Revista de Ciencias Sociales (RCS)

Vol. XXXI, No. 4, Octubre - Diciembre 2025. pp. 45-58

FCES - LUZ ● ISSN: 1315-9518 ● ISSN-E: 2477-9431

Como citar: Rodriguez-Barboza, J. R., Carreño-Flores, O. D., Mendoza-Zuñiga, M., y Michca-Maguiña, M. H. M. (2025). Integrating digital skills into English language teaching: Implications for teacher performance. Revista De Ciencias Sociales, XXXI(4), 45-58.

Integrating digital skills into English language teaching: Implications for teacher performance

Rodriguez-Barboza, Jhonny Richard*

Carreño-Flores, Oscar David**

Mendoza-Zuñiga, Marleni***

Michca-Maguiña, Mary Hellen Mariela****

Abstract

Research underscores the importance of enhancing teachers’ digital competencies to effectively address contemporary educational challenges and improve educational quality. Therefore, this study aimed to examine the impact of digital skills on the teaching performance of English teachers at a private university in Lima, Peru. Adopting a quantitative approach with a non-experimental and correlational design, the study conducted surveys with 85 teachers. The results indicate a significant relationship between digital skills and overall teaching performance, explaining 71.4% of the variability. However, the study did not find a significant impact of digital skills on specific dimensions of teaching performance, such as disciplinary proficiency, didactic aspects, didactic thinking, motivation, and self-efficacy. This suggests that other factors, such as academic background and professional experience, may have a greater influence in these areas. Finally, the study highlighted the need for universities to prioritize the development of digital skills among educators, while recognizing the continued importance of traditional academic and professional factors in teaching effectiveness.

Keywords: Digital skills; teacher performance; English language teaching; higher education; professional development.

*        Doctor en Educación. Magister en Didáctica en Idiomas Extranjeros. Magister en Educación. Docente Investigador del Centro de Investigación de la Creatividad en la Universidad de Ciencias y Artes de América Latina, Lima, Perú. E-mail: jrodriguezb@ucal.edu.pe ORCID: https://orcid.org/0000-0001-9299-6164

**      Doctorando en Administración, enfocado en Fortalecer Habilidades de Investigación y Contribuir al Desarrollo del Campo Educativo. Magister en Negocios Internacionales. Docente de la Escuela de Posgrado en la Universidad César Vallejo, Lima, Perú. E-mail: ocarrenof@ucvvirtual.edu.pe ORCID: https://orcid.org/0009-0006-3082-7254

***    Doctoranda en Administración, enfocado en Fortalecer Habilidades de Investigación y Contribuir al Desarrollo del Campo Educativo. Magister en Gestión Pública. Docente Investigadora de la Escuela de Posgrado en la Universidad César Vallejo, Lima, Perú. E-mail: mmendozazu@ucvvirtual.edu.pe ORCID: https://orcid.org/0000-0002-4882-5592

****  Doctora en Contabilidad y Finanzas. Doctora en Administración de la Educación. Docente Investigadora de la Escuela de Posgrado en la Universidad César Vallejo, Lima, Perú. E-mail: mmichcam@ucvvirtual.edu.pe ORCID: https://orcid.org/0000-0001-7282-5595

Recibido: 2025-06-06 • Aceptado: 2025-08-24

Integración de habilidades digitales en la enseñanza del inglés: implicaciones para el desempeño docente

