Micro-stories, Robotic Coding, Digital
Applications, and Augmented Reality to Enhance Children's Computational
Thinking
Microrrelatos, codificación robótica,
aplicaciones digitales y realidad aumentada para potenciar el pensamiento
computacional infantil
Dra. M. Esther Del-Moral-Pérez. Catedrática de Universidad. Universidad de Oviedo. España
Dra. Nerea
López-Bouzas. Contratada postdoctoral. Universidad de Oviedo. España
Dr. Jonathan
Castañeda-Fernández. Profesor Ayudante
Doctor. Universidad de Oviedo. España
Received: 2025/02/13 Revised: 2025/03/01 Accepted: 2025/05/20 Online First: 2025/05/21 Published: 2025/05/24
How to cite:
Del-Moral-Pérez, M.E.,
López-Bouzas, N., & Castañeda-Fernández, J.
(2025). Micro-stories, Robotic
Coding, Digital Applications,
and Augmented Reality to Enhance Children's
Computational Thinking
[Microrrelatos, codificación robótica, aplicaciones digitales y realidad
aumentada para potenciar el pensamiento computacional infantil]. Pixel-Bit. Revista de Medios y Educación, 73, art.8. https://doi.org/10.12795/pixelbit.115193
ABSTRACT
This research evaluates the
potential of an educational intervention aimed at developing Computational
Thinking (CT) in preschool students aged 4 to 6 years (N=82). The proposal is a
playful approach based on a story featuring a robot, which students must code
to navigate a physical scenario and overcome various challenges by interacting
with digital applications and Augmented Reality (AR). The study follows a
pre-experimental design with a descriptive and comparative methodology, using a
pre-test/post-test to assess CT levels based on key skills: algorithmic
thinking, generalization, abstraction, decomposition, and evaluation. The
results highlight the effectiveness of the proposal in enhancing CT in most
students, regardless of gender and age. Students with Special Educational Needs
and Disabilities (SEND) also showed an improvement in their CT skills, albeit
with greater challenges. All participants were introduced to robotic coding,
which contributed to strengthening their spatial orientation, laterality,
counting skills, hand-eye coordination, logical reasoning, and more. In
conclusion, the use of a playful narrative fostered students' connection with
the story and empathy with the characters, enhancing their engagement with the
proposed tasks and reducing their complexity.
La presente investigación
evalúa la potencialidad de una intervención educativa para desarrollar el
Pensamiento Computacional (PC) en alumnado de Educación Infantil de 4 a 6 años
(N=82). Se trata de una propuesta lúdica apoyada en un relato protagonizado por
un robot, que el alumnado debe codificar para avanzar por un escenario físico y
superar distintos desafíos interactuando con aplicaciones digitales y de
Realidad Aumentada (RA). La investigación es pre-experimental,
adopta una metodología descriptiva y comparativa utilizando un pre-test/post-test que registra
el nivel de PC a partir de sus habilidades plasmadas: pensamiento algorítmico,
generalización, abstracción, descomposición y evaluación. Los resultados
evidencian la contribución de la propuesta para incrementar el PC de la mayoría
del alumnado, independientemente de la variable género y edad. El alumnado con
ACNEAE también ha incrementado su nivel de PC aunque
con mayores dificultades. Todos se iniciaron en la codificación robótica, lo
que contribuyó a activar e impulsar su orientación espacial, lateralidad,
capacidad de conteo, coordinación óculo-manual, razonamiento lógico, etc. A
modo de conclusión, se subraya que la utilización de una narrativa lúdica ha
promovido la conexión del alumnado con la historia y la empatía con los
personajes, favoreciendo su engagement con las tareas
propuestas y minimizando su complejidad.
.
KEYWORDS · PALABRAS CLAVES
Computational Thinking, Micro-stories, Digital Apps,
Augmented Reality, Early Childhood Education.
Pensamiento computacional, microrrelatos, app digitales, realidad aumentada, Educación Infantil.
1. Introductión
Since the Horizon Report, Teaching and Learning
Edition (Pelletier et al., 2022) and the UNESCO ICT Competency
Framework for Teachers (UNESCO, 2019), the promotion of digital
competencies among both teachers and students has been emphasized as essential
for interacting in an increasingly technologized world. Computational Thinking
(CT) has become a fundamental logic for interacting with increasingly
sophisticated machines. In this regard, DigComp (Vuorikari et al.,
2022) identifies CT as one of the key components in fostering digital
competence.
CT is understood as an individual's ability to solve
problems systematically, creatively, and collaboratively, with the support of
digital resources. It encompasses dimensions such as algorithmic thinking,
generalization, abstraction, and evaluation (Wing, 2006). According to Shute et
al. (2017), it is a skill for solving problems effectively and
efficiently—algorithmically—with or without the aid of computers, using various
resources applicable across different contexts. From a pedagogical perspective,
authors such as Montuori et al. (2024) point out that computational thinking
(CT) can foster fundamental cognitive skills in early childhood, such as
logical thinking, creativity, and autonomy. However, according to Yang et al.
(2024), there is a risk of prematurely instrumentalizing learning by
prioritizing programming languages or technological tools over the holistic
development of students. Therefore, the focus should be on providing playful
and meaningful experiences that respect the principles of child development.
One of the key discussions in the current literature
on CT revolves around whether it should be considered a transversal
competence—similar to critical thinking or problem-solving—as suggested by
Dagiené et al. (2024), or whether it represents a form of advanced digital
literacy, as argued by Pajchel et al. (2024). Proponents of the former view
argue that CT can be integrated into various curricular areas such as
mathematics, science, or even language learning, supporting an
interdisciplinary approach (Ouaazki et al., 2024). On the other hand, authors
such as Akramova et al. (2024) and Yuberti et al. (2024) associate CT with
skills specific to the STEM field and suggest that its implementation requires
a conceptual foundation that may exceed the cognitive capacities of younger
students.
