Revista Científica de Ingeniería, Industria y Arquitectura
Vol.8, Num.15 (jan-jun 2025) ISSN: 2737-6451
Suggested citation: Espinal-Lino, R., Rangel-Anchundia, L., Cedeño-
Zambrano, L., Peña-Miguel, N., & Echegaray, L. (2025). Analysis of the
attitudes and knowledge in digital environments of the students of the Faculty
of Engineering, Industry and Architecture – ULEAM. Revista Científica
FINIBUS – Ingeniería, Industria y Arquitectura. 8(15) 142-158
https://doi.org/10.56124/finibus.v8i15.015
Received: 25-06-2024 Review: 05-10-2024
Accepted: 15-12-2024 Published: 24-01-2025
DOI: https://doi.org/10.56124/finibus.v8i15.015
Received: 25-06-2024 Review: 05-10-2024
Accepted: 15-12-2024 Published: 24-01-2025
Scientific paper
Analysis of the attitudes and knowledge in digital
environments of the students of the Faculty of
Engineering, Industry and Architecture - ULEAM
Randy Espinal-Lino [1]
Lindsay Rangel-Anchundia [1,2]
Lia Cedeño-Zambrano [1]
Noemí Peña-Miguel[2]
Lázaro Echegaray[3]
[1] Universidad Laica Eloy Alfaro de Manabí – ULEAM. Facultad de Ingeniería, Industria y Arquitectura. Manta – Ecuador.
[2] Universidad del País Vasco/ Euskal Herriko Unibertsitatea (UPV/EHU). Bizkaia – España.
[3] Cámarabilbao University Business School. Bilbao – España.
Corresponding author: lindsay.rangel@uleam.edu.ec
Abstract
This study investigates the attitudes and knowledge of students from the Faculty of Engineering, Industry, and Architecture at
Universidad Laica Eloy Alfaro de Manabí (ULEAM) toward digital environments. This study employed a computerized survey
incorporating the ACMI questionnaire (Attitudes and Knowledge in Computer Media), augmented with a Likert scale to assess
the variables within the attitude dimension. Furthermore, open-ended questions were incorporated to stimulate discourse
regarding the results. The research involved a sample of 303 students chosen from a population of 2,763 students enrolled in
the faculty for the 2023-2 academic term. The assessment concentrated on three key dimensions: demographic information,
attitudes toward digital environments, and degree of digital competencies. Students exhibit a positive disposition and
understanding of digital media; yet, there exists an urgent need for continuous training to rectify existing shortcomings and
guarantee adequate professional preparedness in anticipation of an era characterized by relentless technological advancement.
Keywords: attitudes, knowledge, digital environments, digital skills, survey, students.
Análisis de las actitudes y conocimientos en entornos digitales de los estudiantes de la
Facultad de Ingeniería, Industria y Arquitectura - ULEAM
Resumen
Este estudio analiza las actitudes y conocimientos en entornos digitales de los estudiantes de la Facultad de Ingeniería, Industria
y Arquitectura de la Universidad Laica Eloy Alfaro de Manabí (ULEAM). Se realizó utilizando una encuesta en medio digital
basada en el cuestionario ACMI (Actitudes y Conocimientos en Medios Informáticos) con una ampliación a la escala Likert
de las variables a evaluar de la dimensión de actitudes, además de agregarse preguntas abiertas que alientan a la discusión de
los resultados. Se llevó a cabo a una muestra de 303 estudiantes de una población de 2763 estudiantes de la Facultad al periodo
académico 2023-2. Se evaluaron tres dimensiones principales: datos demográficos, actitudes hacia los entornos digitales y nivel
de competencias digitales. Se concluye que, aunque los estudiantes muestran una disposición positiva de actitudes y
conocimientos hacia el medio digital, existe una necesidad imperativa de capacitación continua para cerrar las brechas
existentes y asegurar una adecuada preparación profesional ante un futuro inminente de constante avance tecnológico.
Palabras Clave: actitudes, conocimientos, entornos digitales, competencias digitales, encuesta, estudiantes.
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1. Introduction
The integration of technology in the educational
environment has become an essential component in the
training of students. The development of digital skills and
knowledge of technological tools facilitates access to
information and enhances learning and preparation for the
labor market. Particularly, students in technical majors, such
as engineering, industry and architecture, are in a privileged
position to take advantage of these technologies, given that
their field of study is intrinsically linked to innovation and
the use of digital tools (George-Reyes, 2023; Vargas-
Murillo, 2019).
Digital competencies involve both the ability to use devices
and software, as well as the ability to navigate critically in
digital environments, solve technical problems and apply
technological knowledge effectively. These competencies
are increasingly valued in the professional environment,
where digitization and automation of processes are
predominant trends. But, despite their importance, there is a
significant gap in the level of digital literacy among students
from different disciplines and educational contexts (Reis et
al., 2019; Sánchez and Carrasco, 2021).
The Faculty of Engineering, Industry and Architecture of the
Universidad Laica Eloy Alfaro de Manabí (ULEAM) is no
stranger to these challenges. In this context, it is crucial to
analyze the attitudes and knowledge of students regarding
digital environments. This analysis will provide a clear
picture of their level of preparedness and identify areas for
improvement and specific training needs.
The objective of this article is to investigate the perception
and use of digital technologies by ULEAM students in their
learning process. The following research questions were
considered as fundamental to understand the current
situation and develop strategies to strengthen digital
education in the faculty: 1) to what extent are students
equipped with the necessary digital competencies? and, 2)
how do students’ attitudes towards technology influence
their academic performance and future career?
