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Revista de Ciencias Humanas,
Teoría Social y Pensamiento Crítico
Universidad Nacional Experimental Rafael María Baralt
Maracaibo, Venezuela
N°Septiembre - Diciembre
Abstract
We examined the multidimensional structure of constructs related to lear-
aim was to understand whether Geertz’s concept of depth description can
-
standardized instruments, bibliographic resources, conceptual understan-
ding, learning strategies, metacognitive regulation, cognitive performance,
affective engagement, and demographic factors to measure the perceived
-
-
-
rogeneous distribution patterns, the presence of significant outliers, and
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lations were identified between conceptual understanding and metacogni-
tive strategies, as well as between metacognitive and affective dimensions,
Keywords:
RESUMEN
Examinamos la estructura multidimensional de los constructos relacionados con
-
-
comprensión conceptual, estrategias de aprendizaje, regulación metacogniti
-
medir la percepción de una mejor absorción del objetivo de competencia en
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normal en varias escalas, particularmente dentro de las dimensiones cognitiva
Palabras claves: Derechos reproductivos, infertilidad, técnicas de
29/09/2025
MultidiMensional patterns of learning in
higher education: an evidence-based
statistical analysis on thickness
Patrones multidimensionales de aprendizaje en la educación supe-
rior: un análisis estadístico basado en la evidencia sobre la densidad
Patricia Moyota-Amaguaya
patricia.moyota@espoch.edu.ec
Este trabajo está depositado en Zenodo:
https://doi.org/10.5281/zenodo.21895447
Lina Morales-Rodas
lina.morales@espoch.edu.ec
María Eugenia Rodríguez-Durán
Daniel Hernán Millán-Ramos
hernan.millan@espoch.edu.ec
Patricia Moyota, María Rodríguez, Lina Morales y Daniel Milan
Multidimensional patterns of learning in higher education:...
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Introduction:
blended/
hybrid educational
strategies in srl
metacognitive processes in HE has
gained significant attention due to
the rapid expansion of digital and
-
ghlights that effective learning in HE
-
tional, and behavioral processes du-
ring blending activities (Broadbent,
“For this reason, blended learning, if
done well, may combine the bene-
fits afforded by online technologies,
with structure and social aspects of
face-to-face time, to give an overall
richer experience (Van Doorn & Van
environment provides flexibility, but
orientates students to a specific time
and location each week to attend
on-campus classes. It also allows
students to customise their learning,
while teaching staff are still able play a
pivotal role in providing structure, or-
ganisation, scaffolding, and time ma-
nagement to the learning experience
students should assume greater re-
-
-
-
most influential theoretical frameworks
technologies in teaching and learning
the process through which learners ac-
cognitive, motivational, and behavioral
processes in order to achieve specific
perspective views students as active
agents who engage in reflective learn-
ing practices, developing metacogni-
tive different kills and self-regulation
From a theoretical standpoint,
-
eral conceptual frameworks that
explain how learners manage their
process composed of three main
phases: forethought, performance,
-
thought phase, students set learning
goals, select strategies, and establish
expectations regarding their perfor-
-
students evaluate their outcomes
and adjust their strategies for future
adopted in studies examining learning
Within HE, the development of
self-regulation skills has become in-
-
ing demand for autonomous learning
-
ies have demonstrated that students
to achieve better academic out-
comes, demonstrate stronger intrinsic
motivation, and persist longer when
research indicates that self-regula-
academic performance in online and
their time, attention, and learning re-
-
phasizes the multidimensional nature
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between cognitive, metacognitive,
In this sense, self-regulated learn-
of cognitive strategies and includes
complex processes of emotional and
motivational regulation that influence
students’ persistence when engag-
ing with challenging academic tasks
perspective has allowed researchers
to gain a deeper understanding of
how learners manage their learning
-
learning environments has generat-
-
learning platforms, and artificial intel-
ligence-based educational tools have
to educational resources but also
interact with knowledge and regulate
their learning processes (Gambo &
In digital learning environments,
-
schedules, select appropriate learn-
ing resources, and monitor their aca-
shown that success in online learning
in terms of planning, time manage-
ment, and metacognitive monitoring
-
nologies such as artificial intelligence