However, CT can be stimulated at this stage through
interventions involving robotic programming (Canbeldek & Isikoglu, 2023),
which entails planning tasks to activate the robot, coding its movements in
space, and solving encountered problems in a logical and coherent manner. This
practice promotes understanding and abstraction by helping children associate
the robot's buttons with their corresponding actions (Zhang et al., 2020).
Additionally, it activates the ability to generalize, enabling children to
recognize patterns and identify the optimal sequence for executing pre-set
commands (Silva et al., 2023). Moreover, interacting with the robot allows for
an evaluation of programmed actions, providing feedback on successes
and errors and offering opportunities for adjustments.
In educational contexts, stories are commonly used in
early childhood as vehicles to facilitate learning across different domains.
Their immersive quality fosters children's emotional engagement with characters
and enhances task engagement by allowing them to take on protagonist
roles (Leoste et al., 2021). In this regard, various experiences have
incorporated robots as protagonists in fictional narratives, integrating
curriculum content and promoting CT through playful challenges that motivate
young learners in their learning process (Chang et al., 2023; Chen & Lee,
2023).
Additionally, digital applications and Augmented
Reality (AR) enable direct interaction with scenarios and characters to
solve problems such as identifying elements based on specific characteristics
(colors, sizes, shapes, etc.), thereby enhancing abstraction and
generalization skills. Immersive scenario visualization also broadens
educational activities by stimulating students' spatial
orientation as they navigate digital fictional environments (Işik et
al., 2024). Furthermore, assuming different roles within predetermined
narratives enhances engagement in the story's development, as students become
involved in conflict resolution through various activities. Clearly, these
applications can activate CT by fostering multisensory stimulation in
problem-solving scenarios while providing immediate feedback on performance.
Thus, this study aims to determine whether an
educational intervention combining robotic programming, digital
applications, and AR—integrated through micro-stories—contributes to the
development of Computational Thinking (CT) in preschool students.
2. Digital Applications,
AR, and Robotics in Micro-Stories to Activate Computational Thinking
CT has emerged as a key approach in educational
innovation, especially in early childhood, showing strong connections with
pedagogical trends such as constructivism, Challenge-Based Learning (CBL), and
the STEAM approach. From Papert’s constructionist perspective (1980), learning
is strengthened when knowledge is actively constructed through the manipulation
of objects. This is closely related to the activation of CT through playful
activities in which children must interact with physical and/or digital elements
to carry them out. In addition, the incorporation of micro-stories allows for
the contextualization of challenges that encourage planning, sequencing, and
problem-solving, as proposed in CBL (Nawawi et al., 2024). The STEAM approach,
in turn, connects different areas of knowledge through creative, collaborative,
and meaningful experiences, contributing to an integrative view of CT (Yuberti
et al., 2024). However, it is essential to adopt a critical perspective on CT
as an educational construct, distinguishing between general cognitive
skills—such as logic and problem-solving (Singh & Kaunert, 2024)—and
specific computational skills—such as programming (Canbeldek & Isikoglu,
2023).
The use of micro-stories to activate CT also makes
activities more understandable and emotionally engaging by helping students
connect with their prior knowledge and experiences—either by incorporating
emotional robots as narrators (Antunes et al., 2022) or by using concrete
narratives to increase students’ involvement in the story’s plot (Yang et al.,
2023). According to narrative learning theories (Bruner, 1990), children
structure the world through stories. Therefore, giving a robot a mission to
solve a problem or conflict within a story can enhance their motivation and
comprehension, giving meaning to their actions. Furthermore, based on the
theory of meaningful learning (Ausubel, 1963), stories act as advance
organizers, generating non-arbitrary connections between new information and
what is already known. Thus, structuring CT-stimulating activities around
micro-stories not only encourages students to engage with those activities, but
to do so with a clear purpose or mission—such as helping a character or solving
a specific situation—while becoming emotionally invested in their actions
(Yang, 2024).
The interactivity offered by digital applications,
along with the recreation of scenarios and challenges, fosters user immersion
and engagement in performing various tasks or solving simple problems aimed at
stimulating Computational Thinking (CT) (Dorouka et al., 2020; Shanmugam et
al., 2019). Augmented Reality (AR), in turn, allows for the superimposition of
3D virtual elements onto the real world, facilitating the assimilation of
complex concepts and processes while activating spatial thinking through multisensory
experiences. This reduces students' cognitive load by making abstract elements
more tangible (Işik et al., 2024). However, the combined use of digital
applications and AR must be harmonized within a coherent narrative, requiring a
prior selection process that justifies its integration (Dietz et al., 2021;
2023). In this regard, augmented digital micro-stories can be used,
where characters placed in playful scenarios present challenges to students,
fostering engagement and emotional involvement with the story itself
(Triantafyllou et al., 2024).
Students’ empathy with story
protagonists—where they must intervene to help them achieve their goals—is
enhanced when digital and AR resources facilitate the development of CT.
However, there are few educational applications that incorporate
micro-stories specifically designed to develop CT (Yadav & Chakraborty,
2023). Therefore, it is essential to carefully select applications that include
activities designed to stimulate CT-related skills (Utesch et al., 2020), such
as problem-solving through abstraction, generalization, algorithmic
thinking, and evaluation strategies. These applications should also allow for
the analysis of students' progress and difficulties, enabling them to
learn from their mistakes.
CT can be activated in early childhood education
(ages 3–6) by presenting activities that align with a cohesive
narrative, fostering: Abstraction skills, through activities like
assembling puzzles, classifying objects by matching them with their shadows,
identifying color, shape, and size patterns, etc.; Generalization
skills, by grouping elements based on similarities, playing memory-matching
games, and associating icons, words, or images according to their semantic
categories; Algorithmic thinking, through tasks that require following
predefined steps, sequencing elements, completing series, and identifying
multiple solutions to a problem (e.g., choosing the shortest path to a goal,
identifying pieces that fit a given space, or constructing blocks); Task decomposition
and evaluation skills, through counting activities, basic addition and
subtraction, comparing solutions, spatial orientation exercises, and
movement-based tasks (e.g., moving objects forward, backward, left, or right by
following instructions and verifying their accuracy).