2. Literature Review
Interaction in the digital environment has profoundly
transformed the way people communicate, learn and work.
In this context, it is vital to examine prior studies on digital
competencies, attitudes towards technology, and the
utilization of digital environments in higher education to
expand the understanding of this issue.
The concept of digital competencies refers to the ability of
individuals to use digital technologies effectively. These
competencies are crucial in higher education as they
condition students to meet the challenges of the modern
working world. Reis et al. (2019) emphasizes the need to
clearly define terms such as "digital literacy" and "digital
competence" to avoid confusion and facilitate their
application in education. Their study revealed that, despite
technological advances, the lack of clear definitions remains
a significant problem.
Vargas-Murillo (2019) explored digital competencies in the
context of information and communication technologies
(ICT) in higher education. Their interpretive analytical
approach highlights the importance of these competencies
for educational and professional development. However, it
does not include statistical results, which limits the
generalizability of its conclusions.
In another study, Sánchez and Carrasco (2021) used an ad
hoc questionnaire to assess the degree of assimilation of
digital competencies among students of communication and
innovative education majors. Their findings indicated gender
differences in the development of these competencies,
highlighting the need for specific strategies to close these
gaps.
Attitudes towards technology also play a fundamental role in
the adoption and use of digital tools in education. George-
Reyes (2023) investigated the relationship between
computational thinking and digital literacy, highlighting the
importance of these skills in the training of 21st century
professionals. His systematic review of the literature
highlights the need to incorporate computational thinking in
modern education (Aguilar and Otuyemi, 2020).
Restrepo-Palacio and Segovia (2020) developed an
assessment instrument called "Digital Campus" to measure
students' digital competencies. Their results showed that,
although most students reached an acceptable level of digital
competence, there are still areas that require improvement,
especially in the technological dimension.
The use of digital environments in higher education has been
the subject of numerous studies. Saltos et al. (2019)
conducted a systematic review to assess the presence of
digital competencies in higher education institutions in Latin
America. Their results indicated an insufficient presence of
digital competencies in these institutions, highlighting the
need for greater preparation for both teachers and students.
León-Pérez et al. (2020) evaluated the self-perception of
digital skills of students at the Universidad Autónoma de
Querétaro, Mexico. Using a questionnaire, they found that
students use technology mainly for academic projects,
highlighting the need to integrate ICT more effectively into
classroom activities.
The literature review also reveals significant gaps in the
training and use of digital technologies. Alvarado (2020)
highlighted the differences in ICT management between
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
Engineering, Industry and Architecture – ULEAM
students and teachers, indicating a lack of updating and
continuous training in the use of these technologies. This
suggests the need to address these challenges to improve
learning in the digital era.
Finally, Bernate et al. (2021) analyzed digital competencies
in students majoring in Physical Education, finding
acceptable scores in several dimensions while also
highlighting issues that necessitate intervention. This study
emphasizes the significance of tailored curricula and
ongoing adaptation in the application of technology in
education.
3. Materials and methods
This cross-sectional study was designed as a descriptive
analysis with a quantitative approach, using a digital survey
to collect data on the attitudes and knowledge in digital
environments of students of the Faculty of Engineering,
Industry and Architecture of the Universidad Laica Eloy
Alfaro de Manabí (ULEAM). The methodological process
followed to carry out this research is detailed below.
For data collection, the ACMI (Attitudes and Knowledge in
Computer Media, from the Spanish Actitudes y
Conocimientos en Medios Informáticos) questionnaire
developed by Amorós-Poveda (2019) was adapted. This
instrument has proven to be effective in the assessment of
digital competencies in educational contexts. The ACMI
questionnaire is structured in three main dimensions: 1)
identification, which collects basic demographic data of
respondents, including age, gender, semester of study and
academic major; 2) attitudes, which assesses the perception
and disposition of students towards digital environments,
segmenting general attitudes, by age and academic major;
and 3) knowledge, which measures the level of digital
competencies of students, including previous computer
training, frequency of use of digital devices, and knowledge
and use of computer programs.
Revisions were implemented to the original questionnaire to
gather more information for the ensuing study. The Likert
scale was extended from 5 to 10 in the questions of the
attitudes dimension and the following open-ended questions
were added to gather the opinions of the students: 1) what
digital skills do you consider most important to develop as a
professional in your field?; 2) what kind of additional
training or support would you like to receive to improve your
skills in digital environments?; 3) how do you think
technological evolution will affect your professional field in
the coming years?; and 4) what recommendations would you
give to improve the integration of technology in the
educational process of the faculty?
The survey was distributed digitally to students of the
Faculty of Engineering, Industry and Architecture of
ULEAM. Participation was voluntary and the confidentiality
of the responses was guaranteed. The sample was composed
of students from different semesters and majors within the
faculty, with the objective of obtaining a representative view
of the level of digital competencies in this group.
The data collected through the questionnaire was analyzed
using descriptive statistical techniques to summarize and
present the findings. Tables, graphs and frequency analysis
were used to illustrate the results according to the
dimensions and sub-dimensions of the ACMI questionnaire.
For the inferential analysis of the data, the IBM SPSS
version 26 statistical software was used.