also created new opportunities to
support the development of self-reg-
tools can provide personalized feed-
back and adaptive learning support
that enhances students’ metacog-
-
porting reflective and autonomous
-
plete new frame to introduce the idea
of thickness
thIckness to gIve epIsteMo-
logIcal dIMensIon to blen-
dIng/hybrId strategIes
-
kness does not come from statistical
and technical studies, nor from stu-
-
help to found interpretative anthropo-
more important to this argument, to
adds context and meaning to user
-
zes understanding from the actors’
perspectives rather than imposing
the center on motivation giving de-
sign decisions, based on uncovered
people thinking, feelings, and beha-
viors when interacting with products
become assumptions into evidences
to intuitive, meaningful, and impactful
-
on a different assumption about how
culture works, which is no contradic-
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as a cognitive process:
“The distinction here is an important
one and goes well beyond the narrow
view of qualitative as in an open-en-
ded survey question. Rather, studies
that are qualitative in nature genera-
te data about behaviors or attitudes
based on observing or hearing them
directly, whereas in quantitative stu-
dies, the data about the behavior or
attitudes in question are gathered
indirectly, through a measurement or
an instrument such as a survey or an
analytics tool. In field studies and usa-
bility testing, for example, researchers
directly observe how people use (or
do not use) technology to meet their
needs or to complete tasks. These
observations give them the ability to
ask questions, probe on behavior, or
possibly even adjust the study proto-
col to better meet study objectives.
-
In short, “thickness” in Geertz’s mo-
del is about depth of interpretation,
which gives not just a different tech-
turn raw observations into rich, cultu-
what epistemological enriching about
-
-
“The concept of culture I espouse,
and whose utility the essays below at-
tempt to demonstrate, is essentially a
-
ber, that man is an animal suspended
in webs of significance he himself has
spun, I take culture to be those webs,
and the analysis of it to be therefore
not an experimental science in search
of law but an interpretive one in search
of meaning. It is explication I am af-
ter, construing social expressions on
pronouncement, a doctrine in a clau-
se, demands itself some explication.
If you want to understand what a
science is, you should look in the first
instance not at its theories or its fin-
dings, and certainly not at what its
apologists say about it, you should
thickness
-
learners in digital environments with
the purpose of understanding and
optimizing learning processes (Hei-
enables researchers and educators to
-
teractions with learning materials, and
patterns of participation in academic
for thickness scopes because, besi-
-
vide empirical insights into learning
-
-
tal learning platforms, researchers can
-
ted with different learning strategies
and levels of self-regulation (de Barba
allows for a deeper understanding of
how students plan, monitor, and regu-
late their learning activities in digital
thickness approach, because it ena-
bles real-time observation of learning
behaviors based on data generated
-
tance, learning dashboards based
-
dback regarding academic progress,
allowing students to reflect on their
performance and adjust their learning
-
nitive reflection and promote more
effective self-regulated learning be-
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Several empirical studies have
demonstrated that educational inter-
experimental research indicates that
-
dents’ planning behaviors, time mana-
gement, and monitoring of academic
-
-
ning processes but also provides
From the thickness point of view,
one of the most significant develop-
ments in this field involves is the use
-
tion logs from digital platforms, be-
havioral data from online activities,
-
siological indicators associated with
allows researchers to develop more
sophisticated models capable of cap-
-
Methodology
In order to measure the perception
of an integrated tool, offering multiple
-
-
rent academic programs, ensuring a
heterogeneous sample suitable for
Data were collected using a set of
standardized instruments designed to
-
bibliographic resources, conceptual
understanding, learning strategies,
metacognitive regulation, cognitive
performance, affective engagement,
-
and correlation matrices to examine
distributional properties and biva-
regression and principal component
-
between cognitive, metacognitive,
-
-
ght the importance of self-regulated
learning, metacognitive awareness,
and student engagement in HE, parti-
-
diated learning environments (Broad-
also demonstrates that the integra-
tion of cognitive and metacognitive
-
dents’ academic performance and
-
more, emerging research emphasizes
-
patterns of self-regulated learning,
