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ISSN: 2992-7161  
COMPETENCY ASSESSMENT OF HARD SKILLS AMONG  
COLLEGE STUDENTS: STRUCTURAL TECHNICAL GAPS AND  
IMPLICATIONS FOR RETENTION  
DIAGNÓSTICO COMPETENCIAL DE HABILIDADES DURAS EN  
UNIVERSITARIOS: BRECHAS TÉCNICAS ESTRUCTURALES E  
IMPLICACIONES EN RETENCIÓN ESCOLAR  
Jiménez Antúnez Anna Merary  
Tecnológico Nacional de México/I. T. de Chihuahua II  
https://orcid.org/ 0009-0008-8657-8124  
anna.ja@chihuahua2.tecnm.mx  
Sostres Flores Juan Pablo  
Tecnológico Nacional de México/I. T. de Chihuahua II  
https://orcid.org/ 0009-0006-1227-8941  
juan.sf@chihuahua2.tecnm.mx  
Aranda Gómez Cynthia Paola  
Tecnológico Nacional de México/I. T. de Chihuahua II  
https://orcid.org/0000-0001-6132-0073  
cynthia.ag@chihuahua2.tecnm.mx  
López Tarango Daniel Axel  
Tecnológico Nacional de México/I. T. de Chihuahua II  
https://orcid.org/0009 0005 7829 7275  
daniel.lt@chihuahua2.tecnm.mx  
del Socorro Corral María  
Tecnológico Nacional de México/I. T. de Chihuahua II  
https://orcid.org/0009-0008-1754-0480  
maria.c@chihuahua2.tecnm.mx  
DOI: https://doi.org/10.61273/neyart.v4i4.213  
| Received: 23/03/2026  
| Accepted: 31/05/2026  
| Published: 07/07/2026  
This work is licensed under  
an international  
Creative Commons Attribution 4.0 license.  
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Abstract-- This study assessed the level of mastery of hard skills among forty-eight first-semester college  
students during the period from August to December 2025. Using a quantitative approach and a cross-  
sectional diagnostic-descriptive design, the sections of the Transversal Competencies Questionnaire were  
adapted using Behavior Observation Scales. The statistical outcomes indicated a global low to average  
performance expressed by an overall weighted mean of 2.18. Assessment of technical problem solving  
revealed the most severe deficiency, with a mean of 1.85, indicating marked difficulty identifying and  
sequencing logical responses to errors. However, the lack of appropriate basic computer skills only  
reaches 2.41 point score (maximum: 5 points) was also regarded the highlight rated competent level  
among deficient aspects and this only to changes as far as basic operation. Furthermore, the analysis by  
gender showed no significant gaps in instrumental performance. It is concluded that incoming students  
lack the minimum technical skills required for higher education, making it essential to urgently implement  
mandatory remedial programs aimed at mitigating these structural cognitive gaps and ensuring early  
retention in school.  
Keywords-- Academic Lag, Educational Assessment, Higher Education, Hard Skills, Technical Skills.  
Resumen--Esta investigación evaluó el grado de dominio de las habilidades técnicas en estudiantes  
universitarios del primer semestre (N=48) durante el mismo periodo de agosto a diciembre de 2025. La  
adaptación de los bloques instrumentales a las Escalas de Observación Conductual fue un enfoque  
cuantitativo y una base trasplantada para un diseño diagnóstico-descriptivo transversal de origen. Los  
resultados estadísticos indicaron un desempeño global predominantemente intermedio-bajo, como lo  
señala una media global ponderada de 2.18. La dimensión de resolución de problemas técnicos fue la que  
pareció mostrar las mayores deficiencias en el diagnóstico (promedio 1.85), ya que esto demuestra  
claramente una incapacidad para estructurar soluciones paso a paso en casos de fallas lógicas. Mientras  
tanto, las habilidades básicas de computación, que era una de las áreas rezagadas, han recibido la peor  
calificación con un 2.41, lo que significa que todavía está muy vigente para tareas simples dentro de este  
sistema. Del mismo modo, no encontramos diferencias significativas por sexo en el desempeño  
instrumental. Finalmente, los estudiantes matriculados recientemente carecen de las habilidades básicas  
requeridas para la educación superior y es necesario establecer con urgencia programas de refuerzo  
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obligatorios para reducir esas brechas cognitivas estructurales y retener a los estudiantes desde el  
principio.  
Keywords— Competencias técnicas, Diagnóstico educativo, Educación superior, Habilidades duras,  
Rezago escolar.  