The attitude and knowledge dimensions were analyzed in
terms of several variables; mainly academic major and
semester of study. ANOVA analysis was applied to
determine the relationship between different variables and
the independence of the evaluated digital competencies
dimensions.
4. Results analysis
After analyzing the results, the most relevant findings that
contribute to the discussion were selected to be shown in this
section of the study.
A total of 303 responses were obtained from the population
of 2763 students in the Faculty of Engineering, Industry and
Architecture for the study period of 2023-2. Of the sample
analyzed, 4 questionnaire responses were considered as
missing data, since 4 people did not enter their academic
major. Table 1 shows the sample obtained, segmented by
academic major.
The Cronbach's alpha of the survey is 0.934. Thus, the
questions asked with the Likert scale have reliable results, as
shown in Table 2.
The data collected from the surveys was exported to
Microsoft Excel to code the qualitative responses into
quantitative ones. Then, the database was entered into IBM
SPSS version 26 software to obtain frequency tables and
graphs of the responses for their descriptive analysis. In
addition, in the descriptive analysis segmented by semester
and academic major in the attitudes dimension, the Chi-
Square statistical test was performed to examine the
relationships between variables and to evaluate the
independence between categories and determine if there are
statistically significant differences.
One-factor ANOVA was applied to compare means between
groups, while Levene's test was used to confirm assumptions
and post hoc with Games-Howell to ensure the validity of
the analyses and address possible inequalities in variances.
The data complies with the assumption of normal
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distribution necessary for applying ANOVA, as it aligns
with the Central Limit Theorem (CLT), which states that, given a sufficiently large sample, the mean distribution of
the data resembles a normal distribution (Delgado, 2022).
Table 1: Sample obtained segmented by academic major.
Pursued academic major
Frequency
Percent
Valid percent
Cumulative
percentage
Valid
Civil Engineering
48
15,8
16,1
16,1
Maritime Engineering
25
8,3
8,4
24,4
Electricity
33
10,9
11,0
35,5
Architecture
72
23,8
24,1
59,5
Industrial Engineering
96
31,7
32,1
91,6
Food Engineering
25
8,3
8,4
100,0
Total
299
98,7
100,0
Lost
System
4
1,3
Total
303
100,0
Table 2: Reliability statistics of the Likert scale questions
Reliability statistics
Cronbach's
alpha
Cronbach's alpha based on
standardized items
N elements
,934
,929
70
The ANOVA decision rule is: if the significance (Sig.) value
is less than or equal to 0.05, the null hypothesis of equality
of variances is rejected, indicating that variances are
unequal, so post hoc tests such as Games-Howell, which do
not require equality of variances, are used, providing more
robust and reliable results in these cases. This ensures that
conclusions are accurate and that significant differences are
correctly detected.
Each question in these objectives has its Chi-Square
statistical test table, which shows the value to determine
whether or not significant differences exist, in the cross cell
of the "Asymptotic significance (bilateral)" column and the
"Pearson's Chi-Square" row. The decision rule is: if the value
is less than or equal to 0.05, there are significant differences;
if it is greater than 0.05, there are no significant differences.
Attitudes in relation to the semester
This section seeks to understand the relationship between
students’ attitudes and their semester of study. As such, the
hypotheses are as follows:
• Ho: Students' attitudes towards the computer
environment remain the same in relation to the
semester they are studying.
• H1: Students' attitudes towards the computer
environment have significant differences in
relation to the semester they are studying.
Table 3 shows the data between the pairs of attitude
adjectives towards computer use and their relationship to a
student’s current semester of study. Most of the pairs do not
show significant differences, given that the p-values are
greater than 0.05, and thus, the null hypothesis was accepted.
This suggests that attitudes towards the computer medium
are similar among the different semesters of study of the
sample. However, the pairs of adjectives [Inaccessible—
Accessible] and [Individual—Group] show significant
differences (p < 0.05), and for these two cases, the null
hypothesis was rejected.
Table 3: Chi-Square test on pairs of adjectives [attitudes], in relation to the semester
Pair of adjectives
df
Asymptotic significance (bilateral)
α
Decision rule
Boring—Entertaining
81
0,515
0.05
No
Clumsy—Agile
81
0,654
0.05
No
Ineffective—Effective
81
0,286
0.05
No
Worthless—Valuable
81
0,640
0.05
No
Inaccurate—Precise
81
0,716
0.05
No
Stupid—Smart
81
0,313
0.05
No
Inaccesible—Accesible
81
0,013
0.05
Yes
Individual—Group
81
0,010
0.05
Yes
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
Engineering, Industry and Architecture – ULEAM
As seen on Table 4, Levene's test shows that the pair of
adjectives [Boring—Entertaining] has a significance level of
0.068; thus, the null hypothesis was accepted and the
homogeneity of variance in this group was confirmed. On
the other hand, for the rest of the pairs of adjectives, the
significance level was lower than 0.05, which evidences the
existence of differences in the groups’ variance. Likewise,
the ANOVA results indicate that all the significances of the
pairs of adjectives selected are less than 0.05; as such, the
null hypothesis was rejected, revealing statistically
significant differences between attitudes and the semester
attended by the students.
Table 4: Levene's test [attitudes], in relation to semester and ANOVA significance
Pair of adjectives
Levene’s test
Sig.
