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ASSESSMENT OF GENERIC COMPETENCIES IN CIVIL
ENGINEERING STUDENTS DURING PRE-
PROFESSIONAL INTERNSHIPS
Evaluación de las competencias genéricas en estudiantes de ingeniería civil
durante las prácticas preprofesionales
Jhon Herminson Arias-Rueda
jariasr@ups.edu.ec
https://orcid.org/0000-0002-5216-3069
Universidad Politécnica Salesiana (Ecuador)
César Augusto Arias-Rueda
carias@uegonzaga.edu.ec
https://orcid.org/0000-0002-7000-9102
Unidad Educativa San Luis Gonzaga (Ecuador)
Recibido: 06/02/2025
Revisado: 07/04/2025
Evaluado: 13/05/2025
Aceptado: 13/05/2025
Abstract
Pre-professional internships are crucial in training civil engineering students, as
they foster the development of generic competencies essential for professional
success and workplace adaptability. This study assessed these competencies
using a quantitative, descriptive, and correlational approach with a cross-
sectional design through an adapted and validated Work Experience
Questionnaire (WEQ) version. Ninety-five students from the seventh and eighth
levels at Salesian Polytechnic University of Ecuador were surveyed, focusing on
Clear Goals, Workplace Support, University Support, and Generic
Competencies. The instrument's reliability was confirmed with high Cronbach's
alpha values (>0.90), and exploratory (EFA) and confirmatory factor analyses
(CFA) supported its structural validity. The findings revealed that Clear Goals (r
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= 0.867) and Workplace Support (r = 0.838) significantly influence the
development of generic competencies. In contrast, University Support (r =
0.690) showed a moderate effect. The multiple regression analysis corroborated
these results. This study highlights the need for an integrated academic and
workplace environment to maximize the impact of internships on the training of
future civil engineers. A collaborative approach between universities and
companies would not only facilitate the alignment of learning objectives with the
demands of the labour market. However, it would also allow students to receive
more structured and continuous support during their practical experience.
Resumen
Las prácticas preprofesionales son fundamentales en la formación de los
estudiantes de ingeniería civil, ya que promueven el desarrollo de
competencias genéricas esenciales para el éxito profesional y la adaptabilidad
en el entorno laboral. Este estudio evaluó dichas competencias mediante un
enfoque cuantitativo, descriptivo y correlacional con un diseño transversal,
utilizando una versión adaptada y validada del Cuestionario de Experiencia
Laboral (WEQ, por sus siglas en inglés). Se encuestaron 95 estudiantes de los
niveles séptimo y octavo de la Universidad Politécnica Salesiana de Ecuador,
enfocándose en metas claras, apoyo en el lugar de trabajo, apoyo universitario
y competencias genéricas. La confiabilidad del instrumento fue confirmada con
altos valores de alfa de Cronbach (>0.90), y los análisis factoriales exploratorio
(AFE) y confirmatorio (AFC) respaldaron su validez estructural. Los hallazgos
revelaron que las metas claras (r = 0.867) y el apoyo en el lugar de trabajo (r =
0.838) influyen significativamente en el desarrollo de competencias genéricas,
mientras que el apoyo universitario (r = 0.690) mostró un efecto moderado. El
análisis de regresión múltiple corroboró estos resultados. Este estudio destaca
la necesidad de un entorno académico y laboral integrado para maximizar el
impacto de las pasantías en la formación de los futuros ingenieros civiles. Un
enfoque colaborativo entre universidades y empresas no solo facilitaría la
alineación de los objetivos de aprendizaje con las demandas del mercado
laboral, sino que también permitiría a los estudiantes recibir un apoyo más
estructurado y continuo durante su experiencia práctica.
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Keywords: generic competencies, civil engineering, pre-professional
internships, work experience questionnaire, professional success.
Palabras Clave: competencias genéricas, ingeniería civil, prácticas
preprofesionales, cuestionario de experiencia laboral, éxito profesional.
1. Introduction
Pre-professional internships are a fundamental component of civil engineering
training, as they facilitate the integration of academic knowledge with the
practical demands of professional practice (Ferreira et al., 2024; Rajabzadeh et
al., 2022). These experiences enable students to engage in real-world projects,
enhancing technical competencies and essential skills such as problem-solving,
teamwork, and project management (Goteng et al., 2022; Narayanaswamy et
al., 2024; Nogueira et al., 2021). In today's increasingly globalized and
competitive work environment, these skills are vital for addressing the
challenges of civil engineering projects, which often require collaboration within
multidisciplinary teams and effective resource management in complex and
dynamic scenarios (Ferreira et al., 2024; Ragusa et al., 2022; Rajabzadeh et
al., 2022).
