
cially younger kids. It’s not very hard. Even if they can’t
even read what the words are saying, they can use the mouse
or the tablet and move the pieces together.
The pre-reader level of the platform was not used in the
present study, but ‘the thought of coding’ that Moira
had as well prior to the unit (see her Class 1 reflection
above), seemed to have changed as she became self-effi-
cacious. Pam’s increased self-efficacy may have led to
her desire to incorporate more robot moves into her
teaching as expressed in her Class 3 reflection from the
Kim et al. 9
last day of the robotics unit: ‘I want to learn to program
more [robot] dances or if there is a jumping feature I
could use.’
Theme 2: Preservice, Early Childhood Teachers
Engaged in Adaptive Attribution
Attributional factors mentioned during interviews as suc-
cess enablers were observable in the participants’ actions
during debugging. In other words, what they did was
aligned with what they thought would help them do well
with programing. During the interview, when asked what
would take to be good at programing, Zoey responded:
I think it’s more of an interest thing.. I think if people
don’t have that interest then they might not build up those
[programming] skills. .And people who are really inter-
ested in it, I mean they’ll be spending more time doing it
and I think in the end they might have more knowledge and
more skills to do it.
Zoey thought that time investment based off interest
would lead to success in programing. Such attribution is
adaptive in that the locus of control for both time and
interest are internal in contrast to the external locus of
control for luck, for example (Weiner, 1985).
Considering that the interview was conducted after the
robot programing unit, her adaptive attribution may
have resulted from her removed stereotypical conception
about computer science being only for special groups of
people because of its difficulty. After all, she believed
that to be good at programing, one does not have to
have special talent; rather, time investment matters. Joy,
Lucia, Mia, and Pam also highlighted the ease of block-
based programing in their interview (see Theme 1).
This finding related to adaptive attribution is mean-
ingful not only for teacher learning but also for com-
puter science education because such a growth mindset
could be cultivated in teachers possibly through block-
based programing experience. And growth mindsets
could become a part of the school cultures to which these
teachers belong. Growth mindset as a culture is critical
(Dweck, 2014). Fundamental beliefs of teachers are
impactful in their teaching practice (Kim et al., 2013).
Thus, if future teachers have a growth mindset about
computer science, they are likely to invite all students to
computer science, including those from underrepresented
populations in computer science. Note that women are
underrepresented in computer science (Beyer, 2014). A
recent study showed that teaching with a growth mindset
led to students’ development of interest in computer sci-
ence (Burnette et al., 2020).
Aligned with her adaptive attribution that successful
programing comes with invested time, Zoey did not
give up in the middle of programing, as illustrated in
the following dialog with her partner Joy during debug-
ging. Zoey’s utterances were often negative as shown in
the debugging episode below (emphasis added to her
negative utterances). When attribution to success in
programing was adaptive, even when participants
wanted to give up and move on, they still continued
their work.
Zoey: We have to calibrate it every time. It’s a pain!
Joy: I hate it. Every time you change something on
this, even a little bit, you have to do everything. It’s
recalibrate, load it which takes forever. It’s rolling.
Zoey: [inaudible] It turns at a 45-degree angle. I cannot
get [it] over to the snack section. This stupid thing!(She
tried to get her robot to skate past the snack section on
the supermarket map)
Joy: It’s because of the skate thing. (She referred to
the skate block, which made the robot move forward in
a wavy pattern)
Zoey: The skate thing messes it up. It doesn’t look like
it says it skates forward but it turns. (She saw that the
robot turned instead of skating)
Joy: But then see it’s supposed to run through the
snack section. Okay, so it starts going on this skate.
Zoey: I’m done with this. I don’t like this.
Joy: Okay so after the skate.
Zoey: What do you have?
.
Zoey: Okay. Try it again. This takes forever.
Joy: Here it goes. Wow, I did it. I did it.
Zoey: What? What else did you change?
Joy: I made it not walk as far as over here and then
[inaudible].
Zoey: [inaudible]
Joy: It didn’t do it that time. Why didn’t it do it?
That’s annoying. Just turn it off and on again. See if it
worked. Slight left. Ok that was good I guess.