Sig. between
groups
Boring—Entertaining
1,797
0,068
0,033
Clumsy—Agile
4,619
0,000
0,020
Ineffective—Effective
5,785
0,000
0,008
Worthless—Valuable
3,227
0,001
0,048
Inaccurate—Precise
3,822
0,000
0,024
Stupid—Smart
5,024
0,000
0,043
Inaccesible—Accesible
2,479
0,010
0,035
Individual—Group
2,163
0,025
0,010
Once the significant differences between the variables
mentioned above were verified, the Games-Howell post hoc
test was used to identify the groups that differ. This test is
appropriate for the case, since it does not require
homogeneity of variances or equality in sample sizes. The
Games-Howell results indicate significant differences in
attitudes in relation to the semester of study, as shown in
Table 5.
Table 5: Results of the Games-Howell Post Hoc test [attitudes & semester]
Dependent variable
Semester
8
10
Boring—Entertaining
3
0,025
-
Clumsy—Agile
2
0,007
-
3
0,013
-
Ineffective—Effective
2
0,019
0,007
3
0,003
0,001
Worthless—Valuable
3
0,022
-
Inaccurate—Precise
2
0,045
0,004
3
0,033
0,005
Stupid—Smart
2
0,046
-
3
0,009
-
Inaccesible—Accesible
2
-
0,001
3
-
0,005
Individual—Group
-
-
-
To facilitate the understanding of the findings, Figure 1 and
Figure 2 highlight the most relevant graphs that show the
data count and the differences between the attitudes to the
computer environment of the different semesters indicated
by the post hoc test.
The responses were mostly positive from the higher
semesters, with semesters 8 and 10 standing out. On the other
hand, the lower semesters do register negative responses,
although they still have a strong presence of positive
responses. Conversely, the lower semesters tend to have
negative responses with a few favorable exceptions.
On Figure 2, similar to the previous graph, the higher
semesters do not register negative responses; however,
semesters 2 and 3 show a lower presence in the negative side
and present more positive responses.
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Figure 1: Bar chart of attitudes [Inaccessible—Accessible], in relation to the semester
Figure 2: Bar chart of attitudes [Inaccurate—Accurate], in relation to the semester
Attitudes in relation to the academic major
This section seeks to understand the relationship between
students’ attitudes and their selected academic major. As
such, the hypotheses are as follows:
• Ho: Students' attitudes towards the computer
environment remain the same in relation to
their selected academic major.
• H1: Students' attitudes towards the computer
environment have significant differences in
relation to their selected academic major.
Table 6 shows the data between the pairs of attitude
adjectives towards computer use and their relationship to a
student’s academic major. Most pairs present a p-value less
than 0.05, evidencing significant differences in the analyzed
variables. In contrast, the pairs [Worthless—Valuable],
[Useless—Useful], [Stupid—Smart], [Awkward—
Comfortable] and [Detrimental—Beneficial] show a p-value
greater than 0.05, demonstrating no significant differences.
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
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Table 6: Chi-Square test on pairs of adjectives [attitudes], in relation to the academic major
Pair of adjectives
df
Asymptotic significance (bilateral)
α
Decision rule
Unnecessary—Necessary
45
0,018
0.05
Yes
Ineffective—Effective
45
0,004
0.05
Yes
Worthless—Valuable
45
0,186
0.05
No
Impractical—Practical
45
0,000
0.05
Yes
Useless—Useful
45
0,411
0.05
No
Hindering—Facilitating
45
0,001
0.05
Yes
Trivial—Important
45
0,030
0.05
Yes
Inaccurate—Precise
45
0,000
0.05
Yes
Stupid—Smart
45
0,075
0.05
No
Awkward—Comfortable
45
0,104
0.05
No
Discouraging—Motivating
45
0,010
0.05
Yes
Complex—Friendly
45
0,004
0.05
Yes
Detrimental—Beneficial
45
0,290
0.05
No
As seen on Table 7, Levene's test shows that the pairs of
adjectives [Discouraging-Motivating] and [Detrimental-
Beneficial] have a significance level higher than 0.05; thus,
the null hypothesis was accepted, confirming the
homogeneity of variance in these groups. For the other pairs,
the significance was less than 0.05, which indicates
differences in the variances between groups. In addition, the
ANOVA results reveal that all pairs of adjectives have
significance values of less than 0.05. As such, the null
hypothesis was rejected, showing significant differences
between attitudes toward the computer environment and the
academic major of the students.
Table 7: Levene's test [attitudes], in relation to the academic major and ANOVA significance
Pair of adjectives
Levene’s test
Sig.
Sig. between
groups
Unnecessary—Necessary
5,024
0,000
0,005
Ineffective—Effective
8,797
0,000
0,000
Worthless—Valuable
6,815
0,000
0,001
Impractical—Practical
3,903
0,002
0,000
Useless—Useful
7,432
0,000
0,008
Hindering—Facilitating
3,640
0,003
0,000
Trivial—Important
5,191
0,000
0,002
Inaccurate—Precise
9,700
0,000
0,000
Stupid—Smart
5,638
0,000
0,002
Awkward—Comfortable
2,370
0.039
0,016
Discouraging—Motivating
1,594
0,162
0,010
Complex—Friendly
2,748
0,019
0,025
Detrimental—Beneficial
1,793
0,114
0,017
The Games-Howell post hoc test highlights Architecture as
the academic major that varies the most with respect to the
other majors in all pairs of attitude adjectives. Of all these
pairs, the most relevant one is [Hindering—Facilitating],
which indicates that there are significant differences between
Architecture and Civil Engineering, Maritime Engineering,
Electricity and Industrial Engineering.