While the importance of pre-professional internships is widely recognized, their
effectiveness as a training tool depends on factors such as the clarity of
learning objectives, the support provided by workplace mentors, and academic
guidance (Bicaj et al., 2024; Nogueira et al., 2021). However, in the Latin
American context, there is a limited body of empirical studies validating specific
instruments to assess these experiences and their impact on the development
of generic competencies (Cedeño & Santos, 2017; Terán, 2019) This gap
presents a significant challenge for educational institutions in the region, as it
hinders the optimization of internship programs, and the assurance of
comprehensive training aligned with the needs of the global labour market.
The Work Experience Questionnaire (WEQ), initially developed by Elaine Martin
in 1997 (Martin, 1997), is an effective tool to assess students' perceptions of
their practical experiences. Based on the Course Experience Questionnaire
(CEQ), the WEQ incorporates dimensions that directly influence the quality of
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teaching and learning, adapting them to specific work contexts. Recent studies,
such as those of Luk and Chan (2020), have adapted this instrument (Table 1)
to evaluate engineering practices, demonstrating its validity and reliability in
contexts such as Hong Kong and Portugal (Luk & Chan, 2020a; Nogueira et al.,
2021). However, its application in Latin America, particularly civil engineering,
remains limited.
Table 1
WEQ adapted by Luk and Chan to the engineering context
Dimension
Items
Clear Goals
1. It was clear from the beginning what I expected to learn during the placement.
2. I am clear about how I would be assessed.
3. It was easy to understand the standard of work expected during the placement.
4. I generally had a clear idea of what I needed to do during the placement.
Workplace
Support
1. My workplace supervisor tried to make my experience interesting.
2. I received useful feedback on my performance.
3. My workplace supervisor motivated me to do my best.
University
Support
1. My department was beneficial in preparing me to seek a placement.
2. My academic supervisor was extremely supportive.
3. My academic supervisor helped me communicate with the company when
issues arose.
Generic
Competencies
1. The placement helped me develop my ability to plan and organize my daily
work.
2. The placement enhanced my analytical skills.
3. The placement developed my ability to solve problems.
4. The placement developed my ability to work in a team.
5. As a result of the placement, I feel confident facing unfamiliar work-related
problems.
Source: Luk and Chan (2020).
The present study aims to fill this gap in the literature, adapting and validating
the WEQ to the context of civil engineering students in Ecuador, contributing
empirical evidence to a neglected academic context. This paper provides a
detailed analysis of how pre-professional internships contribute to the
development of generic competencies, as well as the influence of the WEQ
dimensions - Clear Goals, Workplace Support, and University Support - on such
development.
The main objective of this study is to assess the generic competencies
developed by civil engineering students during their pre-professional internships
using the adapted version of the WEQ. From this analysis, empirical data are
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intended to optimize academic programs and improve their impact on
professional training.
To achieve this objective, the following research questions are posed:
1) What are the generic competencies developed by civil engineering students
during their pre-professional internships?
2) What is the relationship between the dimensions of the WEQ, Clear Goals,
Workplace Support, and University Support, and the development of
Generic Competencies?
3) How can optimising these dimensions contribute to strengthening generic
competencies in the context of pre-professional internships?
2. Methodology
This study uses a quantitative, descriptive, and correlational approach with a
cross-sectional design. Its purpose is to evaluate the generic competencies
developed by civil engineering students during their pre-professional
internships.
2.1. Population
The research involved 95 students of the Salesian Polytechnic University of
Ecuador, representing all the seventh and eighth-level students enrolled in the
Civil Engineering program who had completed their pre-professional
internships. The sample selection, in this case, the entire population, was
carried out intentionally, considering all students who met the academic and
administrative requirements established by the Pre-professional Internship
program. This approach ensures the inclusion of a complete and relevant
population for the study, eliminating selection biases and providing a solid basis
for interpreting the results.
Table 2 shows a statistical summary of the general characteristics of the
students, evidencing a sufficiently heterogeneous population in terms of age,
gender, academic level and duration of the pre-professional internship. This
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diversity strengthens the validity of the results and ensures that the conclusions
can be generalized to students with similar characteristics in civil engineering.
Table 2.
Statistical Summary of the Population
Percentage (%)
Number of Students
Gender
66.3
63
33.7
32
100.0
95
Age Distribution
50.5
48
37.9
36
9.5
9
2.1
2
100.0
95
Academic Level
67.4
64
32.6
31
100.0
95
Pre-professional Internship Duration
5.3
5
50.5
48
35.8
34
8.4
8
100.0
95
Source: Elaborated by the author.