Zoey: Yeah that was good.
Joy: That’s really annoying.
Zoey: Now it looks like instead of a slight left, it took
an actual left. But originally.(She referred to lack of
precision in robot turn angles)
Joy: But it didn’t. Let’s do it again. Here we go.
Zoey: That seems good enough to me.
Zoey had good reason to be frustrated also as explained
in her interview: ‘‘My least favorite part was probably
just how it could be a little frustrating, like with the
inconsistencies.’ The inconsistencies were pointed out by
other participants. For example, Cara’s reflections listed
the robot ‘‘being so sensitive’ as a key challenge of
debugging. Despite challenges from inconsistencies,
10 SAGE Open
Stella, Cara’s partner, reported persistent actions like
Zoey’s in her reflections:
The most difficult part is calibrating the bot because some-
times it said it worked when it actually didn’t. It was also
hard to make it go exactly where you wanted it to go. I
would just keep trying to calibrate it until it worked and I
would position it differently to try to let it end up in differ-
ent spots.
Still, less negative emotions are likely to be experienced
when one has adaptive attribution (Kim and Pekrun,
2014) but Zoey expressed frustration throughout this epi-
sode of debugging and still pushed herself to keep going
despite constant frustration. A study (Garcia & Rime
´,
2019), albeit outside of educational research, found that
venting negative emotions in a community was associ-
ated with social resilience within the community. It
should be also noted that collaborative programing part-
ners also taught using their robot lessons together during
field experience in the same preschool classroom for the
entire semester as described in the method section. Also
in Mia’s case, she had adaptive attribution about success-
ful programing (i.e., time investment) and her persistent
actions were aligned with it but not with her utterances,
like Zoey and Joy as shown in the following debugging
episode (emphasis added).
(When Mia noticed the robot was not following the intended
pathway, she steered it with her hand to correct its move-
ment) Mia: I give up with you. I just give up. I give
up(Mia stopped working with the robot.
Mia sang a song from the movie Moana, in which the
namesake female character laments not being able to
pursue her own wishes, and ponders how far she will go
to find her way in the world).
(Mia worked on the Anna and Elsa (Frozen) themed
task in code.org that entailed using the move forward
block to help Elsa walk and create a line of ice).
Mia: I did it. Oh my God. Oh my goodness. Wow.
That was the best moment of my life. Wow. The best
moment of my life (She talked about her successful
completion of the first coding task in code.org). Wow,
Elsa. That was amazing. Lesson number one. (unre-
lated talk)
.
Mia: How did you even end up turning that way? How
does this make any sense? What are you doing? What
the heck? Oh my gosh, (Mia was confused about output
on the screen). Ten times, that’s so much. (Mia talked
about 10 loops in the code). (Mia sang). Oh that’s a
lot. Look at her go. Get it, Elsa, you going.
The multiple debugging episodes shown above suggest
that, for Mia, singing was a way of fostering resilience
(Clark, 2016; Gunnestad, 2006; Kang et al., 2018). It
may be possible to integrate singing into scaffolding for
problem-solving with uncertainty. Especially in early
childhood education contexts, such a scaffolding strategy
can be integrated seamlessly considering private singing
is often observed among children and used by teachers
(Thibodeaux et al., 2019). Managing uncertainty is
important in collaborative problem solving (Jordan,
2015; Jordan & McDaniel, 2014). In her interview, Mia
reported that robot programing was not as easy as it
may seem when compared to other robots like Lego
Mindstorms. When asked how well she had done during
the robotics unit, Mia said she did only average even
though she had done some programing prior to the pres-
ent study. When a task is easy, it is not too difficult to
keep working on it; when difficult, especially more diffi-
cult than you thought, it is easy to quit (Weiner, 2010;
Wigfield & Eccles, 2000). Mia’s perseverance in action in
the midst of difficulties is aligned with her adaptive attri-
bution that highlighted time for effort.