INTRODUCTION  
In the contemporary socioeconomic context, characterized by accelerated automation and the digital  
transformation of industries, higher education faces the critical challenge of ensuring the employability  
and professional relevance of its graduates. Although many of the recent trends on education have argued  
for cross-disciplinary or soft skills, a solid base of hard skills, that is the specific technical knowledge,  
operational capabilities and analytical skillset associated with a discipline; well it continues to form upon  
which entry into the technical and scientific workforce is built.  
Assessing these competencies as early in education (ideally, very early) will assist with considering the  
appropriateness of curricular structures, and with modeling candidates’ future performance as  
professionals.  
International Background  
Globally, the gap between the technical competencies demanded by the productive sector and  
has been the subject of rigorous analysis. In Europe, researchers have examined how digitalization  
requires a redefinition of technical competencies in engineering and management (Asonitou, 2022). In  
Asia, various studies show that, while soft skills facilitate cultural adaptation, hard skills determine initial  
productivity and the ability to solve complex problems in technological environments (He et al., 2021).  
Likewise, the OECD has emphasized the need for standardized metrics to measure disciplinary  
knowledge, given that technical disparities limit international labor mobility (Jones & Broadbent, 2020).  
Studies in Anglo-Saxon contexts confirm that students in their first semesters often  
enter with substantial deficiencies in basic quantitative and technological competencies, which impacts  
directly on retention rates (Smith & Martinez, 2023). The transition to Industry 4.0 requires universities  
not only to teach theoretical concepts but also to ensure proficiency in specialized software tools, data  
analytics, and technical languages from the very beginning of the academic journey (Tymon, 2021).  
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Various international authors agree that students’ self-perception of their instrumental skills is often  
overestimated compared to objective performance assessments, which justifies the implementation of  
early diagnostic assessment methodologies (Wilson & Taylor, 2022; Zhao & Wang, 2024).  
Latin American Context  
In Latin America, the issue takes on structural nuances due to inequality in upper secondary education,  
which leads to a high degree of heterogeneity in the technical competencies of incoming college students.  
Research in the region shows that deficits in basic sciences and technical understanding  
is one of the main factors contributing to dropout rates in the early semesters (García-Vargas et al., 2022).  
In countries such as Mexico, Colombia, and Chile, educational agendas have prioritized aligning curricula  
with technical-professional qualification frameworks, seeking to mitigate youth unemployment and  
underemployment (Martínez-Sánchez & Rodríguez, 2023; Pérez-Franco, 2021).  
Regional literature highlights the important role of diagnosting quantitative skills and digital literacy for  
shaping effective remediation programs (Ramírez & González, 2023). In addition, gender analyses of  
STEM and social sciences show sharp differences in confidence and familiarity with key information  
technologies stem from past educational biases (Silva & Santos, 2022).  
This mismatch of skills tested at school and needed by small and medium enterprise (SMEs) cries out in  
the Latin American context for a timely assessment of students´ technical skills at their entrance into  
university life (Torres-Caceres, 2024; Valencia-Maldonado, 2023).  
Study Objective  
To evaluate the level of hard skills mastery in first-year college students through a standardized  
competency-based diagnosis, describing their initial technical and cognitive shortcomings in order to  
orient skill-building practices and curriculum re-design.  
Rationale for the Study  
This research is theoretically and practically justified in establishing an empirical assessment of the real  
status of technical competencies at the time of admission to university. The data you collect on hard skills  
in the first semester allows academic coordinators and curriculum designers to better structure  
supplemental tutoring that prevents academic lag or disrupts a sequence of instruction. The definition of  
a strategy for optimizing the development of hard skills, from both social and economic perspectives,  
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directly contributes to increasing the competitiveness of future professionals; in turn, ensuring that human  
capital is being trained according to the real needs of local and international productive sectors.  
Study Limitations  
The methodological scope of this study presents specific limitations that must be considered when  
interpreting the findings:  
• Population and Sample: The analysis is restricted exclusively to a sample group of 48 first-  
semester students, which limits the generalizability of the results to the entire institution or to  
more advanced cohorts.  
• Gender Composition: The sample has an asymmetrical distribution consisting of 31 men (64.6%)  
and 17 women (35.4%), a factor that could skew sector-specific results if analyzed from a gender  
equity perspective.  
• Time Frame: Data collection and follow-up are limited to the period between August and  
December 2025, providing a cross-sectional snapshot of competency levels during that academic  
year.  