The only exception is the adjective pair [Inaccurate—
Precise], which indicates that there are differences between
Architecture, Civil Engineering and Industrial Engineering;
and between Maritime Engineering and Industrial
Engineering. See Figure 3 and Figure 4 accordingly.
The Architecture major shows a notable trend towards
positive responses, especially in the category "Totally
facilitating," where it concentrates the majority of its
responses. Industrial Engineering follows in second place in
this category. In general, all majors tend to concentrate the
most responses in positive attitude categories, both
intermediate and high.
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Table 8: Results of the Games-Howell Post Hoc test [attitudes & academic major]
Dependent variable
Academic major
Civil
engineering
Maritime
engineering
Electricity
Architecture
Industrial
engineering
Food
engineering
Unnecessary—
Necessary
Architecture
0,017
-
0,013
-
0,041
-
Ineffective—
Effective
Architecture
-
-
0.003
-
0,011
-
Worthless—Valuable
Architecture
0,014
-
0,033
-
0,003
-
Impractical—
Practical
Architecture
0,020
-
0,001
-
-
-
Useless—Useful
Architecture
-
-
-
-
0,022
-
Hindering—
Facilitating
Architecture
0,030
0,025
0,001
-
0,044
-
Trivial—Important
Architecture
0,028
-
0,015
-
-
-
Inaccurate—Precise
Maritime
Engineering
-
-
-
-
0,034
-
Architecture
0,016
-
-
-
0,000
-
Stupid—Smart
Architecture
0,015
-
0,022
-
0,024
-
Awkward—
Comfortable
Architecture
-
-
0,034
-
-
-
Discouraging—
Motivating
Electricity
-
-
-
0,047
0,036
-
Complex—Friendly
N/A
-
-
-
-
-
-
Detrimental—
Beneficial
Architecture
-
-
0,402
-
0,281
-
Figure 3: Bar chart of attitudes [Hindering—Facilitating], in relation to the academic major
Figure 4 shows that, in most categories, Industrial
Engineering has a higher response count compared to
Maritime Engineering and shows a trend towards positive
responses. However, in the category "Totally precise,"
Architecture outperforms Industrial Engineering, registering
the highest number of responses in this category.
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
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Figure 4: Bar chart of attitudes [Inaccurate—Precise], in relation to the academic major
Knowledge in relation to the semester
This section seeks to understand the relationship between
students’ knowledge and their semester of study. As such,
the hypotheses are as follows:
• Ho: Students' knowledge towards the computer
environment remains the same in relation to the
semester they are studying.
• H1: Students' knowledge towards the computer
environment has significant differences in
relation to the semester they are studying.
Table 9 shows the data between knowledge and frequency of
program use by semester, with mixed results. The Database,
Simulation and Hypertexts programs have a p-value greater
than 0.05; as such, the knowledge and frequency of use of
these programs is similar between semesters. In contrast, for
the rest of the programs, there are significant differences in
knowledge and frequency of use between semesters (p <
0.05).
Table 9: Chi-Square test on programs [knowledge], in relation to the semester
Programs
df
Asymptotic significance (bilateral)
α
Decision rule
Text processors
36
0,046
0,05
Yes
Database
36
0,151
0,05
No
Spreadsheets
36
0,003
0,05
Yes
Drawing programs
36
0,004
0,05
Yes
Simulation
36
0,206
0,05
No
Hypertexts
36
0,163
0,05
No
Multimedia
36
0,038
0,05
Yes
Tutorial programs
36
0,013
0,05
Yes
Design programs
36
0,000
0,05
Yes
Modeling programs
36
0,000
0,05
Yes
As seen on Table 10, Levene's test shows that the Hypertexts,
Multimedia, Drawing, Design and Modeling programs have
a significance lower than 0.05, evidencing differences in the
variances between the groups for these programs. In contrast,
the rest of the programs present a significance level higher
than 0.05, demonstrating the homogeneity in the variances
of these groups.
ANOVA results indicate that the value of the significances
in most of the selected programs is less than 0.05. Thus, there
are significant differences between the students’ knowledge
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of these programs and their current semester of study,
differing only on the Simulation and Hypertexts programs, whose significance values were greater than 0.05, indicating
no significant differences.
Table 10: Levene's test [knowledge], in relation to the semester and ANOVA significance
Programs
Levene’s test
Sig.
Sig. between
groups
Text processors
1,387
0,193
0,000
Database
0,503
0,872
0,020
Spreadsheets
1,386
0,194
0,000
Drawing programs
2,923
0,002
0,010
Simulation
1,582
0,120
0,120
Hypertexts
2,048
0,034
0,134
Multimedia
2,508
0,009
0,001
Tutorial programs
1,671
0,096
0,004
Design programs
4,243
0,000
0,000
Modeling programs
3,982
0,000
0,000
The Games-Howell test shows significant differences in the
use of programs between students of different semesters,
except in Simulation programs. The most notable differences
are found in Spreadsheets, where students in semester 8
differ from those in semesters 1, 2, 3 and 5. Differences are
also observed in Modeling programs, where students in
semester 6 differ from those in semesters 1, 2, 3, 5 and 8.
These results are detailed in Table 11 and illustrated in
Figures 5 and 6.