Additionally, using robots as protagonists in
micro-stories is becoming increasingly common for activating CT in early
childhood (Bravo et al., 2021; Hu et al., 2022; Tengler et al., 2021). This
requires students to program the robot’s movements to navigate a
scenario and overcome challenges throughout the story (Bono et al., 2022).
Programming robots with basic commands (forward, backward, turn
right, turn left, etc.) fosters algorithmic thinking, as students must plan
the robot's path to solve problems encountered while interacting
with digital resources (Papadakis, 2022) and/or AR. This also
strengthens logical reasoning, as students must count the number of steps
or turns needed (forward, turn right, left, etc.).
At the same time, abstraction skills are
stimulated, as students must understand the robot's role in the
story while orienting themselves in space and linking each movement to the
corresponding button. Generalization skills are also reinforced as
students recall the story and tasks they need to complete. This entire
process includes continuous evaluation, allowing students to adjust their
problem-solving strategies and redefine their approach as they progress
through the activity.
3. A Playful
Intervention to Stimulate CT: “Cleaning the Turtle’s Home”
The designed intervention integrates digital
resources, AR applications, and a robot to stimulate Computational Thinking
(CT) in early childhood through interaction with the characters of a
micro-story placed on a custom-designed mat (Figure 1). The story begins with
Tina, a sea turtle who lives in the ocean with her family. One day, Tina
realizes that her home is in danger: plastics pollute the sea, corals lose
their color, and some of her marine friends face difficulties. Tina decides to
embark on a mission to save her home and restore life and beauty to the reef.
To achieve this, she needs the help of the students, who will be her companions
in this adventure.
To introduce the story, the narrative starts with the
animated short film “Turtle Journey: Our Oceans Are in Trouble”, a
creation of Greenpeace and the award-winning animation studio Aardman (watch
at https://cutt.ly/Vw4FHQXR).
This short film serves as the starting point for the game, where children have
the opportunity to intervene to restore Tina’s habitat.
Figure 1
Designed game
Source: Own
elaboration
The students’ task is presented through a video
designed with the Powtoon app, where Tina explains her mission and asks them to
join her in solving challenges and returning home. As Tina moves forward, she
encounters an ocean full of plastics that block her path. At this point,
students must use the AR app Plastic Ocean (https://bit.ly/3NFxKRL) to
"collect" the plastics from the ocean within a holographic
environment by interacting with a tablet, clearing the way so that Tina can
swim freely and continue her journey (Figure 2). Specifically, they must
visually identify the plastics and click on them, activating their
generalization skills, as the details vary (location, type of plastic, affected
species), and users must recognize common patterns to classify the waste.
Figure 2
Intervention with
the AR app Plastic Ocean
Source: Own
elaboration
During her journey, Tina encounters several marine
animals that need help to free themselves from the surrounding waste. Students
must use the AR app Ocean 4D+ (https://cutt.ly/7w7ARcz0) to identify
the animals. Each animal explains its characteristics, diet, habitat,
reproduction, and how marine pollution affects its life. The explorers describe
what they learn, helping Tina better understand how to protect them. This
stimulates their abstraction skills, as when observing 3D animals, students
must recognize the shapes, colors, sizes, and movements of the creatures they
encounter, being able to rotate, zoom in, explore, and manipulate them to
identify their characteristics (Figure 3).
Figure 3
Intervention with the AR app Ocean 4D+
Source: Own
elaboration.
Later, with the Marco Polo app (https://cutt.ly/0w7ATfzt),
children must match each fish or marine element with its shadow to rebuild the
coral reef. This stimulates the three main CT skills: Abstraction, by
correctly matching fish or marine elements with their silhouette regardless of
their colors; Generalization, by recognizing common patterns between
different animal shapes and their shadows, promoting the transfer of strategies
to similar cases; Algorithmic thinking, by establishing a logical
sequence of steps: observing each animal's characteristics, comparing them with
the available shadows, and selecting the correct one, solving the problem in a
structured and efficient way. This activity fosters all CT skills in a playful
manner (Figure 4).
Figure 4
Intervention with the Marco Polo app
Source: Own
elaboration
To complete the described activities, students must
move the Tale-Bot robot across the grid on the mat, following a pre-established
itinerary that requires coding its movements to reach the different activities.
To do this, they must sequence the trajectory by counting the movement grids
and using the corresponding buttons to move forward and make the necessary
turns based on the robot's coding (Figure 5).
Thus, this intervention aims to determine the extent
to which CT develops in early childhood students by using a micro-story
featuring a robot as a teaching resource. Children engage in activities by
interacting with digital and AR applications to contribute to the happy ending
of the story.
Figure 5
Coding process integrated into the itinerary
Source: Own
elaboration
4. Methodology
This research is part of the Robot-Digital
StoryTelling project: immersive playful narratives starring robots
that enhance computational thinking, funded by the University of Oviedo
(2024-2025). Specifically, it focuses on analyzing whether the described
educational intervention fosters the development of Computational Thinking (CT)
in early childhood. It is an empirical pre-experimental study, of a
descriptive and comparative nature, with an exploratory and analytical
approach, as classified by Cohen et al. (2011). The adopted design uses
a pre-test/post-test method to measure students' CT levels before and
after participating in the intervention, which is based on a game where they
interact with a robot as the protagonist of the narrative.