2.2. Instrument
The instrument used was the WEQ developed by Luk and Chan (2020), with
some adjustments in civil engineering. These adaptations were made to ensure
greater precision in the technical language of the career, facilitating the
students' understanding of the items. Like its original version, the questionnaire
is composed of 15 items distributed in four main dimensions. Table 3 presents
the final version of the items used, resulting from the validation process and
adjustments made to optimize their clarity and relevance.
Table 3
WEQ Adapted to the Context of Civil Engineering
Dimension
Items
Clear Goals
Q1. From the beginning of my civil engineering internship, I was clear about the
learning objectives I needed to achieve.
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Q2. I knew exactly how I would be evaluated during my internship in civil
engineering.
Q3. It was easy to understand the quality standards and expected outcomes of the
projects I participated in.
Q4. I had a clear idea of the tasks I needed to perform at the internship site.
Workplace
Support
Q5. My supervisor at the internship site (e.g., construction supervisor or technical
lead) ensured my experience was enriching.
Q6. I received useful feedback on my performance in the assigned tasks within the
context of the construction or project.
Q7. My supervisor and colleagues at the internship site motivated me to improve
and make the most of my experience.
University
Support
Q8. The university provided tools or guidance to help me secure a suitable
professional internship in civil engineering.
Q9. My academic advisor provided continuous support during my internship.
Q10. My academic advisor guided me in case of difficulties or problems during my
internship.
Generic
Competencies
Q11. During the internship, I improved my ability to professionally plan and
organize assigned activities.
Q12. The internship helped me develop analytical skills to solve specific technical
problems in civil engineering.
Q13. I feel more prepared to solve practical problems by applying the knowledge
acquired during my studies.
Q14. I learned to work effectively in multidisciplinary civil engineering teams during
the internship.
Q15. At the end of my internship, I gained greater confidence to face professional
challenges in civil engineering projects.
Source: Elaborated by the author.
2.2.1. Validation of the instrument
As described below, a structured series of steps was carried out to validate the
instrument.
i. Pilot Test:
There was a pilot phase where the questionnaire was applied to 15 randomly
selected civil engineering students, complying with the previously established
criteria; the purpose was to evaluate the clarity and relevance of the items about
the academic and professional context of the career. Based on this, some items
were adjusted using more specific technical language related to civil
engineering, ensuring that the questions were relevant and accurate for this
particular group of students.
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There was a pilot phase in which the questionnaire was applied to 15 randomly
selected civil engineering students, complying with the previously established
criteria. The purpose was to evaluate the clarity and relevance of the items
regarding the academic and professional context of the career. Based on this,
some items were adjusted using more specific technical language related to
civil engineering, ensuring that the questions were relevant and accurate for this
particular group of students.
After making the necessary adjustments, the final questionnaire was distributed
to the 95 participants using the Microsoft Forms platform, part of the Office 365
package with an institutional license provided by the university. This medium
was selected for its ease of use, accessibility for students, and ability to collect
data in an organized and efficient manner. Before participation, the anonymity
of responses was guaranteed, and informed consent was requested, ensuring
that students understood the purpose of the study and voluntarily agreed to be
part of the research. This approach allowed for agile data collection, complying
with the ethical and methodological principles established for the study.
ii. Validation
Once the questionnaire was applied to the entire population, Cronbach's Alpha
was calculated to determine the internal consistency of the questionnaire
adapted to the technical language of civil engineering. The calculation was
performed independently for each of the dimensions. In addition, an Exploratory
Factor Analysis (EFA) and a Confirmatory Factor Analysis (CFA) were
performed to validate that the questionnaire items were grouped into the
proposed theoretical dimensions. The EFA allowed us to identify the underlying
structure of the questionnaire, while the CFA assessed the goodness of fit of
the theoretical model with the observed data. This analysis was essential to
check the instrument's structure and confirm that it consistently measures the
characteristics for which it was designed after adjustment.
2.3. Data Analysis
The data were processed using the statistical software R-Studio V. 4.3.2. First,
the instrument's reliability was evaluated by calculating Cronbach's alpha,
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followed by an exploratory factor analysis (EFA) and then a confirmatory factor
analysis (CFA) to validate its theoretical structure. Subsequently, descriptive
statistics were performed to characterize the responses to each item and to
dimension the general behaviour of the questionnaire. Next, an inferential
analysis was performed by finding the Pearson correlation matrix to identify
significant relationships (strength and direction) between the dimensions.
Finally, a multiple linear regression model was used to determine which
dimensions significantly impact the development of generic competencies.