Resumen

La investigación subraya la importancia de mejorar las competencias digitales de los docentes para abordar eficazmente los desafíos educativos contemporáneos y mejorar la calidad educativa. En ese sentido, este estudio tuvo como objetivo examinar el impacto de las habilidades digitales en el desempeño docente de profesores de inglés en una universidad privada en Lima, Perú. Adoptando un enfoque cuantitativo con un diseño no experimental y correlacional, el estudio realizó encuestas con 85 docentes. Los resultados indican una relación significativa entre las habilidades digitales y el desempeño docente general, explicando el 71,4% de la variabilidad. Sin embargo, el estudio no encontró un impacto significativo de las habilidades digitales en dimensiones específicas del desempeño docente, como la competencia disciplinaria, los aspectos didácticos, el pensamiento didáctico, la motivación y la autoeficacia. Esto sugiere que otros factores, como la formación académica y la experiencia profesional, pueden tener una mayor influencia en estas áreas. Finalmente, el estudio destacó la necesidad de que las universidades prioricen el desarrollo de habilidades digitales entre los educadores, al tiempo que reconocen la importancia continua de los factores académicos y profesionales tradicionales en la eficacia docente.

Palabras clave: Habilidades digitales; desempeño docente; enseñanza del inglés; educación superior; desarrollo profesional.

Introduction

In recent years, the international education landscape has faced increasing challenges in integrating digital competencies (DC) into teaching practices. Studies highlight that, although technological tools are widely available and constitute strategic pillars, their effective use in educational settings remains inconsistent (Mejía et al., 2025).

For example, Pozo et al. (2020) found that only 25.99% of educators effectively implemented methodologies such as Flipped Learning, highlighting the lack of adequate DCs. Furthermore, Rodriguez (2019) noted that a significant proportion of preservice educators in Spain demonstrated basic or intermediate levels of CD, suggesting that many lack the fundamental skills needed to thrive in technology-enhanced educational environments. These findings highlight the urgent need for targeted interventions to close the gap between technological availability and its effective pedagogical use.

At the national level, the integration of digital skills into teaching practices also presents notable challenges. Chávez (2023) did not find a significant correlation (ρ = 0.609) between DCs and teaching performance (TP) among 25 university educators, possibly due to contextual or methodological limitations. In contrast, Guidotti (2022) reported a moderate association (ρ = 0.563) in a study involving 48 teachers, with 71% exhibiting high levels of DCs. This suggests that although DCs positively influence TP, external factors such as institutional policies or educational environments play a crucial role.

Varas (2022) further supports this notion, identifying a positive relationship between DCs and TP through a correlational study that attributed 46.7% of TP variability to individual and environmental factors. These national findings emphasize the complexity of improving teacher performance through the development of DCs, which requires a deeper exploration of contextual variables.

This study is highly relevant because it aims to address a critical gap in the effective integration of digital competencies into teacher performance, thus contributing to the advancement of educational quality. By examining the intricate influences among these variables, the research provides valuable insights for policymakers, educators, and institutions seeking to optimize digital competency frameworks. Its impact goes beyond theoretical contributions, offering practical recommendations for improving teacher training programs, fostering innovation in pedagogical strategies, and aligning educational practices with contemporary technological demands. Ultimately, this study aims to empower educators to use digital tools effectively, thereby enriching students’ learning experiences and promoting educational equity.

The overall objective of this research is to explore the influence of digital competences on teacher performance in educational settings. Specifically, it seeks to: (1) assess the current level of digital competences among educators, (2) evaluate the influence of digital competences on teacher performance, and (3) identify contextual factors that mediate this impact. The main research question is: To what extent do digital competences influence teacher performance, and which contextual factors mediate this influence? This research seeks to uncover practical insights that will pave the way for a more effective integration of digital competences into pedagogical practices.

1. Theoretical foundation

1.1. Theories on Digital Competencies

Digital Competencies (DC) are fundamental to modern education, encompassing a combination of skills, knowledge, and attitudes that enable individuals to navigate, evaluate, and use digital tools effectively. Flores-Lueg & Roig-Vila (2019); Delgado et al. (2020); Romero et al. (2023); and Núñez et al. (2024), define DC as educational processes that foster values, beliefs, and skills along with the strategic use of information and communication technologies (ICTs).

They emphasize that digital learning technologies allow learners to actively manage their digital learning processes, transforming information into knowledge. Reyna (2022) elaborates that digital learning technologies integrate pedagogical and didactic approaches, allowing students to innovate and self-direct their learning. Similarly, the European Commission (2022) highlights the strategic and secure management of digital educational technologies, positioning digital learning technologies as a key element in professional contexts.