This methodological approach was selected due to its
suitability for evaluating the impact of an educational intervention in a
naturalistic context, where random assignment and the creation of a control
group were not feasible for ethical and organizational reasons. Compared to
more robust methodologies such as quasi-experimental or experimental designs,
the pre-experimental approach allows for the collection of preliminary evidence
on the effectiveness of the proposed intervention, enabling its implementation
in real-world settings without significantly disrupting school dynamics. It is
worth noting that several strategies were adopted to minimize bias resulting
from the absence of a control group: a) methodological triangulation through
the combination of quantitative instruments (pre-test/post-test and
CT-Robot-DST) and qualitative tools (systematic observation); b)
standardization of the intervention protocol to ensure consistency in its
application; and c) rigorous statistical analysis using non-parametric tests
and multiple regression to control for the influence of confounding variables.
4.1. Sample
The sampling was intentional and non-probabilistic,
conditioned by the participation of 4- to 6-year-old
students from C.P. La Vallina (N=82), with prior authorization
from their families. 58.6% were boys, and 41.5% were girls. Their
ages were distributed as follows: 4 years old (24.4%), 5 years old (59.7%), and
6 years old (15.9%). 84.1% had a neurotypical development, while 15.9%
were Students with Specific Educational Support Needs (SESN): 12.2% had
developmental delay, 2.4% had a diagnosis of Attention Deficit
Hyperactivity Disorder (ADHD), and 1.2% had Autism Spectrum Disorder
(ASD).
It is important to highlight that none of the
participants had prior experience with robotics. After obtaining approval from
the ethics committee of the University of XX (37_RRI_2024), the
purpose and procedures were explained to teachers and families (Figure 6).
Figure 6
Explanatory document (https://cutt.ly/Ze7scD3e)
Source: Own
elaboration.
4.2. Procedure and Data
Analysis Techniques
Figure 7
Research phases
Source: Own
elaboration
A statistical analysis was conducted based
on descriptive analysis, using percentages, means, and standard
deviations. After performing the Kolmogorov-Smirnov test, it was confirmed
that the sample did not meet normality criteria (p<0.001 in all items),
which is why non-parametric tests were used for subsequent
comparisons. The Wilcoxon test was applied to compare pre-test
and post-test results. Mean comparisons were conducted using
the Mann-Whitney U test for dichotomous nominal variables (gender and
presence/absence of SESN) and the Kruskal-Wallis H test for
polytomous nominal variables (age). Finally, multiple linear regression
analysis was performed to determine the extent to which the skills
activated during the intervention could predict CT. All statistical analyses
were carried out using SPSS-V26.
To ensure the neutrality, standardization, and
validity of the data collection procedure, an intervention protocol was
established that included the following aspects: a) each individual session
lasted approximately 27 minutes and 30 seconds; b) a total of 16 sessions were
conducted over the course of 4 weeks; c) evaluation conditions were
standardized, taking place in the students’ regular classroom, without the
presence of any distracting elements and using the same materials and
technological resources that students were already familiar with and used
regularly; d) the instruments were administered by a single researcher,
ensuring greater consistency in the evaluation. This researcher had been
previously trained in the use of the instruments and in the dynamics of the
intervention, thereby ensuring objectivity in data collection.
4.3. Instruments
4.3.1. Computational
Thinking Assessment Test (Pre-test/Post-test)
The instrument used to measure the Computational
Thinking (CT) level of Early Childhood Education students (ages 3-6) integrates
a series of activities adapted from the Bebras Project (https://www.bebras.org/) (Zapata et al.,
2024), with reduced complexity, as the original version is designed for Primary
Education (ages 6-12). The instrument development process was carried out in
four phases: 1) selection of items from the original tests that assessed key CT
skills (algorithmic thinking, generalization, abstraction, decomposition, and
evaluation); 2) graphic and narrative redesign of the activities to adapt them
to the cognitive, emotional, and contextual characteristics of children aged 4
to 6, integrating them into the story used in the intervention; 3) content
validation through expert judgment by specialists in Early Childhood Education
and Educational Technology, who evaluated the relevance, clarity, and
appropriateness of each item; and 4) testing with a group of 2 boys and 2 girls
to ensure the appropriateness of the activities, confirming their
functionality, age suitability, and alignment with the educational objectives
of the intervention.
After this process, the final instrument consists of
six activities presented on sheets that depict the elements and characters from
the described playful narrative. The activities encompass tasks associated with
the theoretical dimensions intrinsic to CT (Vuorikari et al., 2022), such as
algorithmic thinking (sequencing and comprehension), generalization, and
abstraction, along with decomposition and evaluation (Figure 8).
Figure 8
Activities used to
assess CT level (Pre-test/Post-test)
Source: Own
elaboration
Table 1
Evaluation Rubric for Pre-test/Post-test Activities
|
Activity |
Very Low (1) |
Low (2) |
Medium (3) |
High (4) |
|
A1. Indicate the shortest
path for the turtle |
Presents an erratic path |
Chooses an orderly path
but starts with the crab |
Follows the short path but
skips some animals |
Indicates the shortest
path and starts with the jellyfish |
|
A2. Anticipate the next
position |
Unable to anticipate |
Anticipates correctly
after several attempts |
Anticipates correctly on
the second attempt |
Anticipates correctly on
the first attempt |
|
A3. Identify the sequence
(I) |
Points to the fish |
Points to the turtle after
several attempts |
Points to the turtle on
the second attempt |
Points to the turtle |
|
A4. Identify the sequence
(II) |
Points to the turtle or
the crab |
Points to the fish after
several attempts |
Points to the fish on the
second attempt |
Points to the fish |
|
A5. Associate animals with
their shadows |
Does not associate |
Associates 1-2 animals
with their shadows |
Associates 3 animals with
their shadows |
Associates all animals
with their shadows |
|
A6. Guide the turtle to
the cake |
Takes a diagonal path,
ignoring the grid |
Advances linearly but does
not make the turn |
Takes a correct but long
path |
Takes a correct and short
path |
Source: Created by
the author
The instrument was administered individually in a
controlled environment, with visual and verbal support from the researcher,
following a standardized protocol.