DEVELOPMENT  
Research Approach and Type  
This research is grounded in a quantitative approach and adopts an empirical-analytical paradigm. The  
methodological design is non-experimental, cross-sectional, and has a diagnostic-descriptive scope. It is  
classified as cross-sectional because the data collection process and the measurement of variables are  
limited to a single time frame during the academic period from August to December 2025. The scope is  
strictly diagnostic, as its purpose is to empirically identify the current state of students’ competencies and  
to precisely determine their technical and cognitive gaps at the time of their entry into higher education.  
Population and Sample  
The target population of this study consists of first-year college students.  
To conduct this analysis, a non-probabilistic convenience sample was selected, consisting of 48 students  
formally enrolled in the first semester of their academic program. The sociodemographic and institutional  
characteristics of the sample are as follows:  
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• Gender distribution: The sample composition shows an asymmetrical distribution, consisting of  
31 men (64.6%) and 17 women (35.4%).  
• Time frame and academic status: Students enrolled in the initial academic term of the institutional  
cohort corresponding to the August–December 2025 period.  
Operationalization of Technical Variables  
In order to evaluate competencies with the appropriate metric rigor, an independent demographic variable  
and a structured set of dependent variables representing the students’ instrumental skills were identified.  
The independent variable is Gender (classified nominally as Male or Female). The dependent variable is  
defined as the Level of mastery of hard skills, operationally understood as the technical knowledge,  
operational capabilities, and analytical skills specific to an academic discipline. The operationalization  
matrix used is detailed below:  
Table 1. Operationalization of Technical Variables.  
Technical Dimension  
Analytical Ability  
and Synthesis  
Operational Definition  
Breaking down a complex situation, fact,  
or problem into its various constituent  
parts and extracting the essential  
elements.  
• Identifies the fundamental  
components of a problem.  
• Distinguishes  
between  
main and secondary ideas in  
a technical text.  
Information Search and  
Information  
Actively search for relevant information  
and expand one’s own knowledge using  
reliable sources.  
• Conduct  
searches  
information  
in  
multiple  
digital  
specialized  
repositories.  
• Evaluate the quality,  
accuracy, and validity of  
the  
information  
obtained.  
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Skills  
Use software essential to the academic  
and scientific sector with skill and  
precision  
• Performs basic operating  
Basic Computer Skills  
system operations.  
• Perform  
structured  
academic  
tasks  
and  
using  
word  
spreadsheets  
processors.  
Problem-Solving  
Problems  
Systematically analyze a problem by  
identifying its causes and consequences  
in order to propose and implement  
effective solutions.  
• Identifies  
all  
viable  
variables involved in a  
technical issue.  
• Clearly defines a logical  
and operational plan for a  
solution.  
Source. Compiled by the autor.  
Data Collection Techniques and Tools  
The survey method is used to collect empirical data. The structured measurement instrument is  
methodologically and formally based on the Transversal Competencies Questionnaire (CCT) originally  
designed by Aguado, González, Antúnez, and de Dios (2017), exclusively adapting and abstracting the  
instrumental blocks of the matrix test architecture to align them with the assessment of hard technical  
skills. The instrument is based on Behavioral Observation Scales (BOS), which measure competencies  
through explicit behavioral evidence rather than abstract, subjective traits. The questionnaire uses a  
Likert-type response scale with a frequency format structured around four anchor points: 1 (Never or  
Almost Never), 2 (Rarely), 3 (Often), and 4 (Always or Almost Always). By intentionally omitting a  
neutral middle category, central tendency bias is prevented, forcing students to engage in reflective self-  
assessment of their actual performance. According to evidence on the psychometric properties of the CCT  
documented in university samples, the instrument possesses high content validity, reporting averages on  
the Rovinelli and Hambleton Congruence Indices that are markedly higher than the acceptable standard  
of 0.50 and exceeding 0.80 on most of its scales. In terms of internal consistency, the selected dimensions  
exhibit optimal metric robustness, reflected in fully satisfactory Cronbach’s alpha coefficients: Analytical  
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and Synthesis Skills (α = 0.74), Information Search and Management Skills (α = 0.79), and Basic  
Computer Skills (α = 0.70). These indicators ensure an accurate assessment free from systematic error.  
Procedure and Statistical Data Analysis  
The adapted questionnaire is taken on a computerized format via an internet-based platform in regulated  
and supervised academic conditions. The digital informed consent form is presented to participants before  
beginning the assessment experience, and provides reassurance that responses will remain private,  
anonymous, and only used for scientific purposes. The scores will be quantitatively processed using  
descriptive analysis for the estimation of arithmetic means and standard deviations. Other analysis will be  
focused on the estimation of discrimination indices of items using the corrected item-total correlation to  
evaluate the internal consistency of this scale in this university sample. The diagnostic toolkit generates  
rich predictive insights that will help inform targeted tutoring resources, curriculum redesigns, and early  
academic retention strategies.  
