Revista
de Ciencias Sociales (RCS)
Vol.
XXIX, No. 4, Octubre - Diciembre 2023. pp. 50-63
FCES
- LUZ ● ISSN: 1315-9518 ● ISSN-E: 2477-9431
Como citar: Arias-Pittman, J. A., Manrique-Quiñonez,
J. A., Espinoza-Morimoto, M., y Barrera-Loza, A. D. M. (2023). Learning styles and academic performance
in the digital era in Peruvian engineering students. Revista
De Ciencias Sociales, 29(4),
50-63.
Learning styles and academic performance in the
digital era in Peruvian engineering students
Arias-Pittman, José Augusto*
Manrique-Quiñonez, Javier
Alberto**
Espinoza-Morimoto, Manuel***
Barrera-Loza, Ana Doris
Magdalena****
Abstract
The main objective of the research
is to determine the relationship between learning styles and academic
performance, in addition to discriminating the predominant learning style. The
research is a descriptive correlational, non-experimental study. The target was
the 297 students attending the School of Industrial Engineering and the sample
of 233 surveyed. The Honey & Alonso - CHAEA - questionnaire with 20
questions for each style was applied. The questions analysed were those that
were answered with a "YES". It was determined that there is a
difference between the four styles, - p value (0.0298) < α (0.05) -; and that the reflective
and active styles are statistically significantly different; academic
performance is related to the four learning styles where the reflective style
has the greatest impact with a coefficient of 0.462 followed by the theoretical
style with a coefficient of 0.255; using Weka software in Machine Learning,
allowed us to reaffirm that the dominant learning style is reflective, which
will allow us to guide the actions of teaching and learning. It was
demonstrated that there is a relationship between learning styles and academic
performance ρ= 0.833 with (1-α) = 0.99.
Keywords: Learning styles; academic performance; CHAEA
Survey; teaching; students.
Estilos de aprendizaje y rendimiento académico en la
era digital en estudiantes peruanos de ingeniería
Resumen
El objetivo principal de la investigación es
determinar la relación entre los estilos de aprendizaje y el rendimiento
académico, además de discriminar el estilo de aprendizaje predominante. La
investigación es descriptiva correlacional, no experimental. El objetivo fueron
los 297 estudiantes que asisten a la Escuela de Ingeniería Industrial y la
muestra de 233 encuestados. Se aplicó el cuestionario Honey
& Alonso – CHAEA - con 20 preguntas para cada estilo. Las preguntas
analizadas fueron aquellas que fueron respondidas con un “SI”. Se determinó que
existe diferencia entre los cuatro estilos, valor p (0.0298) < α (0.05); y que los estilos reflexivo y activo son estadísticamente
significativamente diferentes; el rendimiento académico se relaciona con los
cuatro estilos de aprendizaje donde mayor impacto tiene el estilo reflexivo con
un coeficiente de 0.462 seguido del estilo teórico con un coeficiente de 0.255;
utilizar el software Weka en Machine Learning, permitió reafirmar que el estilo de aprendizaje
dominante es el reflexivo, lo que consentirá orientar las acciones de enseñanza
y aprendizaje. Se demostró que existe relación entre los estilos de aprendizaje
y el rendimiento académico ρ= 0.833 con (1-α) = 0.99.
Palabras clave: Estilos de aprendizaje; desempeño académico;
Encuesta CHAEA; enseñanza; estudiantes.
Academic performance is the indicator of students'
learning and the indicator of their educational quality. It is related to a
number of internal and external factors. At a worldwide level, many studies
emphasise academic performance at the university level in a context where have
evolved into knowledge and information and communication technologies.
Since the 1990s, higher education in Latin America has
shown great interest in educational quality, with the emergence of a new vision
of the market, the privatisation of public higher education, which led to an
interest in studying the determinants of academic performance, finding a great
diversity, among them: Personal factors (inherent to the student), social
(inherent to the social environment) and institutional (Garbanzo, 2007). Of the diversity of factors associated with academic
performance, the present research addresses only learning styles, as an inherent part of the cognitive conditions
referred to by Garbanzo in the personal factors.
There
is a variety of research on learning styles around the world: In Spain, Arias, Gonzáles &
García (2020), oriented to
identify the learning styles preferred by nursing students based on the
Honey-Alonso questionnaire of Learning Styles (CHAEA); in Mexico, Campos &
Campos (2018), administering a questionnaire of 44 questions identifies the
levels of preferences in 8 dimensions grouped in pairs as established by the
model of Felder and Silverman in 1988; sensory/intuitive, also visual/verbal,
likewise active/reflective and finally sequential/global.