2.3.1. Reliability of the instrument
Cronbach's alpha: Cronbach's alpha was calculated individually for each
dimension of the questionnaire to evaluate the instrument's internal
consistency. This statistical index measures the average correlation between
items within the same dimension, reflecting how these items consistently
assess the same theoretical construct. According to reliability criteria proposed
in the literature (Cortina, 1993; Roco-Videla et al., 2023), a Cronbach's Alpha
value greater than 0.70 is considered acceptable, ensuring that the items are
adequately related to one another. Furthermore, values above 0.80 indicate
high reliability (Cortina, 1993; Roco-Videla et al., 2023), desirable in instruments
designed to assess competencies and multidimensional factors, as in this study.
This analysis was essential to identify potentially problematic items that could
affect the homogeneity of the dimensions and to ensure that the questionnaire
accurately measured the characteristics proposed in its design.
Exploratory and Confirmatory Factor Analysis: To complement the internal
consistency results obtained by calculating Cronbach's Alpha, additional
statistical analyses were conducted to ensure coherence among the items
assigned to each dimension. First, an Exploratory Factor Analysis (EFA) was
performed to identify the underlying structure of the instrument's theoretical
dimensions. Subsequently, the proposed structure was validated using a
Confirmatory Factor Analysis (CFA), assessing the model's fit to the observed
data. This sequential approach ensured the questionnaire was reliable and valid
for measuring the proposed dimensions.
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Exploratory Factor Analysis (EFA) was employed to assess the validity of the
questionnaire and explore the underlying structure of the theoretical
dimensions: Clear Goals, Workplace Support, University Support, and Generic
Competencies. The adequacy of the data for factor analysis was evaluated
using the Kaiser-Meyer-Olkin (KMO) index, which measures the sufficiency of
correlations among items; values above 0.60 are considered acceptable, while
values greater than 0.80 are deemed excellent (Bentler & Bonett, 1980; Lloret-
Segura et al., 2014). Additionally, Bartlett's test of sphericity was conducted,
expecting a significant result (p < 0.05) to confirm the presence of sufficient
correlations for factor analysis (Hu & Bentler, 1999).
The principal components method was used for factor extraction, applying the
eigenvalues > 1 criterion to determine the number of factors. Furthermore, the
scree plot was used to identify the optimal number of factors through the
"elbow" of the curve. To facilitate the interpretation of the extracted factors, an
oblique rotation (Promax) was applied, as recommended in the literature for
cases where the dimensions are not entirely independent, (Bentler & Bonett,
1980; Hu & Bentler, 1999).
Subsequently, a Confirmatory Factor Analysis (CFA) was conducted to validate
the factor structure proposed in the Exploratory Factor Analysis (EFA). This
analysis employed the Maximum Likelihood (ML) estimation method, evaluating
various global fit indices to determine the adequacy of the theoretical model
with the observed data. Among the considered indices, the Comparative Fit
Index (CFI) is one of the most relevant, where values above 0.90 indicate a
good fit and values greater than 0.95 reflect an excellent fit (Bentler & Bonett,
1980; Hu & Bentler, 1999). Another index used was the Tucker-Lewis Index
(TLI), deemed acceptable above 0.90 and excellent when exceeding 0.95
according to the same authors.
Additionally, the Root Mean Square Error of Approximation (RMSEA), which
measures the discrepancy per degree of freedom, was interpreted as adequate
when below 0.08 and excellent when below 0.05 (Bentler & Bonett, 1980; Hu &
Bentler, 1999). Lastly, the Standardized Root Mean Square Residual (SRMR),
which evaluates the standardized discrepancies between observed and
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predicted correlations, was considered indicative of a good fit when values were
below 0.08 (Bentler & Bonett, 1980; Hu & Bentler, 1999).
In addition to the global fit indices, standardized factor loadings were analyzed,
considered acceptable when exceeding 0.50 and excellent when surpassing
0.70 (Bentler & Bonett, 1980; Hu & Bentler, 1999). These values indicate that
the items adequately represent their corresponding dimensions. Finally, the
correlations between factors were evaluated; high correlations (>0.50) support
the theoretical coherence among dimensions, providing additional evidence of
validity for the proposed model (Bentler & Bonett, 1980; Lloret-Segura et al.,
2014).
This combined approach of EFA and CFA ensures that the questionnaire is
valid and reliable for measuring the generic competencies developed during the
pre-professional internship.
2.3.2. Descriptive Statistics
A descriptive analysis was conducted to characterize students' perceptions of
the evaluated dimensions using the responses obtained through the adapted
questionnaire. For each item, metrics such as the mean ( ), standard deviation
(SD), mode (Mo), kurtosis, and coefficient of variation (CV) were calculated,
allowing the identification of central tendencies, variability, and the
concentration of responses. This preliminary analysis provided an overview of
students' levels of agreement with the dimensions and an assessment of the
consistency in their responses. The descriptive results represent a crucial step
in understanding the data structure and ensuring the instrument's quality before
advancing to more sophisticated statistical analyses, such as correlations and
multiple linear regression (Cortina, 1993). This approach contextualizes
students' perceptions within a robust quantitative framework, contributing to the
validity and reliability of the study's findings.