Table 11: Results of the Games-Howell Post Hoc test [programs & semester]
Dependent variable
Semester
6
7
8
9
10
Text processors
1
0,002
0,017
0,001
-
-
3
0,019
-
0,011
-
-
Database
1
-
0,025
0,028
-
-
Spreadsheets
1
0,018
-
0,004
-
-
2
-
-
0,004
-
-
3
0,045
-
0,004
-
-
5
-
-
0,034
-
-
Drawing programs
2
0,023
-
-
-
-
Simulation
N/A
-
-
-
-
-
Hypertexts
5
-
-
-
0,009
-
Multimedia
2
0,000
-
0,003
-
0,032
Tutorial programs
1
-
-
-
0,015
-
2
-
-
-
0,014
-
5
-
-
-
0,006
-
Design programs
2
0,000
0,002
-
-
0,018
3
0,002
-
-
-
-
5
0,019
-
-
-
-
6
-
-
0,008
-
-
Modeling programs
1
0,035
-
-
-
-
2
0,000
-
-
-
0,011
3
0,000
-
-
-
-
5
0,027
-
-
-
-
6
-
-
0,036
-
-
As seen on Figure 5, Semester 8 students show a more
balanced use of spreadsheets, with significant counts in the
"Frequently" and "Very Frequently" categories, and no
records in the "Never" and "Rarely" categories, denoting a
clear evolution in this program’s use over the semesters. In
parallel, students in semester 2 also have significant counts
in the most frequent categories, even surpassing those in
semester 8 in one of them, which could reflect the increasing
complexity of academic tasks and greater digital
competencies acquired over time. Figure 6 suggests that, as
students advance in their training, particularly in semester 6,
they increase their use of modeling programs. This may be
attributed to the necessity of this digital expertise in more
advanced semesters or particular projects that require it.
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
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Figure 5: Bar chart of Spreadsheets’ use, in relation to the semester
Figure 6: Bar chart of Modeling programs’ use, in relation to the semester
Knowledge in relation to the academic major
This section seeks to understand the relationship between
students’ knowledge and their selected academic major. As
such, the hypotheses are as follows:
• Ho: Students' knowledge towards the computer
environment remains the same in relation to
their selected academic major.
• H1: Students' knowledge towards the computer
environment has significant differences in
relation to their selected academic major.
Table 12 shows the data between knowledge and frequency
of program use by academic major, with mixed results. Text
processors, Hypertexts, Multimedia and Tutorial programs
have a p-value higher than 0.05, indicating that knowledge
in the use of these programs is similar among academic
majors. In contrast, for the rest of the unmentioned programs,
there are significant differences in the knowledge of their use
between majors (p < 0.05).
As seen on Table 13 Levene's test shows that the Text
processors, Drawing, Design and Modeling programs have a
significance level of less than 0.05, indicating differences in
the variances between groups. For the other programs, the
significance level is greater than 0.05, thus confirming the
null hypothesis and indicating homogeneity of variances
between groups. ANOVA results show significant
differences in the knowledge of use of most of the programs
between majors (p < 0.05), except for Hypertexts and
Tutorial programs, where the significance values are greater
than 0.05. For these two programs, the null hypothesis was
accepted, evidencing no significant differences.
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Table 12: Chi-Square test on programs [knowledge], in relation to the academic major
Programs
df
Asymptotic significance (bilateral)
α
Decision rule
Text processors
20
0,081
0,05
No
Database
20
0,025
0,05
Yes
Spreadsheets
20
0,008
0,05
Yes
Drawing programs
20
0,000
0,05
Yes
Simulation
20
0,050
0,05
Yes
Hypertexts
20
0,720
0,05
No
Multimedia
20
0,058
0,05
No
Tutorial programs
20
0,318
0,05
No
Design programs
20
0,000
0,05
Yes
Modeling programs
20
0,000
0,05
Yes
Table 13: Levene's test [knowledge], in relation to the academic major and ANOVA significance
Programs
Levene’s test
Sig.
Sig. between
groups
Text processors
3,377
0,006
0,030
Database
0,999
0,418
0,019
Spreadsheets
0,487
0,786
0,006
Drawing programs
6,329
0,000
0,000
Simulation
1,001
0,417
0,002
Hypertexts
0,283
0,922
0,224
Multimedia
1,500
0,190
0,006
Tutorial programs
0,329
0,895
0,402
Design programs
4,396
0,001
0,000
Modeling programs
3,196
0,008
0,000
The Games-Howell test highlights most of the programs with
significant differences, except for Hypertexts and Tutorial
programs. As seen on Table 14, Industrial Engineering
stands out for presenting more differences in the use of
programs compared to other majors, especially in Design
programs and Modeling programs, where it is observed that
this major differs from the rest, except for Food Engineering.
Table 14: Results of the Games-Howell Post Hoc test [programs & academic major]
Dependent variable
Academic major
Civil
engineer
ing
Maritime
engineering
Electrici
ty
Architectu
re
Industrial
engineering
Food
engineering
Text processors
Industrial
engineering
0,002
-
-
-
-
-
Database
Civil Engineering
-
-
-
0,025
-
-
Spreadsheets
Civil Engineering
-
-
-
0,005
0,008
-
Drawing programs
Civil Engineering
-
-
-
-
0,000
-
Architecture
-
-
0,026
-
0,000
0,023
Simulation
Industrial
engineering
0,003
-
0,019
-
-
-
Hypertexts
N/A
-
-
-
-
-
-
Multimedia
Architecture
-
-
-
-
0,003
-
Tutorial programs
N/A
-
-
-
-
-
-
Design programs
Industrial
engineering
0,000
0,021
0,000
0,000
-
-
Architecture
-
-
-
-
-
0,007
Modeling programs
Industrial
engineering
0,000
0,026
0,000
0,000
-
-
Architecture
-
-
-
-
-
0,050
Figure 7 and Figure 8 show that, in general, all the majors
show a low incidence in the use of Design and Modeling
programs. A notable exception to this statement is the
Industrial Engineering major, which is present in all the
categories of frequency of use, being only surpassed in the
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Analysis of the attitudes and knowledge in digital environments of the students of the Faculty of
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"Very Frequent" use of both programs by the Architecture
major.