Most
studies seek the relationship between learning style and academic performance,
it is imperative to contribute with research related to subjects taught in
higher education. Therefore, at present, teachers must acquire new teaching
strategies as indicated by Tarazona et al. (2021): “The
teacher must influence, according to the complexity of the subject, and the
individual and group characteristics, and differentiate which styles
predominate in the classroom” (p. 294); and Chambi-Choque, Manrique-Cienfuegos & Espinoza-Moreno
(2020): “Teachers and authorities
must know about Learning Styles and the forms of knowledge acquisition that
students put into practice” (p. 44).
However,
the teacher when developing his synchronous and asynchronous sessions using a
variety of media and techniques, does not guarantee that all students learn in
the same way, taking into account that in a session similar conditions are
considered globalising in a class; being the same study centre with the same
uniform conditions both in requirements, capabilities, age and even the same
teacher, results of evaluations are observed that confirm that not all students
achieve uniform and satisfactory learning.
This
evidence demonstrates that students have different learning capabilities or
rates, which explains that each student has their own learning style,
considering that they have different capabilities and that these evolve
according to their needs, circumstances and time (Espinoza-Poves, Miranda-Vilchez
& Chafloque-Céspedes, 2019; Esteves et al., 2020; Villacís et
al., 2020; Polo et al., 2022).
It
is necessary to consider that the academic performance results obtained by
students not only rely on learning styles, but can also be influenced by other
factors such as study habits, dysfunctional families, poor nutrition, students
who combine work and study, and unconvincing teaching strategies that cause
students to fail to achieve the learning that is imparted to them.
This
research has only taken into account the Learning Styles as a determining
factor in the academic performance of students, and our objective has been to
determine whether there is a relationship between learning styles and academic
performance and what are the predominant styles in Industrial Engineering
students from the first to the ninth cycle university, the collection of data
was conducted in the later years of 2021 through the Honey survey, whose author
states that “the fundamental bases on which the presentation of this
questionnaire is based are part of the cognitive approaches to learning” (Alonso, Gallego &
Honey, 1994, p. 107).
On
the other hand, education and the way of learning in current times are no
longer the same, the teacher is considered as a provider of new knowledge, and
students as the builders of their own knowledge, and all this has been
accentuated more in the “second decade of the XXI century, theories about
learning vary, they are readjusted to the [different] contexts, emphasizing
with respect to learning styles, there are different positions to achieve
evaluate and understand learning in the student body” (Cantú-Martínez
& Rojas-Márquez, 2018, p. 1).
One
of the factors that has a great influence on academic performance is learning
styles. Teachers, before entering the classroom, must know how students learn
their new knowledge, “students learn in different ways, an element that causes
each one to develop a learning style” (Tarazona et
al., 2021, p. 294). It is there where learning styles constitute a pedagogical
tool that makes students always motivated to receive new knowledge; therefore,
it is necessary to consider that “learning constitutes a process in which
knowledge is created in the mind of man through the transformation of
experience” (Fernandez, 2019, p. 3).
Nowadays,
organisations demand highly qualified professionals, who practice values and
principles, who are capable of generating continuous and autonomous learning,
who are proactive professionals, who are always designing new scenarios that
have an impact on the well-being of organisations and society, hence, “one
responsibility of teachers is to help students discover their style and learn
to adapt it to the experiences of each situation” (Canalejas
et al., 2005, p. 34).
In
addition, it is necessary that students should base their actions on an active
and independent participation in the new learning process, because they learn
in different ways, for this reason “learning styles are seen as a process of
change that occurs in the organism, in behaviour, cognitive-cognitive
abilities, motivation and emotions, as a result of the action or experience of
the individual” (Bobadilla et al., 2017, p. 6).
All
learning is acquired through experience, however students learn in different
ways, and this leads us to reflect on the multiple factors that determine the
ability to learn, hence “students' learning preferences have been indirectly
associated with student success in numerous university programmes” (Campos
& Campos, 2018, p. 89).
Finally,
we consider that learning styles should be understood as the ways in which
students acquire new knowledge, therefore “students learn more effectively when
they are taught using strategies according to their predominant learning
styles” (Jiménez et al., 2019, p. 3).