2.3.3. Correlations
To complement the descriptive analysis, a Pearson correlation analysis was
conducted to examine the relationship between the first three dimensions of the
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WEQ (Clear Goals, Workplace Support, and University Support) and the fourth
dimension (Generic Competencies). This analysis allowed for the evaluation of
the strength and direction of associations between the variables using the
correlation coefficient r, which ranges from -1 to 1. A value close to 1 indicates a
strong positive correlation, a value close to -1 reflects a strong negative
correlation, while values near 0 indicate no significant relationship between the
variables (Fiallos, 2021).
2.3.4. Multiple Linear Regression
Finally, a multiple linear regression analysis was conducted to identify which
WEQ dimensions significantly impact the development of generic competencies
in Civil Engineering students. This approach quantified the relationship between
the independent dimensions Clear Goals, Workplace Support, and University
Support and the dependent dimension Generic Competencies while controlling
for the effects of other variables (Izquierdo Bautista, 2020). Additionally, this
analysis supported the findings derived from the correlation analyses, providing
a comprehensive perspective on the relationships between the studied
dimensions.
3. Results
The results obtained using the statistical software R-Studio V.4.3.2 are
presented below.
3.1. Validity of the WEQ Adapted to Civil Engineering
For each of the dimensions of the adapted questionnaire, the Cronbach's Alpha
values were calculated, reflecting the instrument's internal consistency. The
results obtained, presented in Table 3, show values above 0.90 across all
dimensions, demonstrating adequate internal consistency in line with the
standards established in the literature (Cortina, 1993).
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Table 4
Cronbach’s Alpha Results by Dimension of the WEQ Adapted to Civil Engineering
Dimension
Cronbach’s Alpha
Clear Goals
0.95
Workplace Support
0.97
University Support
0.93
Generic Competencies
0.97
Source: Elaborated by the author.
The Exploratory Factor Analysis (EFA) confirmed the proposed theoretical
structure of the questionnaire, organized into four main dimensions: Clear
Goals, Workplace Support, University Support, and Generic Competencies.
The adequacy of the data was evaluated using the Kaiser-Meyer-Olkin (KMO)
index, which reached an excellent value of 0.92, demonstrating adequate
correlation among the items. Additionally, Bartlett's test of sphericity yielded a
chi-square value of 2100.96, reflecting a high level of association among the
instrument's variables. The corresponding p-value, equal to 0 and below the
standard significance level of 0.05, confirmed the presence of significant
correlations among the items, validating the suitability of the factor analysis.
Furthermore, 105 degrees of freedom (df = 105) were identified, corresponding
to the number of evaluated items, which supported the statistical robustness of
the analysis and ensured the feasibility of the results to validate the theoretical
structure of the questionnaire (Bentler & Bonett, 1980; Lloret-Segura et al.,
2014).
The principal components method was used for factor extraction, applying the
eigenvalues > 1 criterion, which identified four main factors explaining a total of
92.30% of the cumulative variance. Factor 1, corresponding to the Clear Goals
dimension, explained the most significant portion of individual variance, with
84.27%, while Factor 2 (Workplace Support), Factor 3 (University Support), and
Factor 4 (Generic Competencies) added 8.03%, 4.58%, and 3.13%,
respectively. The scree plot validated this number of factors by showing an
inflexion point in the curve, indicating the importance of retaining these four
factors. An oblique rotation (Promax) was applied to facilitate interpretation,
which revealed that the factor loadings of the items exceeded the
recommended threshold of 0.70 in most cases, reinforcing the clarity and
cohesion of the proposed dimensions. These results support the questionnaire's
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internal validity and its robust capacity to measure the proposed theoretical
dimensions, providing reliable empirical evidence for its use in educational and
professional evaluation contexts. Table 5 summarizes the values found.
Table 5
Summary of Numerical Results from the Exploratory Factor Analysis (EFA)
Evaluated Aspect
Value
Interpretation
Kaiser-Meyer-Olkin (KMO)
Index
0.92
Excellent data adequacy
Chi-square (Bartlett)
2100.96
High level of association among
items
p-value (Bartlett)
0.000
Significant correlations
Degrees of Freedom (df)
105
Consistent with the evaluated items
Eigenvalue Factor 1
11.03
Explains 84.27% of the variance
Eigenvalue Factor 2
1.05
Explains 8.03% of the variance
Eigenvalue Factor 3
0.60
Explains 4.58% of the variance
Eigenvalue Factor 4
0.41
Explains 3.13% of the variance
Cumulative Variance (4
factors)
100%
Total variance explanation
Source: Elaborated by the author.