enabling more robust statistical mo-
deling of student learning processes
and blended learning, several studies
-
sis of learning behaviors provides
valuable insights into the complex in-
teractions between motivation, cog-
nition, and metacognition (Urbina et
addresses a significant gap in HE
-
tion of cognitive, metacognitive, and
contextual variables within a unified
statistical framework capable of ex-
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ting, or prediction in political science
the source of Geertz’s interpretive
-
tic descriptions that still suffer from
-
the appreciation initiated in this ma-
understanding that remains valid, or
-
minance of statistical models, even
-
-
-
-
-
-
-
-
-
-
-
-
tructs was performed using confir-
guarantee and establish the most sig-
case, self-perception of the English
language was determined as a de-
pendent variable, which allowed for
influence personal knowledge and
with an abbreviated name, as detailed
-
-
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N° 28 Septiembre – Diciembre (2026). 190-206
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Question ID
Timestamp i1
Email Address i2
Score i3
1. Age i4
2. Gender i5
3. Type of residence i6
4. Student residence status i7
5. Higher education institution you belong to i8
6. Type of degree program i9
7. Level of English you are studying i10
8. Identify three problems that cause you difficulty when reading and interpreting English
text * i11
9. Self-perception of your level of English i12
10. Motivation for reading (English) i13
11. Have you taken any previous courses or training in the English language? i14
12. Have you read material in English related to academic topics for your degree sub-
jects? i15
13. Have you read material in English related to academic topics in your English class? i16
14. If this type of material has NOT been included in your English class, how often would
you like to do so? i17
15. Which of the following texts do you read most often in English? i18
16. I have a stable and fast internet connection to carry out my online learning activities. i19
17. Access to online education improves my independent learning. i20
18. I find online audiovisual resources crucial to my learning. i21
19. Access to online material outside of campus allows me to structure my independent
learning. i22
20. I learn more efficiently when I can access online resources using different devices. i23
21. I find that technological resources improve my ability to learn and understand course
content. i24
22. I learn better when course content includes graphics, diagrams, and explanatory
videos. i25
23. I retain information more effectively when listening to audio explanations or lectures. i26
24. I prefer hands-on activities and interactive exercises that allow me to experiment with
the concepts I have learned. i27
25. I prefer course content to be presented in a logical and structured, step-by-step
manner. i28
26. I learn more effectively when I study on my own and at my own pace. i29
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27. I prefer to work in groups and discuss topics with other classmates to better under-
stand the content. i30
28. I ask myself questions about the text I intend to read. i31
29. I pay attention to bold and italicized text to identify key information. i32
30. I plan what strategies I will use when reading the text, such as highlighting or taking
notes. i33
31. I review the material briefly to know what the text will be about. i34
32. When I finish reading, I use a checklist to see how well I understood the text. i35
33. I manage my reading, that is, I plan the time, place, and tasks to be done. i36
34. When I read, I decide what to read carefully and what to ignore according to what is
requested. i37
35. I take notes while reading to better understand the text. i38
36. When the text becomes difficult, I read aloud to understand it better. i39
37. I use reference materials (e.g., a dictionary) to understand what I read. i40
38. I read the same text two or more times for better understanding. i41
39. I deduce the meaning of technical words in English based on the context in which
they are used i42
40. I group technical terms or concepts in English into categories or topics to facilitate
learning i43
41. I use mental images to remember and understand the text i44
42. I ask the teacher for help when I don’t understand something in the text i45
43. You cooperate with your classmates in a group activity i46
44. You use relaxation or breathing techniques to reduce your anxiety i47
45. You talk positively to yourself about your ability to read i48
46. You take risks to practice the English language i49
47. You control your nervousness or frustration when faced with a difficult text i50
48. You reward yourself when you complete a task correctly i51
Appearance Corresponding Question
English Proficiency
Bibliographic Re-