The
analysis of achievement is highly complex due to the many variables that
influence it internally and externally, from psychological and physiological to
pedagogical, socio-economic, family and especially educational. In the effort
to conceptualise achievement from the assessments that students are subjected
to, it is necessary to consider not only the individual student's approach but
also the effects as an influence that they receive from the educational and
family perspective (Martínez, Renés & Martínez,
2019).
There
are various factors that influence students' learning and achievement or academic
performance, including: Motivation, prior knowledge, aptitudes, beliefs,
personality and learning styles; however, the complexity of establishing the
most significant factors that favour performance can exclude some that,
although secondary, are still decisive (Cabrera
y Fariñas, 2007; Vivas,
Cabanilla & Vivas, 2019).
Research
concerning to academic performance conceptualises it as: 1) a quantitative
outcome; 2) a quantified or unquantified evaluative judgement on academic
training; or 3) a combined process and outcome, assuming performance as a
process and outcome, on the student's capabilities and 'know-how' (Polo &
Niño, 2018).
It
has been discovered that the academic performance of students is regular and at
the same time teachers are lacking knowledge of learning styles because most of
them are trained as engineers (Yana et al., 2021), which states that “the
higher the level of applicability of the different teaching styles, the better
and higher the academic performance of students” (p. 133), the knowledge of
these styles on the part of teachers could improve the academic performance of
students.
In
this research, the relationship between learning styles and academic
performance has been determined. Likewise, identify the predominant learning
style that is most related to the academic performance of Industrial
Engineering students at the “Universidad Nacional José Faustino Sánchez Carrión” in Peru".
Learning
strategies are sequences of procedures that allow acquiring, storing and using
knowledge (Pizano, 2012). This requires teachers to
know the strategies and the functions of each one of them and how they can be
used and complemented with motivation and work (Montes de Oca
& Machado, 2011).
According
to Honey and Mumford (1992); Guerra, Zuluaga & Saravia (2019); Esteves et al. (2020);
Villacís et al. (2020); Polo et al. (2022) each person will manifest
different ways of absorbing or assimilating knowledge to increase their
comprehensive training; their style when learning: Active, Reflective,
Theoretical, or Pragmatic; Therefore there will be Active learners: Those who
fully involve and without prejudice in new experiences, getting involved in the
affairs of others, as well as centering all
activities around them, characterized by being entertainers, improvisers,
risk-takers, spontaneous, adventurous, as well as creatives.
For
their part, the Reflective: They reflect on all the alternatives before making
a move or making a decision, they gather data by analysing it carefully, they
observe others and create a slightly distant, as well as condescending, climate
around them, they are characterized by being receptive, analytical, observant,
patient, prudent, as well as distant. Theorists: Refers to those who adapt and
integrate observations within logical and complex theories, they like to analyze and synthesize, as well as seek rationality and
objectivity, fleeing from the subjective and ambiguous; Therefore, they are
methodical, logical, objective, critical, structured, as well as systematic.
Finally,
the Pragmatic style: It refers to the fact that their strong point is the
practical application of ideas, they like to act quickly and safely on those
ideas, projects that attract them, not hesitating to put them into practice;
Unlike the rest of the styles, these learners are described as experimenters,
realistic, planners, organized, practical and direct.
The
students of the School of Industrial Engineering in their learning process
mostly use the reflective and pragmatic style, then, the appropriate strategy
to be used within a class session should consider, just like those pointed out
by Negrete (2012); and Flores et al. (2017):
a.
Consider the general characteristics
of the learners (level of cognitive development, background knowledge,
motivational factors, among others).
b. The type of knowledge domain in general and of
the curricular content to be addressed.
c.
The intentionality or goal to be
achieved and the cognitive and pedagogical activities to be undertaken by the
learner in order to achieve it.
d.
Constant monitoring of the teaching
process (of previously employed teaching strategies, if pertinent), as well as
of student progress and learning.
e.
Determine the inter-subjective context
(e.g. knowledge already shared) created with the learners up to that point, if
applicable.
The
reflective learning style is identified by people who prefer to record
experience by collecting data and analysing it carefully, being thoughtful,
conscientious, receptive, analytical, as well as exhaustive (Cantú-Martínez & Rojas-Márquez,
2018; Polo et al. 2022).