Che Had & Ab Rashid (2019) argue that digital literacy includes the ability to manage digital devices, networks, and communication tools to effectively handle diverse types of information. Mercader & Gairín (2020); Oliveira et al. (2021); along with Moreira-Choez et al. (2024), emphasize the pedagogical benefits of digital tools, highlighting their ability to create interactive and engaging learning experiences while respecting the role of the educator. These theoretical perspectives establish digital literacy as a transformative force in educational practices, connecting technological access with meaningful application.

The European Commission’s DigComp 2.2 framework (2022) identifies five essential dimensions of digital skills:

a. Information and Data Literacy, which emphasizes critical evaluation and storage of digital information for informed decision-making, as highlighted by Falloon (2020).

b. Communication and Collaboration, which fosters effective interactions, online participation and intercultural awareness, with Falloon (2020) highlighting its role in improving teaching and learning processes.

c. Digital Content Creation, focused on the innovative generation of technological projects, defended by Diez-Sanmartín et al. (2020) as essential for modern education.

d. Security, which prioritizes data protection and responsible digital citizenship to mitigate online risks, as Zaharov et al. (2018) warn.

e. Problem Solving, which promotes creative solutions to technical challenges through the strategic use of technology, as emphasized by the European Commission (2022).

By integrating these perspectives, it becomes clear that digital competencies transcend technical skills and instead promote the empowerment of individuals’ capabilities, making technology a useful tool for learning. In essence, digital competencies empower students to navigate a digitalized world, transforming access to technology into applied knowledge relevant to their academic and professional lives.

1.2. Theories on Teacher Performance

Teacher Performance (TP) refers to educators’ ability to effectively manage and execute their instructional roles, ensuring quality learning outcomes. Kartini et al. (2020) describe TP as the accomplishment of specific tasks influenced by classroom dynamics, the institutional context, student needs, and teacher characteristics. Fabelico & Afalla (2020) frame TP as a process of sharing theoretical and practical knowledge while mastering pedagogical and didactic techniques. Kusumaningrum et al. (2019) argue that TP builds on current knowledge and experience, adapting to diverse educational demands.

Bush & Grotjohann (2020) highlight the correlation between teachers’ professional competence and student outcomes, emphasizing the role of PD in reducing educational gaps. Gómez-Tejedor et al. (2020) link PD with student achievement and classroom leadership, underscoring that well-trained teachers ensure high-quality education. Similarly, Chng & Lund (2018) identify various factors that influence PD, including teaching strategies, use of classroom resources, behavior management, and family collaboration.

Carlos-Guzmán (2016) identifies five key dimensions of teacher performance:

a. Disciplinary Knowledge, which emphasizes the ability to organize ideas methodically and apply them in context, as highlighted by Klaassen (2018), who highlights the importance of mastering the content for effective engagement with students.

b. Teaching Skills, which focus on teaching students across various educational levels, with Gorev et al. (2018) advocating innovative methodologies to maintain interest and engagement.

c. Didactic Thinking, which involves the ideological clarity of educators and methodological frameworks, aligning teaching objectives with the needs of students, as emphasized by Kjällander et al. (2018).

d. Motivation, in which both Carlos-Guzmán (2016) and Borah (2021) underline its role in inspiring students to achieve their academic potential.

e. Self-efficacy, which involves teachers’ confidence to face challenges, as highlighted by Perera et al. (2019), who emphasize the importance of responsibility and continuous professional development.

The interconnections between digital skills and teacher unemployment pose an effective educational approach, which requires more than the use of technologies. In this context, digital skills are not technical skills, but a set of capabilities that enhance students’ independent learning, innovation, and critical thinking. Through an educational approach, the integration of teaching skills into classrooms is crucial for preparing students for development in a globalized world.