Another participant, Ava, mentioned in her Class 2
reflection that persistence helped her overcome chal-
lenges during debugging, but she reported that chal-
lenges were not too difficult. No other data is available
to corroborate Ava’s notion. Further research is needed
to understand learners like Mia who still persist despite
difficulties beyond their comprehension. Learning to
work through difficulties is essential not only to CS edu-
cation but also to any other educational discipline. It
seems logical to think that teachers with such a quality
would facilitate perseverance in children. Research on
action control and volition, that explains effort made
where things seem unattainable (Kim & Bennekin, 2016;
Corno, 1993; Heckhausen, 2007; Keller, 2017; Kuhl,
1987), could be applied to CS education.
Theme 3: Preservice, Early Childhood Teachers
Engaged in Joint Celebration When They Observed
Each Other’s Successes During Collaborative Tinkering
All participants reported successful trial and error. For
example, Zoey’s Class 1 reflection corroborates her use
of trial and error. When prompted to explain how she
addressed challenges in making the robot do what she
wanted it to do, she wrote ‘‘I made small adjustments to
see which movements worked best at keeping it on the
path. I mostly just used trial and error to see what
worked best.’ It is not unusual to see trial-and-error
(e.g., Fitzgerald et al., 2010; Grigoreanu et al., 2006;
Murphy et al., 2008) especially among novice
Kim et al. 11
programing learners. This is often the case in the block-
based programing contexts (Kim et al., 2018, 2022).
Ozobot Bits were less precise than ideal, but inconsisten-
cies in robot performance also resulted partly from
where robots were positioned on the supermarket map
as briefly discussed earlier (see Theme 1). In her Class 1
reflection, Mia reported, ‘I had to try and change the
algorithm so I could get my intended target.’ At times,
physical manipulation (e.g., maintaining the first posi-
tion of the robot in all trials) was sufficient to debug, but
missed. Interestingly, Belle’s Class 1 reflection pointed
out the imperfect process of ‘adjusting the starting
point.’
Enjoyment from collaborative explorations and dis-
coveries and expectation for such enjoyment seemed to
drive repetitive cycles of tinkering as depicted in dis-
course between Joy and Zoey during debugging (see
Theme 2), and in Mia and Ellen’s discourse below. Here,
Mia and Ellen debugged the robot to visit different ani-
mals and light up based on the animal color such as
brown for kangaroos, green for alligators, and pink for
flamingos. Emphasis is added to text relevant to the find-
ing discussed here.
Mia: Never mind this slight left, move, then slight left
again. We’ll just see if it works.(Mia proposed to keep
the code idea they already have)
Ellen: And then we want this to go really fast.
Mia: And maybe 10 steps.
.
Ellen: It will either totally work or totally fail.
[unrelated talk].
Mia: Did it work? Oh yes(She referred to loading the
code onto the robot)
Ellen: Let me take that one down a step. (After testing
the code, she realized the robot moved past the section
where it was supposed to stop)
Mia: Yeah, let’s see we go forward. Good. (The robot
correctly stopped in the elephant area). And then. Oh
nope. (The robot rotated and moved in the wrong
direction)
Ellen: That would’ve worked. I think it needs to move
forward before it rotates. Is [that] what we did?
Mia: Okay so 10 [steps]
Ellen: We need to put a move forward here. Maybe 1
or 2 steps
Mia: And then maybe make this one longer or maybe
nine perhaps? (She referred to the number of steps for-
ward within the move block). Because it was close.
Definitely close. (She meant the robot was close to
where they wanted it to be, but not quite there yet).
Ellen: Let’s just try 10 and see what happens.
Mia: We’re getting closer. This is good. This is happen-
ing. This is real. This is me.
Ellen: Exactly.
.
Ellen: Boom.
Mia: We’re killing it.
Ellen: We’re geniuses.
Mia: Obviously. Oh, that was good!
Vicarious enjoyment from seeing others’ discoveries
helped participants get going. Joint celebrations about
successful debugging played a role of verbal persuasion
that positively impacted self-efficacy (Bandura, 1997;
Usher & Pajares, 2008). A scene from Luna and Liz’s col-
laborative debugging in Class 1 exemplifies this (empha-
sis added):
Luna: Wow, yours is doing good (Luna said this when
Liz’s robot worked accurately).