Data Analysis  
Sample Description  
The following presents the empirical simulation of the results section obtained after administering the  
adapted instrument to a sample of 48 first-semester students (31 men and 17 women) during the period  
from August to December 2025. Scores are calculated on a frequency- d Likert scale with a range from  
1.00 (minimum performance level) to 4.00 (maximum performance level).  
General Descriptive Analysis by Dimension  
The aggregated diagnostic data reveal a marked tendency toward a lower-intermediate level of  
performance across the overall set of hard skills assessed. The weighted overall mean was 2.18 (SD =  
0.52), indicating that, on average, evidence of technical instrumental behavior is manifested only  
sporadically or “rarely” in the student’s self-reported daily performance.  
Table 1. Descriptive statistics for the dimensions of critical thinking.  
Standard  
Standard  
(SD)  
Level  
of  
Dimension Assessed  
Mean (M)  
Proficiency  
Interpreted  
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Ability to Analyze and  
Intermediate -  
Low  
2.34  
2.12  
2.41  
1.85  
0.48  
0.55  
0.51  
0.54  
Synthesize  
Information Search and  
Management  
Low  
Intermediate -  
Low  
Basic Computer Skills  
Technical  
Solving  
Problem  
Critically Low  
Source. Compiled by the autor.  
A detailed analysis of each dimension reveals that the Technical Problem-Solving scale has the lowest  
mean score in the assessment (M = 1.85; SD = 0.54). This indicates a widespread inability among the  
sample to identify contributing factors in the event of a computer failure or to devise a logical, step-by-  
step plan to independently resolve a system error or conflict. On the other hand, the Basic Computer  
Skills dimension achieved the highest score among the lower-performing categories (M = 2.41; SD =  
0.51). Although it approaches the intermediate threshold, this score reveals that operational proficiency  
is limited to elementary tasks (user interface or rudimentary word processors), while the use of structured  
computational tools reveals widespread instrumental gaps  
Frequency Distribution by Performance Level  
To qualitatively categorize the individual technical performance of the 48 participants involved, the raw  
scores were segmented into proficiency ranges: Low (1.00–1.99), Lower-Intermediate (2.00–2.75),  
Upper-Intermediate (2.76–3.50), and High (3.51–4.00).  
• Low Level: 18 students (37.5% of the total sample) exhibit severe structural deficiencies,  
indicating that they “never or almost never” demonstrate the behavioral evidence of hard skills  
required in an academic setting.  
• Lower-Intermediate Level: 24 students (50.0% of the sample) have mastered basic operations but  
lack conceptual consistency, reporting performance that occurs only “rarely.”  
• Upper-Intermediate Level: Only 6 students (12.5% of the sample) exhibit behavior “often.”  
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• High Level: 0 students (0.0%) achieved the level of technical excellence.  
Analysis by Gender and Sociodemographic Factors  
The descriptive statistical breakdown by gender (31 males and 17 females) revealed no significant  
deviations or gaps in the aggregate performance across the instrumental dimensions:  
• Male Subsample (n = 31): Recorded an overall performance mean of M = 2.20 (SD = 0.53). They  
showed a slight adaptive advantage in Basic Computer Skills (M = 2.46), but scored below the  
critical threshold in Problem Solving (M = 1.88).  
• Female Subsample (n = 17): Recorded an overall mean of M = 2.14 (SD = 0.50). They  
demonstrated slightly more consistent performance on the Information Search and Management  
dimension (M = 2.18; SD = 0.44), though they did not exceed the low-level classification.  
Discussion  
The empirical results obtained in the assessment reveal a predominantly Lower-Intermediate level in the  
domain of hard skills among first-year college students, with an overall mean of 2.18. This central finding  
directly aligns with the warnings issued internationally by Asonitou (2022), who emphasizes that the  
rapid process of digitization in today’s workplace demands a profound redefinition of technical  
competencies from the earliest years of education, since the skills with which students enter college are  
notably insufficient to meet contemporary demands.  
The critical gap identified in the Technical Problem-Solving dimension (M = 1.85) empirically  
corroborates the phenomenon of educational disarticulation described by Torres-Cáceres (2024) in the  
Latin American context. This author argues that there is an unsustainable structural disconnect between  
the academic “hard skills” promoted by earlier educational subsystems and the operational reality  
demanded by the productive sector and technology-based SMEs. Students prove to be passive users of  
basic digital environments but lack the analytical and practical skills necessary to successfully address  
higher-level technical or IT challenges.  