The Confirmatory Factor Analysis (CFA) validated the proposed factor structure
by assessing the consistency between the observed data and the theoretical
model. The global fit indices provided evidence of correspondence between the
model and the evaluated dimensions.
The Comparative Fit Index (CFI) yielded a value of 0.935, indicating a good
model fit. Similarly, the Tucker-Lewis Index (TLI) achieved a value of 0.919,
reflecting an acceptable fit. In contrast, the Root Mean Square Error of
Approximation (RMSEA) presented a value of 0.132, suggesting room for
improvement in model fit. However, the Standardized Root Mean Square
Residual (SRMR), with a value of 0.038, indicated excellent alignment between
the observed and predicted correlations. The standardized factor loadings
ranged from 0.756 to 0.987, supporting that the items adequately represent
their respective theoretical dimensions. Furthermore, the significant factor
correlations, with values ranging from 0.649 to 0.898, validated the theoretical
relationships among the evaluated dimensions. These results collectively
support the questionnaire's internal validity. Table 6 provides a summary of the
obtained values.
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Table 6
Summary of Confirmatory Factor Analysis (CFA) Results
Evaluated Aspect
Value
Interpretation
Comparative Fit Index (CFI)
0.935
Good fit
Tucker-Lewis Index (TLI)
0.919
Acceptable fit
Root Mean Square Error of
Approximation (RMSEA)
0.132
Room for improvement
Standardized Root Mean
Square Residual (SRMR)
0.038
Excellent fit
Factor loadings range
0.756 - 0.987
Adequate item representation
Factor correlations
0.649 - 0.898
Significant relationships
between dimensions
Source: Elaborated by the author.
The Confirmatory Factor Analysis validated the proposed theoretical structure of
the model, showing predominantly adequate global fit indicators with high factor
loadings that support the relationship between the items and their dimensions.
Although the RMSEA indicated room for improvement in the fit, the SRMR and
significant correlations among dimensions confirmed the model's validity. These
results reinforce the robustness of the WEQ adapted to the civil engineering
context, demonstrating that the instrument is reliable and appropriately
measures the proposed constructs, providing a solid foundation for evaluating
pre-professional internships.
3.2. Descriptives
The following are the descriptive statistics for the 15 items comprising the four
WEQ dimensions adapted to the civil engineering context. This analysis
provides an overview of the central characteristics of the responses obtained,
including the mean, standard deviation, mode, kurtosis, and coefficient of
variation for each dimension and its respective items.
Overall, the dimensions Workplace Support and Generic Competencies show
the highest means (3.87 and 3.80, respectively), suggesting a positive
perception among students regarding these aspects. In contrast, the University
Support dimension has a lower mean (3.41), which could indicate an area for
improvement in the support provided by the institution. The kurtosis coefficients
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of the items in the adapted WEQ are consistently high, indicating a significant
concentration of responses around specific values on the Likert scale (primarily
4 and 5). In the Generic Competencies dimension, the items exhibit
exceptionally high kurtosis (e.g., Q12: 7.10, Q13: 7.08, and Q15: 7.72),
reinforcing the perception of a strong presence of generic skills, with a marked
tendency toward the upper values of the scale. Similarly, in Workplace Support,
the items reflect high kurtosis (e.g., Q5: 6.83, Q6: 6.79, and Q7: 6.72),
indicating positive perceptions regarding the support received and a
concentration of responses at higher values.
In contrast, in the University Support dimension, although kurtosis is lower (e.g.,
Q8: 4.87), responses still tend toward the modal value of 4, suggesting a more
moderate perception. These patterns are also reflected in the Coefficient of
Variation (CV), which is lower in dimensions with high kurtosis, such as
Workplace Support (32.85%) and Generic Competencies (32.93%), indicating
less dispersion in responses. Conversely, \textit{University Support} shows a
higher CV (39.70%), reflecting more significant variability in student
perceptions.
In particular, these results address the first research question, as it becomes
evident that during pre-professional internships, civil engineering students
developed various essential generic competencies for their professional
training. Notably, the confidence to face professional challenges (Q15) stood
out, achieving the highest mean (3.95) and the lowest dispersion (SD = 1.16),
reflecting the positive impact of internships in strengthening their professional
confidence.
Similarly, significant progress was observed in the ability to work effectively in
multidisciplinary teams (Q14), with a mean of 3.82, and in problem-solving skills
in practical scenarios (Q13), with a mean of 3.83 and low variability (SD = 1.19),
highlighting the applicability of acquired knowledge in real-world contexts. On
the other hand, the development of analytical skills to solve technical problems
(Q11) was one of the best-rated competencies, with a mean of 3.87 and
moderate dispersion (SD = 1.20).