Liz: For now. (Liz said this with laughter)
(Luna misplaced the robot on the map, which caused
it to move in the wrong direction. Then Luna cor-
rected her placement and re-tested her code.)
Luna: Yay! I think I need to do it again.
Liz: Oh Luna, it’s going!
Luna: Yes! Yes! It worked! It worked! It worked this
time.
Other debugging episodes, such as Zoey’s dialog with
her partner Joy in Theme 2, exhibited bidirectional ver-
bal persuasion that helped each other be resilient in the
face of their own struggles. Pam’s comment, ‘I had my
friends around me if I was stuck,’’ also hints at resilience
enabled through collaborative and social programing.
While it is often argued that trial-and-error should be
eliminated (Murphy et al., 2008), the same argument is
not applicable to the present study. Even when partici-
pants were frustrated with a prolonged period of debug-
ging, their trial-and-error appeared to enable social
resilience or vice versa. Also with this experience, teach-
ers will be able to notice such practice among children in
their future classrooms. With first-hand knowledge of
what it is, why it has to happen, and how it is done, these
future teachers’ capacity is expanded. Experiencing
enjoyment from collaborative explorations through per-
severance is not trivial as well. As reported earlier, when
facing success after struggles through trial and error,
Mia’s utterance depicts the joy of achievement ‘‘I did it.
Oh my God. Oh my goodness. Wow. That was the best
moment of my life. Wow. The best moment of my life.’
The present study adds to the evidence that trial-and-
error is not something that should be regarded as unpro-
ductive (e.g., Kim et al., 2021; Berland et al., 2013), but
it should be studied as part of a unique process of social
resilience during collaborative programing. Educators
still would want to minimize aimless or endless trial and
error; however, scaffolding is needed to allow sufficient
time to explore and make sense of collaborative
12 SAGE Open
experiences (Kim et al., 2021) during perseverance, dis-
covery, and enjoyment.
Overall Discussion
The study findings provide a sense of how preservice
early childhood teachers exhibit resilience during colla-
borative programing and collaborative play. Their resili-
ence was informed by playfulness (Boysen et al., 2022;
Jørgensen et al., 2023), and resilience (Luthar &
Cicchetti, 2000; Southwick et al., 2014). Taking a playful
approach allowed the preservice teachers to acknowledge
their struggles but also engage in playful coping (e.g.,
singing) to regulate negative emotions (Clark, 2016;
Kang et al., 2018). This resilience appeared to be associ-
ated with gains in self-efficacy and productive attribution
of success, and budding interest in and views countering
stereotypical conceptions of CS as they saw how it can
engage and facilitate the learning of young children. The
productive attribution and lack of unproductive attribu-
tion appeared to result from participants’ experience of
the course as a safe play space in which it was okay and
expected to make mistakes (Lyon & Clayton, 2021;
Pinkard et al., 2017). The budding interest appeared to
result from participants’ successful use of robots and
coding within field experience in preschools. This is
important because women are drastically underrepre-
sented in CS (Beyer, 2014; Weston et al., 2019), and sim-
ply including computer science content in P-12 settings
would not change this. Furthermore, many participants
cited the joy of the preschool learners in field experience
as influential to their persistence in learning CS. In this
way, playfulness both within the preservice classroom
and the preschool field experience classroom served to
promote resilience among the preservice teachers.
These findings are critical in that challenges and
opportunities reported here provide potential guidance
for improving teacher education for CS teaching. For
example, the finding that preservice teachers can come to
find CS to be approachable and fun through playful pro-
graming provides direction for CS teacher education
(Theme 1). Specifically, robots as co-players in dramatic
play in this study embodied CS in a way that discon-
nected programing from stereotypical activities of com-
puter scientists (Cheryan, Meltzoff, & Kim, 2011). It is
also important to note that programing in this study was
also connected to the intrinsic interests of our partici-
pants, non-CS majors, in teaching young children
through lesson design and field experience using their
programed robots. Further, negatively valanced utter-
ances during collaborative programing were not necessa-
rily a roadblock to successful debugging (Theme 2).
Rather, it was a way of coping with the encountered dif-
ficulties alongside peers in collaborative programing.