1. Methodology
The
research covers a non-experimental design in its descriptive correlational
variant, because we have determined the relationship between the variables
learning styles and academic performance. The population consisted of 297
students enrolled in the 2021-I academic semester from I to IX cycle at the
School of Industrial Engineering of the “Universidad Nacional José Faustino
Sánchez Carrión” in Peru, during the 14th week of
classes and was answered by 233 students; in this sense, the sample was
determined by convenience following the criteria of accessibility and
availability. The sample inclusion criteria considered the students present at
the time of the application of the survey.
The
CHAEA survey was used, which consists of 80 questions with two dichotomous
options (YES and NO). “YES” means agreeing with the question asked and “NO”
means disagreeing. Its execution was under the supervision of the teacher of
the subject at the time the instrument was applied. The questionnaire was
structured as a web form whose link was provided to the students at the
beginning of the virtual classes. Each learning style included 20 questions. The
distribution is detailed in Table 1.
Table 1
Distribution of items for each learning style
|
Number |
Active |
Reflective |
Theoretical |
Pragmatic |
|
1 |
3 |
10 |
2 |
1 |
|
2 |
5 |
16 |
4 |
8 |
|
3 |
7 |
18 |
6 |
12 |
|
4 |
9 |
19 |
11 |
14 |
|
5 |
13 |
28 |
15 |
22 |
|
6 |
20 |
31 |
17 |
24 |
|
7 |
26 |
32 |
21 |
30 |
|
8 |
27 |
23 |
34 |
38 |
|
9 |
35 |
36 |
25 |
40 |
|
10 |
37 |
39 |
29 |
47 |
|
11 |
41 |
42 |
33 |
52 |
|
12 |
43 |
44 |
45 |
53 |
|
13 |
46 |
49 |
50 |
56 |
|
14 |
48 |
55 |
54 |
57 |
|
15 |
51 |
58 |
60 |
59 |
|
16 |
61 |
63 |
64 |
62 |
|
17 |
67 |
65 |
66 |
68 |
|
18 |
74 |
69 |
71 |
72 |
|
19 |
75 |
70 |
78 |
73 |
|
20 |
77 |
79 |
80 |
76 |
Source: Own elaboration, 2022 from
Alonso et al. (1994).
The
results of the students' academic performance were provided by the academic
records office at the end of semester 2021-I. For statistical processing we
used ANOVA tool, to test the equality of the means of the students' learning
styles, this “procedure is based on the assumptions that the selected samples
are independent and the populations involved are normal with equal variance
(homoscedasticity condition), although unknown” (Durand & Ipiña, 2008, p. 317).
Complementary
to ANOVA, we used Tukey's criterion for pairwise comparison (Webster, 2005). Likewise,
the multiple linear regression test has also been used, which according to Levin
& Rubin (2011) “the correlation between two variables may be insufficient
to determine a reliable estimating equation; however, if we aggregate data from
more independent variables, we can determine an estimating equation that more
accurately describes the relationship” (p. 566).
The analysis performed with Machine Learning, which is
a software supported by algorithms belonging to the field of Artificial
Intelligence, is processed on high-performance computers, data mining identifies
patterns of behaviour housed in Big Data; this allows to run predictive
processes and arborisation for business decision making; here, it is sought not
to incur in type I error where the treatment having no effect, it is decided to
accept the null hypothesis; or type II error where the treatment if it has an
effect, we do not accept it and therefore reject the hypothesis. On the other
hand, once the data mining is executed, it is processed probabilistically for
decision making, as well as the hypothesis testing, and finally the
unsupervised process is executed.
To complete the structured statistical process, we
used Spearman's Rho test, which is a non-parametric measure to determine the
dependence relationship between two variables. For the processing of
the information, we have used statistical software such as: Excel 2019, XLSTAT
2022.1.2, Statistical Package for the Social Sciences (SPSS) version 25,
Minitab 18.1 and Machine Learnig - Weka 3.9.5.
2. Results and discussion
The CHAEA instrument
was tested, consisting of 80 questions for the four learning styles: Active
(1), Reflective (2), Theoretical (3) and Pragmatic (4), with 20 questions each.
The results obtained show the knowledge or proficiency in one or more learning
styles. The Reflective and Theoretical learning styles were the most preferred
by industrial engineering students with 27.3% and 26.1% respectively (see Table
2).
Table 2
Most preferred learning styles of Industrial
Engineering students
|
Styles |
Amount (*) |
% |
|
Active |
2,994 |
21.5% |
|
Reflective |
3,804 |
27.3% |
|
Theoretical |
3,632 |
26.1% |
|
Pragmatic |
3,491 |
25.1% |
|
Total |
13,921 |
100% |
Note: * The frequencies refer to the preference of each
style based on the “Yes” answers according to CHAEA. Includes the 233 students
surveyed in cycles I to IX.