Figure 7: Bar chart of Design programs’ use, in relation to the academic major
Figure 8: Bar chart of Modeling programs’ use, in relation to the academic major
Analysis Overview
The attitude towards the computer medium is generally
positive, since the computer represents for the students a
TOTALLY necessary, effective, valuable, useful and
intelligent instrument (Med = 10), as well as being VERY
entertaining, agile, pleasant, practical, facilitating,
controllable, comfortable and beneficial (Med = 9), it is also
considered to be VERY manageable, simple, educational
and essential (Med = 8). The students also consider that the
computer is a socializing instrument (Med = 7), but of
individual use (Med = 5).
As seen on Figure 9 the pair of attitude adjectives
[Unnecessary—Necessary], has a cumulative percentage of
87.46% in positive responses, which corroborates that the
vast majority of students consider the computer media
provided by the computer as Necessary.
More than half of the students qualify the computer as an
individual medium, with 55.11% of accumulated percentage
of negative answers, leaving the rest with an accumulated
percentage of 44.89% who consider, on the contrary, that it
is a group medium
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Table 15: Attitudes about the computer
Pair of adjectives
Mo
M
Z min.
Z max.
Median
Boring—Entertaining
10
8,01
1
10
9,00
Rigid—Flexible
10
6,92
1
10
7,00
Unfair—Manageable
10
7,85
1
10
8,00
Clumsy—Agile
10
8,33
1
10
9,00
Unnecessary—Necessary
10
8,52
1
10
10,00
Unpleasant—Pleasant
10
8,13
1
10
9,00
Ineffective—Effective
10
8,63
1
10
10,00
Complicated—Simple
10
7,38
1
10
8,00
Worthless—Valuable
10
8,63
1
10
10,00
Malignant—Educational
10
7,87
1
10
8,00
Impractical—Practical
10
8,49
1
10
9,00
Useless—Useful
10
8,75
1
10
10,00
Hindering—Facilitating
10
8,36
1
10
9,00
Trivial—Important
10
8,63
1
10
9,00
Uncontrollable—Controllable
10
8,35
1
10
9,00
Inaccurate—Precise
10
8,55
1
10
9,00
Expendable—Essential
10
7,80
1
10
8,00
Isolator—Socializer
10
6,62
1
10
7,00
Stupid—Smart
10
8,60
1
10
10,00
Awkward—Comfortable
10
8,00
1
10
9,00
Discouraging—Motivating
10
7,80
1
10
8,00
Usual—Novel
10
7,23
1
10
8,00
Complex—Friendly
10
7,73
1
10
8,00
Inaccesible—Accesible
10
8,00
1
10
8,00
Individual—Group
5
5,48
1
10
5,00
Detrimental—Beneficial
10
8,33
1
10
9,00
Where: 1= Totally negative; 2= Very negative; 3= Quite negative; 4= Negative; 5= Somewhat negative;
6= Somewhat positive; 7= Positive; 8= Quite positive; 9= Very positive; 10= Totally positive.
Figure 9: Bar chart of attitude [Unnecessary-Necessary]
Figure 10: Bar chart of attitude [Individual—Group
Table 16 results show that Text processors, Database,
Spreadsheets, Videogames, Simulation, Hypertexts,
Drawing, Tutorial, Design and Modeling programs are
FREQUENTLY used by students (Med = 4) and multimedia
programs are VERY FREQUENTLY used (Med = 5).
Programs such as hypertexts have a significantly lower
usage, being used RARELY or OCCASIONALLY (M =
2.73 and Med = 3).
A 49.17% of the students use Hypertexts programs
infrequently or not at all, while 37.29% of the students
expressed that they use this program frequently, leaving the
remaining 13.53% with an occasional frequency of use.
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Table 16: Knowledge about the use of programs
Program use
Mo
M
Z min.
Z max.
Median
Text processors
5
3,92
1
5
4,00
Database
4
3,45
1
5
4,00
Spreadsheets
4
4,01
1
5
4,00
Drawing programs
5
3,84
1
5
4,00
Videogames
5
3,81
1
5
4,00
Simulation
5
3,57
1
5
4,00
Hypertexts
1
2,73
1
5
3,00
Multimedia
5
4,17
1
5
5,00
Tutorial programs
5
3,51
1
5
4,00
Design programs
5
3,85
1
5
4,00
Modeling programs
5
3,69
1
5
4,00
Where: 1= Never; 2= Rarely; 3= Occasionally; 4= Often; 5= Very Often
Figure 11: Bar chart of knowledge in program use [Hypertexts]
The vast majority of students indicated that they use
Spreadsheets, with 75.57% of them using it frequently, as
opposed to 8.91% of students who reported never using this
program or using it infrequently. The remaining 15.51% use
it occasionally.