4.3.2. Assessment
instrument for Computational Thinking during an interaction supported by
Robot-DST (CT-Robot-DST)
To record the level of CT displayed by the students during
the execution of the playful activities conducted in the intervention with the
robot, the CT-Robot-DST instrument was designed and validated. Its development
began with a systematic review of previous instruments used in similar studies,
identifying measurable indicators—based on observation—linked to the dimensions
of CT. These indicators were inspired by those used in other research focused
on assessing CT in early childhood (Berson et al., 2023; Ching & Hsu, 2023;
Yang, 2024; Zeng et al., 2023). Subsequently, an operationalization matrix was
created to define the variables, categories, and performance levels. Thus,
similarly to the procedure followed by Terroba et al. (2021), four categories
were established: 1=Very Low; 2=Low; 3=Medium; 4=High. These categories were
used to identify the participants’ levels in each skill (Table 2).
Table
2
CT-Robot-DST
|
Dimension |
Variable |
Categories |
|
Algorithmic
Thinking |
Counting skill |
(1) Does not recognize the cells guiding the path |
|
Logical reasoning (coding of the 6 sequences the robot must perform) |
(1) Does not code any of the sequences the robot must perform |
|
|
Spatial
orientation |
(1) Does not know how to move elements in the physical and digital
environment |
|
|
Generalization
and Abstraction |
Eye-hand coordination and interaction |
(1) Does not recognize or interact with physical and digital
elements |
|
Abstraction (identifying the button/action) |
(1) Does not identify the buttons to move the robot |
|
|
Memory
activation |
(1) Does not remember the story or tasks requested |
|
|
Decomposition
and Evaluation |
Discrimination
of laterality |
(1) Has not acquired laterality |
|
Moves the robot across the squares of the mat with effectiveness |
(1) Moves the robot without criteria |
|
|
Codes the sequence, engages in the action, and evaluates its
accuracy |
(1) Does not know how to code the sequence in the robot |
|
|
Engages in the story as a supporting character |
(1) Remains indifferent to the story and the challenges presented |
Source: Created by
the author.
The instrument was validated through Exploratory
Factor Analysis. Bartlett’s sphericity test is significant (p<0.001) and the
Kaiser-Meyer Olkin (KMO) measure of sampling adequacy presents a high value
(KMO=0.914). The unweighted least squares (ULS) method was used, and the
factors obtained were rotated obliquely using the Oblimin method, as despite
having sufficient response categories, their distribution is not normalized
according to the Kolmogorov-Smirnov Test (KS<0.001). It was found that 74.5%
of the variance is explained by a single factor (Figure 9).
Figure 9
Scree plot
Source: Created by
the author.
Referencing the extraction communalities, all items
explain a significant portion of the variability of each variable, with values
equal to or greater than 0.600. Furthermore, according to the component matrix,
all items group around a single factor, making this a unidimensional instrument
for measuring CT. Complementarily, the obtained Cronbach’s Alpha coefficient is
very high (α=0.954), indicating that
the scale has a good level of reliability.
The CT-Robot-DST was administered during the
intervention through direct observation by the researcher, who recorded the
students' behaviors in real time using a previously agreed-upon rubric.
5. Results
5.1. Initial Computational
Thinking (CT) Diagnosis and Level Achieved After the Intervention
The data analysis confirms a statistically significant
improvement in the students' CT level after the intervention, as seen in the
scores achieved both in the skills related to algorithmic thinking and
generalization and abstraction. Particularly relevant is the increase in skills
for decomposition and evaluation (x̄: pre-test=2.17 vs. x̄:
post-test=3.40; p <0.001) (Table 3).
Table 3
Comparison of
scores achieved in the dimensions defining CT
|
|
Pre-test |
Post-test |
Rxy |
Z |
p |
||
|
Activity (A) |
x̄ |
DT |
x̄ |
DT |
|||
|
A1. Algorithmic Thinking |
3.59 |
0.888 |
3.76 |
0.658 |
0.817 |
-2.81 |
0.005 |
|
A2. Algorithmic Thinking |
3.09 |
0.834 |
3.41 |
0.702 |
0.803 |
-5.014 |
<0.001 |
|
A3. Algorithmic Thinking |
2.89 |
1.122 |
3.15 |
1.044 |
0.899 |
-4.200 |
<0.001 |
|
A4. Algorithmic Thinking |
2.33 |
1.267 |
2.70 |
1.437 |
0.889 |
-4.388 |
<0.001 |
|
A5. Generalization and Abstraction |
3.41 |
0.816 |
3.54 |
0.706 |
0.895 |
-2.887 |
0.004 |
|
A6. Decomposition and Evaluation |
2.17 |
0.979 |
3.40 |
0.814 |
0.672 |
-7.343 |
<0.001 |
|
TOTAL: COMPUTATIONAL THINKING |
2.94 |
1.070 |
3.38 |
0.855 |
0.875 |
-5.840 |
<0.001 |
Source: Self-made.
Post-hoc contrasts show no statistically significant
differences related to age or gender. However, it is noteworthy that girls show
higher values in the different skills and, therefore, in CT (Table 4).