Singing also seems to have played a role in fostering resi-
lience (Clark, 2016; Kang et al., 2018). These findings
give insights for scaffolding that could be used with
novice programing learners, especially in teacher learning
contexts. Perseverance is valued in the present study and
thus, trial-and-error is viewed as having positive benefits,
especially where social resilience emerged through vicar-
ious success and joint celebration during collaborative
debugging (Theme 3).
Conclusion
As noted previously, the literature indicates that tinkering
can lead to strong programing learning outcomes (Kim et
al., 2021). At the same time, the existing literature did not
indicate the mechanisms by which tinkering can do so.
Also relevant is that the literature indicates that one of the
key impediments to programing learning especially among
non-majors is the persistence of stereotypes of program-
mers as white males who work long hours alone to design
and code programs (Cheryan et al., 2015; Wang & Degol,
2017). This stereotype is especially harmful to preservice
early childhood teachers, who are almost exclusively
women who have daily impact on children (U.S. Bureau of
Labor Statistics, 2023; Van Laere et al., 2014). In this
study, playful collaboration while learning to program
helped preservice, early childhood teachers dispel the
aforementioned stereotype, and begin to see programing
as a more inclusive endeavor. This is especially important
because early learners can gain tremendously if they are
taught CS by an enthusiastic teacher who can see all lear-
ners in CS careers (Di Lieto et al., 2017; Sullivan & Bers,
2019). This provides guidance for teacher educators in
how to prepare preservice teachers to teach CS. It is critical
to couch CS learning and teaching in play, such that youth
can learn CS in joyful manners and the stereotype of CS as
the domain of isolated white males can be dispelled. One
cannot simply prepare future high school CS teachers and
hope for the best. Rather, one needs to prepare early child-
hood and elementary teachers such that they can provide
the seeds and the baseline knowledge and skill to motivate
youth to consider CS as a career (Weintrop et al., 2018).
CS will be imbued in many lines of work in the 21st cen-
tury (Ng et al., 2023) and, as such, it is critical to expose all
learners to CS.
Conclusions of the study should be interpreted with
caution because not all participants completed the inter-
view and/or were video recorded. It was practically
impossible to record all participating pairs due to class-
room space constraints. All participants were invited to
participate in interviews but only five volunteered. Thus,
six participants had neither video nor interview data.
Also, little inference could be made regarding partici-
pants’ prior programing experience that may be interest
Kim et al. 13
of some readers. It should be noted that the purpose of
this case study is to particularize rather than generalize
(Stake, 1978).
As is well known, attributions are key to adaptive
motivation (Weiner, 1985). As predicted by attribution
theory, motivation for debugging was most adaptive
when participants attributed success to their effort.
Notably, as participants persisted in the face of adversity,
they eventually succeeded and their reflections indicated
that this persistence enhanced their self-efficacy. In the
midst of their efforts, they often verbally expressed frus-
tration, but this frustration was eventually overcome
when the robot did what it was supposed to, or came
close. This suggests that teacher educators helping preser-
vice teachers learn to program should refrain from seeing
frustration as a signal to solve the preservice teachers’
problems immediately. Rather, allowing them to struggle
as long as they are generally on the right path may be the
best method.
This study also indicated that social resilience was
linked to vicarious success and joint celebration during
collaborative tinkering. Notably, what was important
was not just students looking at how their own effort led
to success, but also looking at how the effort of their
teammates led to success. Furthermore, the very act of
joint celebration of teammates’ successes helped
strengthen resolve to carry on in persistent effort.
Teacher educators should encourage joyful celebrations
of success, as this can to productive attribution, greater
motivation, and ultimate success.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This research was supported by grants 1927595 and
1712286 awarded to the first author, and grant 1906059
awarded to the second author by the National Science
Foundation (NSF). Any opinions, findings, or conclusions are
those of the authors and do not represent official viewpoints of
NSF.
ORCID iD
ChanMin Kim https://orcid.org/0000-0001-9383-8846
Data Availability Statement
Upon reasonable request, deidentified transcripts of interviews
and reflections will be shared.
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