Source: Own elaboration, 2022.
These results were analysed by the five procedures
detailed below:
2.1. Procedure 1: Analysis of Variance Test (ANOVA)
This test was applied to analyse whether the
proficiency of the four styles on average is similar or whether there is one
style that stands out. The hypotheses are:
Ho: µ1 = µ2 = µ3 = µ4; H1:
At least one of the styles is different from the others.
The results in Table 3, indicate that there is
sufficient evidence to support the claim that the four means are not equal,
concluding that the proficiency of the four styles shows some significant
difference. This is supported by the fact that the calculated "F" is
greater than the critical "F" and also that the p-value (0.0298) <
α
(0.05).
Table 3
ANOVA
analysis of variance test results
|
Source |
SS |
df |
MS |
F |
Fcritical |
p-value |
|
|
Between |
17715,94 |
3 |
5905,31 |
3,15 |
2,72 |
0,0298 |
Reject |
|
Within |
142462,3 |
76 |
1874,5 |
||||
|
Total |
160178,2 |
79 |
|
|
|
|
|
Note: Significance level α=5%.
Processed with SPSS version 25.
Source: Own elaboration, 2022.
2.2. Procedure 2: Tukey's test
In order to determine which styles show significant
difference, Tukey's pairwise comparison was performed, which indicates that the
reflective style and the active style are significantly different (not
including zero). The other pairs of styles show no significant difference.
The details are shown in Figure I.
Note: Processed in
Minitab 18.1.
Source: Own elaboration, 2022.
Figure I: Tukey's pairwise comparison
2.3. Procedure 3: Multiple linear regression test
For the modelling of the research we used the multiple
linear regression test for the Learning Styles and Academic Performance
variables. The highest mean value corresponds to the reflective
style and the lowest to the active style (see Table 4). The equation of the
multiple linear regression model is:
(1)
This equation will allow to theoretically establish
academic performance, with the assumption that the variable that determines it
is the student's mastery learning style.
Table 4
Summary for quantitative
variables
|
Variable |
Media |
Standard deviation |
|
E Active |
12,863 |
3,082 |
|
E Reflective |
16,339 |
2,236 |
|
E Theoretical |
15,541 |
2,624 |
|
E Pragmatic |
14,644 |
2,861 |
Note: Processed in XLSTAT.
Source: Own elaboration, 2022.
It can be seen from the equation that the Reflective
Style has the greatest impact on academic performance with a value of +0.462,
followed by the Theoretical Style with +0.255 (see Table 5). These two styles
are the most important for the academic performance of the students. The
Pragmatic Style has no impact on academic performance.
Table 5
Research
model parameters
|
Parameter |
Value |
Standard deviation |
Student's t-test |
Pr > t |
Lower limit 95 % |
Upper limit 95 % |
|
Intersection |
0,000 |
- |
- |
- |
- |
- |
|
X1 E Active |
0,146 |
0,052 |
2,788 |
0,006 |
0,043 |
0,249 |
|
X2 E Reflective |
0,462 |
0,062 |
7,442 |
0,0001 |
0,340 |
0,585 |
|
X3 E Theoretical |
0,255 |
0,068 |
3,745 |
0,000 |
0,121 |
0,389 |
|
X4 E Pragmatic |
-0,030 |
0,070 |
-0,424 |
0,672 |
-0,168 |
0,108 |
Note: Processed in XLSTAT.
Source: Own elaboration, 2022.
2.4. Procedure 4: Machine Learning - Weka
By performing an unsupervised statistical analysis
with the Machine Learning software Weka and processing the static information
contained in the cluster, without considering the supervision variable -
academic performance -, two centroids are determined which measure the
Euclidean distances forming nodes; after activating the unsupervised filters in
the pre-processes, the attribute from numeric to binary to consider the
dichotomous variables in the learning styles, and, sequentially selecting the
clustering option for the evaluation of the clusters, activating the simple
K-means algorithm selecting the use training set mode, it is found for the 233
students that the Reflective style (1, 1) and the Theoretical style (1, 1)
represent 82 % of the domain of the styles (see Figure II).
Note: Processed in Machine
Learning software Weka, using the data matrix of 233 surveys.
Source: Own elaboration, 2022.