Finally, the ability to plan and organize activities (Q11) showed a mean of 3.51
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but with more significant variability (SD = 1.78), suggesting individual
differences in students' experiences. These results indicate that pre-
professional internships foster critical skills for professional performance in civil
engineering, consolidating technical and interpersonal competencies.
3.3. Correlations
In response to the second research question, related to the relationship
between the WEQ dimensions —Clear Goals, Workplace Support, and
University Support — and the development of Generic Competencies, the
results presented in Table 7 show positive and significant associations. The
Clear Goals dimension exhibits the highest correlation (r = 0.867), indicating
that a clear understanding of learning objectives during pre-professional
internships is strongly linked to the development of generic competencies.
Similarly, Workplace Support shows a robust correlation (r = 0.838), highlighting
the importance of a favourable work environment and effective feedback.
Finally, although University Support demonstrates a moderate correlation (r =
0.690), it remains significant, suggesting that academic support contributes to
the development of these competencies, albeit to a lesser extent. These
findings emphasize that all WEQ dimensions positively influence generic
competencies, with Clear Goals and Workplace Support being the most
determining factors in this context.
Table 7
Correlation of independent dimensions with Generic Competencies using Pearson’s correlation
coefficient.
Independent Dimension
Correlation with Generic Competencies (r)
Clear Goals
0.867
Workplace Support
0.838
University Support
0.690
Note: All correlations are positive, indicating that more significant development in independent
dimensions is associated with higher generic competencies.
3.4. Multiple regression equation
Multiple linear regression was used to evaluate how the WEQ dimensions —
Clear Goals, Workplace Support, and University Support — predict the
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development of generic competencies in civil engineering students. This
statistical technique quantifies the individual effect of each dimension, controlled
by the others, to identify its specific contribution to the overall model. The
multiple regression equation (1) and the coefficients summarized in Table 8
reflect the relative importance of each dimension.
(1)
Where:
GC: Generic Competencies
CG: Clear Goals
WS: Workplace Support
US: University Support
β0: Intercept of the model.
β1, β2, β3: Regression coefficients for each dimension.
: Error term.
Table 8
Results of the multiple linear regression analysis
Predictor Variable
Coefficient (β)
Standard Error
p-
value
Intercept
0.68
0.23
0.004
Clear Goals
0.44
0.07
<0.001
Workplace Support
0.31
0.07
<0.001
University Support
0.11
0.05
0.049
Source: Elaborated by the author.
The Clear Goals dimension (β₁ = 0.44, p < 0.001) emerges as the most
influential factor, indicating that a one-unit increase in the perception of clear
objectives is associated with an average increase of 0.44 units in generic
competencies, controlling for the other dimensions. Similarly, Workplace
Support (β₂ = 0.31, p < 0.001) also contributes significantly, highlighting the
crucial role of the work environment in professional training. Finally, although
University Support (β₃ = 0.11, p = 0.049) has a smaller coefficient, its
contribution remains statistically significant, suggesting that adequate academic
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support complements the impact of workplace practices.
These findings reaffirm that Clear Goals and Workplace Support are
determinants in developing generic competencies, while University Support
presents opportunities to optimize its impact. Combining these results with the
previous correlations strengthens the empirical evidence of the importance of a
structured and well-supported internship environment.
4. Discussion and conclusions
The main objective of this study was to evaluate the generic competencies
developed by civil engineering students during their pre-professional internships
using an adapted version of the Work Experience Questionnaire (WEQ). This
analysis generated empirical data to optimise academic programs and enhance
their impact on professional training.
The adapted Work Experience Questionnaire (WEQ) validation confirms its
robustness as an instrument for evaluating competencies in this context.
Despite its limitations, such as the localized sample and the cross-sectional
focus, this study provides a solid foundation for optimizing pre-professional
internship programs by integrating collaborative strategies between academia
and the labour sector to better prepare future civil engineers for the challenges
of the workplace environment.
The results revealed a complex interrelationship between the evaluated
dimensions and the development of generic competencies during pre-
professional internships. Factors such as clarity in objectives and support in the
workplace were confirmed as necessary, while academic support showed a less
prominent influence. These results align with recent studies (Nogueira et al.,
2021), which reported similar findings on university support in developing pre-
professional internships in Portugal. By integrating this reference, an empirical
foundation reinforces the results obtained in the present study, highlighting a
consistent trend across different educational settings. These findings raise
questions about the role of academic institutions in this context and underscore
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the need to rethink collaborative strategies between academia and the labour
sector to maximize the formative impact.