Table 4
Comparison of CT
scores (Pre-test/Post-test) by gender
|
|
Pre-test |
Post-test |
||||
|
Activity (A) |
Boy |
Girl |
p |
Boy |
Girl |
p |
|
A1. Algorithmic Thinking |
3,54 |
3,65 |
0,567 |
3,67 |
3,88 |
0,259 |
|
A2. Algorithmic Thinking |
3,04 |
3,15 |
0,603 |
3,35 |
3,50 |
0,475 |
|
A3. Algorithmic Thinking |
2,83 |
2,97 |
0,660 |
3,06 |
3,26 |
0,492 |
|
A4. Algorithmic Thinking |
2,31 |
2,35 |
0,917 |
2,63 |
2,79 |
0,564 |
|
A5. Generalization and Abstraction |
3,40 |
3,44 |
0,756 |
3,48 |
3,62 |
0,406 |
|
A6. Decomposition and Evaluation |
2,15 |
2,21 |
0,690 |
3,33 |
3,50 |
0,617 |
|
TOTAL: COMPUTATIONAL THINKING |
2,90 |
3,00 |
0,700 |
3,29 |
3,50 |
0,431 |
Source: Self-made
Based on these
results, the overall CT level of the students was calculated, establishing four
performance levels: very low: 0.00-0.99; low: 1.00-1.99; medium: 2.00-2.99;
high: 3.00-4.00. Figure 10 shows the distribution of students according to the
CT level achieved before and after the intervention, with notable increases
observed.
Figure 10
Percentage
distribution of students according to the CT level achieved in the
pre-test/post-test
Source:
Self-made
Thus, Table 5 shows that 17.1% maintain their initial
CT level after the intervention, while 42.7% increase one or even two levels.
On the other hand, 40.2% of students who were already at the highest level
before the intervention remain at that level. As for students with Special
Educational Needs (SEN), six students from the "very low" level move
to the "low" level, one from the "low" level stays in the
same level, and two who were at the "high" level remain there (one
with ADHD and the other with ASD). Therefore, half of the 12 students with SEN
improve their CT with the intervention. However, four of these students (one
with ADHD and three with developmental delays) remain in the "very
low" or "low" levels.
Table 5
Distribution of
subjects by their CT level and increase after the intervention
|
Level |
Pre-test |
Post-test |
Level maintained |
Increase 1 level |
Increase 2 levels |
|||||
|
With SEN |
Without SEN |
With SEN |
Without SEN |
With SEN |
Without SEN |
With SEN |
Without SEN |
With SEN |
Without SEN |
|
|
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
N(%) |
|
|
Very low |
9(11.0) |
2(2.4) |
3(3.7) |
0(0.0) |
3(3.7) |
0(0.0) |
6(7.3) |
1(1.2) |
0(0.0) |
1(1.2) |
|
Low |
1(1.2) |
15(18.3) |
7(8.5) |
3(3.7) |
2(2.4) |
2(2.4) |
0(0.0) |
12(14.6) |
0(0.0) |
0(0.0) |
|
Medium |
0(0.0) |
22(26.3) |
0(0.0) |
20(24.4) |
0(0.0) |
7(8.5) |
0(0.0) |
15(18.3) |
0(0.0) |
0(0.0) |
|
High |
2(2.4) |
31(37.8) |
2(2.4) |
46(56.1) |
- |
- |
- |
- |
- |
- |
Source: Self-made
5.2. Effect of the
Intervention on the Increase in Computational Thinking (CT)
During the described intervention, which was supported
by a playful narrative featuring a robot, the skills intrinsic to the
dimensions of CT that the students demonstrated in their executions were
measured. These were associated with the progress and achievement of the
story’s goal, which was led by the turtle. The CT-Robot-DST instrument was used
for this purpose, allowing for the evaluation of the skills activated by the
subjects, classifying them into four performance levels (Table 6).
Table 6
Descriptive
statistics of the levels achieved by the students in the CT skills
|
Computational Thinking Skills |
Variables |
Ver low |
Low |
Medium |
High |
x̄ |
DT |
|
N(%) |
N(%) |
N(%) |
N(%) |
||||
|
Algorithmic Thinking |
Skill for
counting |
0(0.0) |
6(7.3) |
12(14.6) |
64(78.0) |
3.71 |
0.598 |
|
Logical
reasoning |
13(16.0) |
15(18.5) |
23(28.4) |
30(37.0) |
2.86 |
1.093 |
|
|
Spacial
orientation |
7(8.5) |
19(23.2) |
18(22.0) |
38(46.3) |
3.06 |
1.023 |
|
|
Subtotal |
- |
- |
- |
- |
3.16 |
1.024 |
|
|
Generalization and Abstraction |
Eye-hand coordinarion and interaction |
3(3.7) |
16(19.5) |
24(29.3) |
39(47.6) |
3.21 |
0.885 |
|
Abstraction |
5(6.1) |
13(15.9) |
17(20.7) |
47(57.3) |
3.29 |
0.949 |
|
|
Memory
activation |
11(13.4) |
15(18.3) |
18(22.0) |
38(46.3) |
3.01 |
1.094 |
|
|
Subtotal |
- |
- |
- |
- |
3.30 |
1.002 |
|
|
Descomposition and evaluation |
Lateralization
discrimination |
4(4.9) |
11(13.4) |
20(24.4) |
47(57.3) |
3.34 |
0.892 |
|
Moves the robot across the squares of the mat, verifying its
effectiveness |
2(2.4) |
10(12.2) |
22(26.8) |
48(58.5) |
3.41 |
0.800 |
|
|
Codes the sequence, engaging in the action and evaluates its accuacy |
13(15.9) |
21(25.6) |
13(15.9) |
35(42.7) |
2.85 |
1.145 |
|
|
Engages in the story as a secondary character |
17(20.7) |
12(14.6) |
16(19.5) |
37(45.1) |
2.89 |
1.197 |
|
|
Subtotal |
- |
- |
- |
- |
2.95 |
1.065 |
|
|
TOTAL: Computational Thinking |
3.22 |
1.006 |
|||||
Source: Self-made.
The highest scores achieved by the students are
recorded in the skill "Generalization and Abstraction" (x̄=3.30)
and "Algorithmic Thinking" (x̄=3.16), followed by
"Decomposition and Evaluation" (x̄=2.95). On the other hand, the
level of computational thinking (CT) obtained by the subjects is medium-high (x̄=3.22)
(Figure 11).