Figure II: Identification of dominant
learning styles
When executing the split percentage evaluation, in
which the software discriminates and determines valid data and separates those
that have distorted and illogical responses, in the cropped sample of 153
students, it can be seen in Figure III, which is the Reflective style (1, 1)
with which students learn best, with 35% mastery of the style, compared to 65%
distributed for the other three styles.
Note: Processed in Machine Learning software Weka, using the
discriminated data matrix 153 surveys.
Source: Own elaboration, 2022.
Figure III: Identification of the
dominant learning style
2.5. Procedure 5: Spearman's Rho test
Once the results had been analysed using the four
previous procedures, the hypotheses set out in the research were validated. Spearman's
correlation model was chosen, which determines that there is a relationship
between learning styles and academic performance and that this relationship is
not due to chance, but is statistically significant. The following hypotheses
were put forward:
H0:
Learning styles are not related to academic performance in the digital era in
engineering students in Peru.
H1:
Learning styles are related to academic performance in the digital age in
engineering students in Peru.
Spearman's Rho test was performed with 181 pairs of
data, which were left by eliminating those that generated distortion in the
data, such as cases of high mastery of styles and low academic performance. A
correlation of 0.833 and a p-value = 0.000 were found (see Table 6). As the
p-value = 0.000 < 0.01 we can state, with 99% confidence, that there is a
relationship between learning styles and academic performance in the digital
age in engineering students in Peru, having a high positive correlation of
0.833 (83 %).
Table 6
Spearman's
Rho correlation
|
Spearman's Rho |
Style |
Academic performance multiple regression |
|
|
Style |
Correlation coefficient |
1,000 |
0,833* |
|
Sig (Bilateral) |
0,000 |
||
|
N |
181 |
181 |
|
|
Performance Acad. multiple regression |
Correlation coefficient |
0,833* |
1,000 |
|
Sig (Bilateral) |
0,000 |
||
|
N |
181 |
181 |
|
Note: * Correlation is
significant at the 0.01 level (bilateral). Processed in SPSS v25.
Source: Own elaboration, 2022.
Likewise, Figure IV shows the learning styles of
industrial engineering students, demonstrating that the predominant style is
reflective, followed by theoretical.
Note: Based on Kolb Model. Processed with Excel.
Source: Own elaboration, 2022.
Figure IV:
Learning styles of
industrial engineering students
The
research carried out determined a statistical value of the p-test value of
0.000 lower than the assumed significance level of 0.01, so it can be conclusively
state that there is a relationship between the variables learning styles and
performance, this coincides with Tarazona et al. (2021),
who state that there is a direct relationship between learning styles and
academic performance of students in the course of differential calculus of the
I cycle of the school of fluid mechanics of the Universidad Nacional Mayor de
San Marcos.
The
research by Chambi-Choque et al. (2020), indicates that learning styles in
nursing interns at the public university are dominated by reflective learning
(0.80), followed by theoretical learning (0.75), which coincides with the
present research, where it has been determined that the reflective style has
the greatest impact with a value of +0.462, followed by the theoretical style
with +0.255.
Unlike
this research, which determined that there is a relationship between the
variables of learning styles and academic performance, Vivas
et al. (2019); and Chambi-Choque et al. (2020), found
no significant statistical evidence of association between learning styles and
academic performance respectively.
Conclusions
The
present research determined the existence of a direct and significant
relationship between learning styles and academic performance in engineering
students in Peru. The statistical analysis shows that the reflective style has
a greater impact on student performance, followed by the theoretical style.
These results highlight the importance of considering individual learning
styles in engineering education processes to implement more effective teaching
strategies that improve academic performance.
The
study also found significant differences between the four styles evaluated
through variance analysis. Specifically, the reflective and active styles
showed greater variability among students. This finding provides evidence of
the distinctions that exist in the learning methods of industrial engineering
students.
Moreover,
multiple linear regression enabled the mathematical modeling
of the relationship between the main variables, confirming the positive effect
of the reflective and theoretical styles on performance. This model provides a
quantitative representation of the differentiated influence of each learning
style.
On
the other hand, unsupervised data processing with Machine Learning corroborated
the predominance of the reflective style among the students in the sample. This
Artificial Intelligence technique adds robustness to the identification of the
most frequent learning styles in this population.
In
summary, the findings agree with previous research that links the reflective
style with better academic performance. Therefore, it is valuable for teachers
to know the individual styles of their students to implement effective
educational strategies that positively enhance their performance.
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