Based on the four dimensions of the Work Experience Questionnaire (WEQ),
the statistically validated factorial model represents a significant theoretical
contribution to the field (Luk and Chan 2020). However, the exclusive reliance
on quantitative tools may limit a deeper understanding of students' individual
experiences. This study highlights the importance of integrating qualitative
methods, as conducted in the United States (McAlexander et al., 2024), to
capture the nuances and specific contexts of these experiences. This approach
opens new opportunities for future research that is more comprehensive and
holistic.
Within the realm of generic competencies, the results highlight the potential of
pre-professional internships to develop skills such as adaptability, problem-
solving in real-world settings, and the integration of interdisciplinary knowledge
(Belchior-Rocha et al., 2022; Rajabzadeh et al., 2022). Although less tangible,
these capabilities are essential for future civil engineers to address their
profession's complex and multifaceted challenges. In this regard, expanding
existing assessment instruments to include these more subjective dimensions is
proposed, aiming to enrich the analysis of formative experiences.
From a practical perspective, the results suggest that internship programs
should reconsider their traditional approach toward one that places the
student's experience at the centre as a necessary driving force (Cedeño and
Santos 2017; Terán 2019). Identifying clarity in objectives and workplace
support as critical factors underscores the importance of establishing
measurable success indicators in these areas. Furthermore, the moderate
influence of university support raises a structural dilemma: how can academia
transcend its role as a facilitator to become an active agent driving significant
improvements in the quality of pre-professional experiences?
4.1. Limitations of the study
The results obtained reveal certain limitations that could be addressed in future
research. First, the sample consisted of 95 students belonging to the seventh
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and eighth levels of the civil engineering program at the Salesian Polytechnic
University. Although this sample covered the entire population of these levels
and was adequate for the statistical analyses performed, its small size could
restrict the possibility of generalizing the results to larger populations or other
academic contexts. Second, the study was conducted in a single educational
institution in Ecuador, limiting the findings' external validity. This university's
socioeconomic, cultural and curricular particularities may not reflect the
conditions present in other institutions, both nationally and internationally.
Additionally, the study's cross-sectional nature prevents the evaluation of the
development of generic competencies over time. A longitudinal approach could
provide a more comprehensive understanding of how these competencies
evolve during and after pre-professional internships.
Finally, the instrument used (adapted Work Experience Questionnaire) was
validated in this specific context. Although the results demonstrate high
reliability and statistical validity, their application in other academic and
professional settings may require additional adaptations to ensure their
relevance.
Future research could address these limitations by:
• Including larger and more diverse samples of different universities and
regions.
• Employing longitudinal designs to evaluate the evolution of competencies
over time.
• Comparing academic programs and engineering fields to identify
contextual similarities and differences.
These improvements would not only strengthen the generalization of the results
but also contribute to a broader understanding of the impact of pre-professional
internships on developing generic competencies in future engineers.
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5. Recommendations
The findings of this study align with the recommendations of various authors
(Cedeño & Santos, 2017; Guarnizo Crespo, 2018; Rajabzadeh et al., 2022;
Terán, 2019) who emphasize the importance of clarity in objectives and support
in the workplace for the development of generic competencies. These factors
are essential for enhancing students' ability to face professional challenges with
greater confidence and effectiveness, particularly in contexts requiring
teamwork, maximizing the impact of pre-professional internships on their
comprehensive training.
In response to the third research question, a structured system that establishes
clear objectives at the beginning of each internship period, accompanied by
detailed evaluation rubrics and specific deliverables, is proposed. This approach
will enable students to precisely understand the process's expectations and
continuously assess their progress.
Additionally, it is essential to strengthen workplace support by implementing
structured mentorship programs. These programs will facilitate students'
integration into the professional environment by providing continuous feedback
and creating dedicated spaces for developing technical and interpersonal
competencies. In this way, a comprehensive training approach will be
promoted, addressing the demands of the current labour market.
Strengthening pre-internship preparation by providing specific workshops that
guide students in developing competencies and creating evidence portfolios to
document their progress is also necessary. Diversifying projects and expanding
the network of collaborating companies will significantly enhance the formative
experience, allowing students to face a variety of professional contexts.
Finally, educational institutions must establish clear policies that support the
development of pre-professional internships, allocating adequate resources and
providing continuous training to tutors and supervisors. Additionally, a quality
management system should be implemented to ensure the constant
improvement of internship programs. To achieve this improvement, continuous
monitoring systems that evaluate the effectiveness of internships are
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recommended. These systems should use specific metrics to measure the
development of both technical and generic competencies and ensure
appropriate mechanisms to collect feedback from students, supervisors, and
academic tutors.
This comprehensive approach will strengthen the connection between
academic training and professional experience, optimizing the impact of pre-
professional internships on future engineers' training.
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