Figure 12: Bar chart of knowledge in program use [Spreadsheets]
79.54% of the students indicated that they frequently use
multimedia programs, in contrast to 9.24% of the students
who reported using this program infrequently or not at all,
with the remaining 11.22% of students using it occasionally.
Figure 13: Bar chart of knowledge in program use [Multimedia]
5. Discussion
This study evaluated the perception of attitudes and
knowledge in digital environments among students of
different majors and semesters of the Faculty of Engineering,
Industry and Architecture of the Universidad Laica Eloy
Alfaro de Manabí, using the ACMI questionnaire of
Amorós-Poveda (2019). To address the research questions,
frequency tables and both descriptive and inferential
analyses were conducted using the SPSS software,
comparing data from the employed digital questionnaire by
contrasting the variables of the dimension of attitudes and
knowledge across different majors and semesters.
The attitudes and knowledge of the students have shown
positive results, with most of them having a good knowledge
of the use and management of technological media. Most of
the medians in the frequency tables have results close to the
upper limits (Med = 10 and 9 in attitudes; Med = 4 and 5 in
knowledge). These results coincide with the study by
Bernate et al. (2021), where they were able to show that most
of the people sampled had a good knowledge of the use and
management of technological media by obtaining an
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approximate mean of 5 on a Likert scale in a variable that
evaluated digital literacy. Despite that, there are areas for
improvement, since in their sample there were registers
closer to lower values.
Among the key findings, it was found that most students
consider computers to be accessible, especially in advanced
semesters, with Architecture and Industrial Engineering
students standing out, who perceive the computer as a
facilitator instrument.
It was observed that the use of specialized programs, such as
Excel and AutoCAD, increases in higher semesters due to
the academic and professional demands of certain majors.
Architecture and Industrial Engineering stood out in the
frequency of use of these programs, essential in their fields,
while in other careers the general use of software
predominated. However, no use or limited use of tutorials
and hypertexts was identified in all majors.
In the open-ended questions, many students noted the
importance of handling specialized software and the Office
Suite, expressing interest in receiving free, personalized
training from the university. They also shared mixed
opinions about the impact of technology in their professional
fields, reflecting expectations about future changes.
Finally, when comparing the results obtained with those of
other related studies, it can be seen that the ACMI
questionnaire has a very general evaluative approach that
does not allow it to make more specific comparisons to
studies such as those of León-Pérez et al. (2020) and
Restrepo-Palacio and Segovia (2020), where they have a
different questionnaire design and results analysis based on
the formulated variables.
6. Conclusiones
Among the results obtained in the analysis of each question,
it stands out that the vast majority of students of the Faculty
of Engineering, Industry and Architecture have positive
attitudes towards the computer environment. Students
possess knowledge and frequently use various computer
programs that allow them to function in digital
environments, such as the use of Spreadsheets, Design
programs and modeling, etc. Conversely, there is a large
percentage of students who do not know about Hypertexts
and use them infrequently or not at all.
Through the open questions applied on the survey, the
students evidenced their opinions about the digital skills that
they consider important and useful in their respective majors
and about the training they would like to receive. Students
have positive expectations of their future and are interested
in acquiring skills that are useful for them to face the trends
of the labor market.
The ACMI questionnaire has proven to be effective in
generally assessing students' digital competencies. However,
areas for improvement were identified that could optimize its
applicability. It is suggested to reduce the number of
variables to simplify its use and to adjust the identification
dimension, limiting it to the categories "Male" and "Female"
to avoid interpretation errors. In addition, it would be
pertinent to update the questionnaire by incorporating
variables related to current trends, such as artificial
intelligence and Big Data, in the dimensions of attitudes and
knowledge. This would respond to students' expectations
about the impact of these technologies on their training and
professional future, broadening the relevance and accuracy
of the assessment.
These findings prompt reflection on how activities that
previously required a face-to-face environment can now be
conducted digitally, often with more agility and efficiency.
As time progresses, conventional work methodologies will
transform, making it essential for students to navigate digital
surroundings in a forthcoming era where their digital
competencies will increasingly have more significance.
This initial research paves the path for potential subsequent
studies utilizing the acquired data, with the ongoing
objective of enhancing the digital competencies of future
professionals.
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CRediT author statement
Espinal-Lino, R.: Conceptualization, Data Curation,
Methodology, Software, Investigation, Writing - Original
Draft, Writing - Review & Editing, Visualization. Rangel-
Anchundia, L.: Conceptualization, Data Curation,
Methodology, Software, Investigation, Writing - Review &
Editing, Resources, Project administration. Cedeño-
Zambrano, L.: Conceptualization, Visualization, Writing -
Review & Editing. Peña-Miguel, N: Conceptualization,
Methodology, Supervision, Writing - Review & Editing.
Echegaray, L.: Conceptualization, Methodology,
Supervision, Writing - Review & Editing. All authors have
read and accepted the published version of the manuscript.
Conflict of interest
The authors have declared that there is no conflict of interest
in this publication.
Editor's Note
Disclaimer: The data, statements, opinions contained in the
publication are the sole responsibility of the authors and not
of FINIBUS Scientific Journal - Engineering, Industry and
Architecture. The Journal and its editors disclaim all liability
for damage to person or property resulting from the methods,
instructions, product or idea mentioned in the contents.
Copyright 2025. FINIBUS Scientific
Journal - ISSN: 2737-6451.
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