Figure 11
Percentage distribution of the sample according to the level of CT
demonstrated during the intervention
Source: own
elaboration
Subsequent analysis of means according to the age
variable does not yield statistically significant differences. However, when
comparing the scores achieved by students based on gender, it is found that
girls perform better than their male counterparts, although these differences
are statistically significant only regarding their ability to decompose and
evaluate tasks (x̄: boy=2.71 vs. x̄: girl=3.29; p=0.021).
Specifically, they stand out in coding and evaluating the sequence of robot
movements required to move it across the mat. They appear more self-critical
and more engaged in the action. They also excel in their immersion in the narrative
by assuming the role of a secondary character, concerned with achieving the
final goal of the challenges, in this case, finding the turtle’s home (Table
7).
Table 7
Mean comparison by gender
|
CT
Skills |
Variables |
Boy |
Girl |
p |
d |
|
Algorithmic Thinking |
Skill for
counting |
3.63 |
3.82 |
0.312 |
0.096 |
|
Logical
reasoning |
2.89 |
2.82 |
0.653 |
0.057 |
|
|
Spacial
orientation |
3.13 |
2.97 |
0.303 |
0.126 |
|
|
Subtotal |
3.29 |
3.06 |
0.514 |
0.079 |
|
|
Generalization and Abstraction |
Eye-hand coordinarion and interaction |
3.25 |
3.15 |
0.361 |
0.111 |
|
Abstraction |
3.23 |
3.38 |
0.796 |
0.031 |
|
|
Memory
activation |
2.94 |
3.12 |
0.681 |
0.051 |
|
|
Subtotal |
3.23 |
3.41 |
0.554 |
0.067 |
|
|
Descomposition and evaluation |
Lateralization
discrimination |
3.42 |
3.24 |
0.092 |
0.196 |
|
Moves the
robot across the squares of the mat, verifying its effectiveness |
3.31 |
3.56 |
0.243 |
0.135 |
|
|
Codes the
sequence, engaging in the action and evaluates its accuacy |
2.58 |
3.24 |
0.010 |
0.317 |
|
|
Engages in
the story as a secondary character |
2.58 |
3.32 |
0.008 |
0.327 |
|
|
Subtotal |
2.71 |
3.29 |
0.021 |
0.287 |
|
|
TOTAL:
PENSAMIENTO COMPUTACIONAL |
3.15 |
3.32 |
0.552 |
0.071 |
|
Source: Self-made
As expected, given the difficulties of students with
SEN, when comparing the mean scores achieved in computational thinking (CT)
with those of the rest of their classmates, significant differences are found
(x̄: with SEN=1.69 vs. x̄: without SEN=3.51; p<0.001). In Figure
12, it is observed that students with greater difficulties are at the lower
levels.
Figure 12
Percentage distribution of the sample with and without SEN according to
their CT level
Source: own
elaboration
The evolution of CT after the intervention is
positive, as evidenced by comparing the means achieved in the Computational
Thinking construct by the students, showing an increase of 0.44. In particular,
there is a notable increase of 1.23 points in the skill "Decomposition and
Evaluation" (Figure 13).
Figure 13
Evolution of the CT means and its skills before, during, and after the
intervention
Source: own
elaboration
Finally, to determine the extent to which the skills
activated during the intervention influence the level of CT reached in the
post-test, a multiple linear regression analysis was conducted, confirming that
all of them have an impact on the students' CT level (Table 8).
Table 8
Multiple regression model between the intrinsic CT
skills activated during the intervention
|
Model |
Unstandardized coefficients |
Standardized coefficients |
t |
Sig. |
|
|
B |
Desv. Error |
Beta |
|||
|
(Constant) |
-0.120 |
0.119 |
- |
-1.009 |
0.317 |
|
Counting ability |
0.042 |
0.051 |
0.089 |
0.820 |
0.415 |
|
Logical reasoning |
0.055 |
0.039 |
0.216 |
1.402 |
0.165 |
|
Spatial orientation |
0.009 |
0.045 |
0.033 |
0.197 |
0.844 |
|
Oculo-manual coordination and interaction
|
0.026 |
0.054 |
0.083 |
0.484 |
0.630 |
|
Abstraction |
-0.006 |
0.042 |
-0.020 |
-0.138 |
0.890 |
|
Memory activation |
0.124 |
0.036 |
0.490 |
3.464 |
0.001 |
|
Laterality discrimination |
-0.022 |
0.039 |
-0.072 |
-0.575 |
0.567 |
|
Moves the robot across the squares of the mat, verifying its
effectiveness |
0.051 |
0.036 |
0.146 |
1.435 |
0.156 |
|
Encodes the sequence, engaging in the action, and evaluates its
accuracy |
-0.016 |
0.027 |
-0.066 |
-0.599 |
0.551 |
|
Gets involved in the story as a supporting character |
0.018 |
0.019 |
0.080 |
0.989 |
0.326 |
Source: own
elaboration
It can be observed that the set of skills activated by
the intervention explains a high percentage of computational thinking, as it
predicts 78.3% of the subjects' results (R2=0.783). Specifically, memory
activation—associated with recalling the story and the tasks requested—is
statistically significant. The regression line shows the relationship between
the values obtained by the subjects in the analyzed skills (independent
variables on the x-axis) and their connection with computational thinking
(dependent variable on the y-axis) (Figure 14).
Figure 14
Scatter plot and
multiple linear regression analysis
Source: Own
elaboration
6. Discussion and
Conclusions
In light of the results, it is evident that the
intervention—supported by the narrative of the search for a home for the
turtle, which required robot coding and the completion of digital, physical,
and augmented activities—contributed to the increase in computational thinking
(CT) among Early Childhood Education students. This innovative practice has
yielded positive results, similar to the experiences conducted by Berson et al.