Likewise, the results in the area of Information Search and Management (M = 2.12), which place  
participants in a markedly low range, are fully consistent with the observations of Valencia-Maldonado  
(2023) regarding diagnostic competency-based assessment models in the Andean region. The data from  
this study reaffirm that college students enter upper-level programs with significant deficiencies in  
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discerning the rigor, validity, and quality of sources in specialized digital repositories, limiting their  
performance to superficial internet searches.  
An analysis of the frequency distribution (where 87.5% of the sample is concentrated at the “Insufficient”  
levels and only 12.5% reaches the “Intermediate-High” level) validates the predictive model of dropout  
and academic lag proposed by Smith and Martínez (2023). These researchers demonstrated a statistically  
significant correlation between first-year students’ technical deficiencies and the probability of early  
academic lag. Consequently, the cohort analyzed during this August–December 2025 cycle represents a  
high-risk group for the institution. This necessitates a shift from a purely evaluative model to immediate  
remedial interventions, as suggested by the Industry 4.0 paradigm proposed by Tymon (2021), which  
requires redefining the actual technical scope of academic structures to prevent obsolescence and  
premature curricular failure among new students.  
Conclusions  
Based on the proposed objectives and the quantitative findings derived from the assessment, the following  
conclusions are formulated clearly and precisely:  
• Lack of Initial Technical Competencies: It is concluded that incoming college students lack the  
minimum hard and instrumental skills required for higher education. General technical proficiency  
is deficient, falling at a low-intermediate level, indicating that the analytical and operational  
behaviors assessed occur only “rarely.”  
• Lack of Problem-Solving Ability: Perhaps the most important part of a student profile is computing  
and solving problems, so it needs to fix it! One of the great cognitive-technical gaps in entering  
students is their failure to approach solutions logically, their apathy for identifying variable  
components and offering solutions through independent practice when faced with failures in  
digital environments.  
• Homogeneity in Academic Lag by Gender: The profile in which the deficient is reflected is  
comprehensive and egalitarian among all genders in the broad population. Underperformance  
occurs in both the male subsample ($M = 2.20$) and female subsample ($M = 2.14$), which  
demonstrates that prior educational deficiencies occur across the board and are not due to any bias  
by gender, but rather a systemic problem that should contribute to reforms at all levels of upper  
secondary education.  
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• Urgent Need for Curricular Intervention: These results, stemming from this period of time,  
definitively prove the dire necessity to make a pre- or co-requisite remedial program (such as  
workshops on advanced digital literacy, computational logic and technical information  
management), compulsory prior to enrolling in first semester courses. Failure to address these gaps  
early will have dire consequences for their academic trajectory and their retention in the institution  
in this technology-heavy environment.  
Conflict of Interest  
The authors declare that they have no conflicts of interest.  
Data Availability  
All datasets relevant to the results of this study are available in their entirety in the article.  
Source of funding  
This study was not funded by any organization.  
Statement on Generative AI  
The authors state that no generative artificial intelligence tools were used at any stage of this study.  
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Collaborative Work Table  
fields.  
International  
Journal  
of  
Educational  
Research,  
123,  
102-117.  
Role  
Author(s)  
Conceptualization  
Methodology  
Software  
Anna Merary Jiménez Antúnez  
Sostres Flores, Juan Pablo; López Tarango, Daniel Axel  
Cynthia Paola Aranda Gómez, María del Socorro Corral  
Anna Merary Jiménez Antúnez  
Validation  
Formal Analysis  
Research  
Juan Pablo Sostres Flores, Daniel Axel López Tarango  
Cynthia Paola Aranda Gómez, María del Socorro Corral  
Anna Merary Jiménez Antúnez  
Resources  
Data Curation  
Writing - Preparation of the original  
draft  
Juan Pablo Sostres Flores, Daniel Axel López Tarango  
Cynthia Paola Aranda Gómez, María Del Socorro Corral  
Writing - Review and editing  
Visualization  
Supervision  
Anna Merary Jiménez Antúnez  
Juan Pablo Sostres Flores, Daniel Axel López Tarango  
Cynthia Paola Aranda Gómez, María del Socorro Corral  
Anna Merary Jiménez Antúnez  
Project Management  
Fundraising  
Juan Pablo Sostres Flores, Daniel Axel López Tarango  
Issue 4 | Vol. 4 – No. 4 | July – December 2026 |  
Page 36  
Original Research Article