2. Methodology

2.1. Type and design of the research

This study is classified as basic research, following the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (Concytec, 2020) categorization, since it draws on existing theories to propose new hypotheses or modify current ones, contributing to the advancement of scientific knowledge. The research employs a non-experimental, cross-sectional (or transversal) methodological design, with a quantitative approach based on systematic procedures and empirical evidence.

Statistical methods, including descriptive and inferential techniques, are used to investigate the causal relationship between the study variables (V1 and V2). It is defined as a causal-correlational investigation, aiming to explore the causality and reciprocal influence between these two variables, according to the framework proposed by Hernández-Sampieri & Mendoza (2018). By applying the hypothetico-deductive method, this study formulates hypotheses that are tested and validated or refuted through the analysis of empirical results, providing a detailed examination of the dynamics and implications of the interaction between V1 and V2.

2.2. Population, sample and sampling

Hernández-Sampieri & Mendoza (2018) define a population as the complete set of individuals or cases that share common characteristics in terms of content, location, and time. In this study, the target population is made up of English teachers within a corporate group. The sample includes 85 teachers, representing a census sample as it covers 100% of the population. The selection criteria included teachers of both genders who worked under specific conditions within these institutions, excluding those from other departments or academic levels. The sampling method used was non-probability and convenience-based.

2.3. Data collection techniques and instruments

This study used surveys as the primary data collection technique to measure Variable 1 (digital competencies) and Variable 2 (teaching performance). The instruments, validated by three academic experts, met the criteria of sufficiency, clarity, coherence, and relevance, according to Medina-Díaz & Verdejo-Carrión (2020). For digital competencies, a 20-item virtual questionnaire designed by the European Commission (2022) was used; while for teaching performance, a similar 20-item instrument by Carlos-Guzmán (2016) was used. The instruments demonstrated reliability, with Cronbach’s alpha coefficients of 0.834 and 0.904, respectively.

2.4. Data collection procedures

A non-probability sampling method was used to select a representative sample of English teachers, ensuring diversity in terms of gender, English proficiency level, and type of institution. The questionnaires were distributed digitally to encourage participation. The data collection process was implemented in several phases: pilot testing, data collection, data analysis, and validation of relevant data.

2.5. Data analysis method

Data analysis followed a quantitative, correlational, and causal approach. Initially, descriptive statistics were used to summarize the basic data collected. Ordinal logistic regression was then applied to identify associations between variables and determine the degree of their relationships.

2.6. Ethical considerations

This research complied with the ethical principles established in Resolution No. 0262-2020/UCV, guaranteeing the well-being and rights of the participants while maintaining scientific rigor. Participant anonymity was protected through data encryption and secure storage, accessible only to the research team. Intellectual property rights were respected, and informed consent was obtained, ensuring the integrity of the study and its contribution to the advancement of English language teaching.

3. Results and discussion

3.1. Descriptive results of digital competencies and their dimensions

This section presents the descriptive results of digital competencies and their dimensions, providing an overview of the frequency distribution of teachers’ competencies in terms of beginner, basic, intermediate, and advanced levels. Furthermore, it highlights performance in specific areas such as information and digital literacy, communication and collaboration, content creation, security, and problem-solving. These results offer valuable insights into the level of digital competencies of English teachers at the three institutions studied.

Table 1 presents the descriptive results of digital competencies, categorizing teachers into four levels: Beginner, basic, intermediate, and advanced. It shows the distribution of digital competencies among English teachers at three institutions, highlighting that the majority are at the basic level (37.65%), followed by intermediate (27.06%), advanced (12.94%), and a smaller percentage at the beginner level (22.35%). The breakdown by dimension reveals that teachers scored highest in the “security” dimension (43.5% advanced), while the lowest performance was observed in “information and digital literacy” (30.6% beginner) and “digital content creation” (37.6% beginner). These results highlight the varying levels of digital competence among teachers and point to areas that may require further development to improve their digital skills.