sources
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Connectivity
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access online resources using different devices)
Learning Strat-
egies
-
practical activities and interactive exercises that allow me to experiment
-
-
in groups and discuss topics with other classmates to better understand
the content)
Metacognitive
Strategies
-
what strategies I will use when reading the text, such as highlighting or
to be done)
Cognitive Strat-
egies
-
-
-
use mental images to remember and understand the text)
Affective Strat-
egies
task correctly)
Demographics
relationships between the observed
latent factors, which correspond to
-
-
-
riables and obtain a clearer and more
understandable representation of the
-
latent factors, improving interpreta-
-
tes the estimation of weights (factor
relationship between each observed
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variable and its corresponding latent
for understanding the relative impor-
tance of each variable in the model,
-
tions have the greatest influence on
each construct and optimizing data
from items is based on calculating
a weighted measure for each latent
factor, using the standardized factor
First, the standardized factor loadings
for each latent variable are obtained,
which indicate the strength and di-
rection of the relationship between
-
dardized factor loading of each item
procedure provides a scalar value
that represents the joint contribution
of the items to the measurement of
accurate representation of the latent
relationships between the items and
-
dition, obtaining these scalars opens
the door to calculating other des-
criptive statistics, such as the mean,
standard deviation, and percentiles,
which increases the robustness of the
provide a more detailed view of the
-
lars, which contributes to improving
-
sis of the scaled variables was carried
out, where boxplots, histograms, and
-
results
-
below the recommended threshold
-
associated with the latent variable
insignificant factor loadings, sugges-
-
from the latent demographic variable,
these items to evaluate the structure
of the model and improve the fit, ob-
taining:
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-
accurate representation of latent va-
relationships between items and the
homogeneous in the sample, with
-
ggests a consistent average level
bibliographic resources is stable but
-
the two previous scales and greater
-
-
re, this is one of the most problematic
scales from a statistical point of view:
-
which is consistent with self-regu-
strategies reflect high differentiation
between subjects and the presence
component of learning is stable and
consistent, suggesting good overall
Demographic aspects present a scale
composed of demographic variables,
-
sis was conducted on eight scales
associated with students’ competen-
cies, strategies, and characteristics:
-
-
nitive strategies, affective strategies,
scale, the shape of the distribution,
dispersion, presence of outliers, and
-
-
suggesting significant differences in
-
showed a unimodal distribution with
values concentrated in the low to me-
a moderate overall level of linguistic
competence in the sample, with few
bibliographic resources scale showed
a strong concentration in low values,
homogeneous use of academic sour-
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-
distribution, with moderate dispersion
behavior suggests that the variable
and consistent statistical performan-
-
-
suggests the possible coexistence of
differentiated subgroups within the
sample or problems in the construc-
tion of the index, such as the inclusion
this scale presents significant risks
for direct use in parametric statistical
-
tribution, with a main concentration
in mean values and a tail extending
-
dicates that, although most students
have basic levels of self-regulation,
there is a subgroup with advanced
For its part, the cognitive strate-
gies scale showed the greatest dis-
suggests a high degree of differen-
associated with levels of expertise
-
-
bution, with controlled dispersion and
no severe outliers, indicating stable
statistical behavior suitable for com-
-
graphic variables scale showed a dis-
values, with some isolated cases of
high scores, which is to be expected
given its composite and categorical
scales do not meet the assumption of
-
metries, and, in some cases, influen-
-
rect implications for the selection of
and cross-relationship between the
scaled aspects, revealing that there is
-
rroborate that the most appropriate
method for describing English profi-
-
riate scatter plots, univariate densi-
coefficients with significance levels,
allowing for the simultaneous evalua-
tion of: Direction of the relationship,
-
was performed between the scales of
-
-
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nitive strategies, affective strategies,
and demographic variables, using