(2023) and Terroba et al. (2021), who also advocated for integrating play within
interventions using robotics at this educational level to stimulate algorithmic
thinking, as well as skills in generalization, abstraction, decomposition, and
evaluation, all of which are part of CT.
The intervention contributed to the activation of
various skills associated with CT. On one hand, students were asked to count
the squares the robot needed to advance through, sequence tasks, orient
themselves spatially on the mat and within the digital environment (tablet) to
perform the indicated activities, and associate marine characters with their
habitats, rotating 3D animals to discern their qualities, among other tasks.
The experience also required eye-hand coordination and interaction, both with
physical elements like the mat, the robot, or the turtle, and with immersive
activities designed through an app. Without a doubt, the use of micro-stories
embedded in immersive scenarios supported by augmented reality (AR) contributes
to the development of students' spatial orientation by immersing them in
digital fictional environments. Additionally, the interaction with physical and
augmented elements creates multisensory experiences that reduce the cognitive
load associated with sequencing and problem-solving, as noted by Işik et
al. (2024).
During the intervention, students recorded lower
values in the abstraction skill compared to pre-test and post-test scores, due
to the novelty and complexity of the proposed activity, as they had to code the
robot's movements by associating each button with the corresponding action to
achieve its goal. Furthermore, performing the sequence of activities integrated
into the story required activating memory to recall the instructions. Regarding
the CT skills enhanced after the playful experience, the improvement in the
“Decomposition and Evaluation” skill stands out, which may be attributed to the
appeal of the story underlying the activity requested from students: finding
the shortest path to the turtle's birthday cake, an activity that is rewarding
for them. This skill is closely related to the segmentation of the robot's
coding activities to move forward and the verification of its adequacy to the
task (“I’m behind!”, “I overshot it, I almost left the path,” etc.), as well as
the ability to repeat actions (“I have to do it again...,” “Can I try again?”,
etc.).
It should be emphasized that, although the
participating students had not previously used educational robotics, all
benefited from the intervention, regardless of gender and age. The same is true
for students with Special Educational Needs (SEN), although only half of them
showed an increase in their CT level, while the rest, with greater
difficulties, remained at their initial values. Without a doubt, integrating
augmented digital micro-stories into playful scenarios where a robot is the
protagonist is an innovative—and effective—proposal for stimulating CT.
Furthermore, the integration of robotic coding as a means to contribute to the
happy ending of a story allows students to be introduced to programming and
develop other associated skills, such as spatial orientation, laterality,
counting, eye-hand coordination, logical reasoning, etc. Advancing the robot
through a playful and immersive scenario that invites students to overcome and
solve various tasks integrated into a story increases their CT while also
enhancing their emotional involvement with the proposed activities.
The design of the intervention is based on a
constructivist view of learning, where students build knowledge through
meaningful and contextualized experiences, in this case within micro-stories.
At the same time, the experience is framed within a situated learning approach
by linking the activities to the students’ immediate environment—the marine
environment—thus promoting knowledge transfer. Moreover, the STEAM approach is
present by integrating technology, narrative, and CT, allowing for the joint promotion
of areas such as Science, Technology, Engineering, Art, and Mathematics. All of
this allows the results to be interpreted not only in terms of their practical
effectiveness but also from a solid conceptual foundation, which strengthens
the validity of the conclusions reached. Therefore, the findings of this
research are grounded in established pedagogical principles that provide
theoretical robustness to the proposal.
As limitations of the study, it should be noted that
the results refer to a specific school context with students aged 4 to 6 years.
Possible biases must also be acknowledged due to the non-random selection of
the sample and the absence of a control group. Additionally, it would be
interesting to expand the sample in the future and/or conduct the intervention
with students of other ages. Similarly, the narrative could be adapted to
centers of interest relevant to the participants’ context, integrating other
applications and activities that require activating skills associated with CT.
Furthermore, the designed instruments and the narrative itself could be
translated into other languages, facilitating their replication in other
contexts.
Regarding future research lines, it would be
appropriate to conduct comparisons with other methodological approaches to
activate CT—both with and without the use of digital devices—to explore the
application of micro-stories in different curricular areas and to carry out
longitudinal studies to assess the sustainability of learning over time.
Author
contributions
Conceptualization, M. Esther Del-Moral-Pérez, Nerea
López-Bouzas; data curation, Jonathan Castañeda-Fernández; formal analysis, M.
Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
funding acquisition, M. Esther Del-Moral-Pérez; investigation, M. Esther
Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
methodology, M. Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan
Castañeda-Fernández; project administration, M. Esther Del-Moral-Pérez;
resources, Nerea López-Bouzas; software, Nerea López-Bouzas; supervision, M.
Esther Del-Moral-Pérez; validation, Jonathan Castañeda-Fernández;
visualization, Nerea López-Bouzas; writing—original draft preparation, M.
Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
writing—review and editing, M. Esther Del-Moral-Pérez, Nerea López-Bouzas, and
Jonathan Castañeda-Fernández
Funding
This research has not received external funding
Data Availability Statement
The data set used in this
study is available at reasonable request to the corresponding author
Ethics approval
The
intervention was carried out after obtaining permission from the Ethics
Committee of the University of Oviedo (37_RRI_2024). Additionally, the purpose
and procedures were explained to the teaching staff and families (see
explanatory document at: https://cutt.ly/geue42E1). The processing,
communication, and transfer of data were conducted in accordance with
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27
April 2016 on the protection of natural persons with regard to the processing
of personal data and on the free movement of such data (GDPR), as well as
Organic Law 3/2018 of 5 December on the Protection of Personal Data and
guarantee of digital rights (LOPDGDD). The research team maintains custody of
the anonymized data to ensure confidentiality.
Consent for publication
The
author has consented to the publication of the results obtained by means of the
corresponding consent forms.
Conflicts of interest
The
author declares that they have no conflict of interest
Rights and permissions
Open Access. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation,
distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source
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