results were visualized using a pair
graph that integrated scatter plots,
-
significant associations, although of
-
-
ciated with the use of bibliographic
-
-
sources showed positive associations
variable, showing moderate positive
associations with metacognitive (r =
negative relationship with cognitive
pattern suggests that access to and
use of digital environments is diffe-
For their part, metacognitive stra-
tegies showed a significant positive
association with affective strategies
-
tional interrelationship between cog-
correlations, which limits its expla-
between the variables and support
-
diated relationships between the
-
allows us to evaluate how different
Several models were compared, with
-
-
-
of residual freedom, suggesting that
adding more variables does not signi-
-
-
of residual freedom, indicating that
-
bles has the worst fit compared to the
-
adjusted deviance, suggesting that the
-
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Modelo Formula AIC Deviance Residual
Mejor Modelo
escalar_est_apren + esca-
lar_demograficos
1293.82 959.53 326
Peor Modelo
escalar_rec_biblio + escalar_
conect + escalar_est_apren
+ escalar_est_metcong +
escalar_est_cognitivas + es-
calar_est_afec + escalar_de-
mograficos
1301.74 953.50 321
Modelo Nulo 1 1309.06 1017.33 328
dIscussIon
-
tidimensional learning scales reveals
relevant patterns that contribute to
-
histograms and boxplots indicates
heterogeneous behavior across the
evaluated dimensions, suggesting
that students exhibit diverse levels
of cognitive processing, metacogniti-
ve regulation, and affective engage-
ment, related to different tools and
-
dimension presents the highest va-
-
to regulate their learning processes,
pattern is consistent with current
research on self-regulated learning
-
parities in students’ strategic learning
behaviors due to variations in digital
competence, academic prepared-
ness, and motivational regulation
-
conceptualize learning as an iterati-
ve process involving cognitive, mo-
tivational, and behavioral regulation,
in environments where learners must
-
veals meaningful relationships among
the evaluated learning dimensions,
highlighting the interconnected na-
ture of cognitive and metacognitive
processes in HE learning environ-
-
lation observed between conceptual
knowledge and metacognitive regu-
lation suggests that students with
deeper conceptual understanding are
-
nitoring, evaluation, and regulation of
supports recent empirical studies de-
monstrating that metacognitive regu-
higher-order thinking and self-mo-
the positive association identified
between metacognitive and affective
dimensions reinforces the idea that
emotional engagement and motiva-
with metacognitive control proces-
-
cognition also demonstrate grea-
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ter persistence, intrinsic motivation,
and emotional investment in learning
-
ging from the correlation matrix is the
contextual variables—such as biblio-
graphic resources and demographic
characteristics—and the core lear-
of academic resources does not ne-
cognitive engagement or the develo-
that access to digital or bibliographic
resources must be complemented
-
te active learning, reflective thinking,
and collaborative knowledge cons-
reinforce the argument that pedago-
gical scaffolding and metacognitive
guidance are essential elements for
fostering deeper learning processes
-
within the cognitive and metacogni-
tive scales also provides valuable in-
-
dent learning behaviors, which favors
learners with advanced self-regula-
tion skills or students who experience
substantial challenges in managing
-
demonstrates that students interac-
of these patterns through multivariate
-
tes to a more refined understanding of
student learning profiles and supports
the development of data-informed
educational interventions aimed at
improving academic performance and
reinforce the growing consensus in
the international literature that lear-
ning in HE should be understood as
a multidimensional construct that
integrates cognitive, metacogniti-
ve, affective, and contextual com-
capable of capturing these complex
interactions through advanced statis-
-
-
monstrates that significant relations-
hips among learning variables can be
evidence that supports the design of
more effective pedagogical strategies
-
thick description in social sciences,
related to perceive multidimensional
itself to introduce sense and accura-
-
tes to addressing a persistent gap
in the recent literature concerning
the empirical integration of cogniti-
ve, metacognitive, and affective di-
examine these dimensions indepen-
considers their interactions through
models of self-regulated learning and
highlights the importance of adopting
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tives, like those based on thick des-
criptions, when investigating student
learning processes in HE contexts
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