Table 1

Frequency distribution of digital skills and their dimensions

Variables and dimensions	Beginner (%)	Essential (%)	Intermediate (%)	Advanced (%)
(V1) Digital Competencies	22.35%	37.65%	27.06%	12.94%
(D1) Information and Digital Literacy	30.6%	56.5%	0.0%	12.9%
(D2) Communication and Collaboration	18.8%	68.2%	1.2%	11.8%
(D3) Digital Content Creation	37.6%	36.5%	1.2%	24.7%
(D4) Security	40.0%	16.5%	0.0%	43.5%
(D5) Problem Solving	17.6%	12.9%	32.9%	36.5%

Source: Own elaboration, 2025.

The results in Table 2 show variability in teacher performance levels across different dimensions. Overall, teacher performance falls primarily into the “Achieved” (37.6%) and “Satisfactory” (27.1%) categories, with a smaller percentage achieving “Proficient” (12.9%). Regarding disciplinary knowledge (D1), a significant proportion showed “Competent” performance (32.9%), indicating solid mastery of the content. In contrast, didactic aspects (D2) are mainly distributed in the “Achieved” category (41.2%).

Table 2

Frequency distribution of Teaching Performance and its dimensions

Variables and dimensions	In progress (%)	Accomplished (%)	Satisfying (%)	Competent (%)
(V2) Teaching Performance	22.4	37.6	27.1	12.9
(D1) Disciplinary Knowledge	7.1	37.6	22.4	32.9
(D2) Didactic Aspects	20.0	41.2	24.7	14.1
(D3) Didactic Thinking	40.0	31.8	0.0	28.2
(D4) Motivation	24.7	15.3	15.3	44.7
(D5) Self-efficacy	20.0	21.2	30.6	28.2

Source: Own elaboration, 2025.

A worrying finding is observed in didactic thinking (D3), where a high percentage of teachers are in the “In Progress” category (40.0%), with no representation in the “Satisfactory” category. Motivation (D4) stands out for having the highest percentage in the “Competent” category (44.7%), reflecting strength among teachers. However, self-efficacy (D5) shows a more balanced distribution, with the majority in the “Satisfactory” (30.6%) and “Competent” (28.2%) levels, suggesting areas for improvement in professional confidence.

This discussion takes on new meaning when we understand that most teachers have a basic level of digital skills, revealing variability across these dimensions. As part of a security process, focused group training is suggested, allowing for a closer connection between the pedagogical and the educational. These findings reflect the current state of teachers and also point the way for future educational interventions and policies.

3.2. Inferential results

Normality tests (see Table 3) indicate that the variables “Digital Competencies” and “Teaching Performance” do not follow a normal distribution. Both the Kolmogorov-Smirnov and Shapiro-Wilk tests yield statistically significant results (p < 0.000) for each variable, rejecting the null hypothesis of normality. These findings suggest that nonparametric statistical methods may be more appropriate for analyzing these variables.

Table 3

Normality Test of Variables: Digital Competencies and Teaching Performance

Test	Statistical	df	Sig.
Kolmogorov-Smirnov
Competencies Digital	0.089	213	0.000
Performance Teaching	0.136	213	0.000
Shapiro-Wilk
Competencies Digital	0.953	213	0.000
Performance Teaching	0.950	213	0.000

Source: Own elaboration, 2025.

In Table 4, the results for the general hypothesis reveal a significant improvement in model fit when comparing the “Intercept Only” model (-2 Log Likelihood = 205.361) with the “Final” model (-2 Log Likelihood = 101.941), with a Chi-Square value of 103.420 (p < 0.001) and a Nagelkerke R² of 0.714, indicating strong explanatory power. These findings suggest that the predictors included in the final model substantially improve its ability to explain the relationship between the studied variables. The Cox & Snell R² and McFadden R² values (0.704 and 0.286, respectively) further support the robustness of the model for the general hypothesis.

Table 4

Information on Model Fit for General and Specific Hypotheses

Hypothesis/Dimension	Model	-2 Log Likelihood	Chi-Square	df	Sig.	Cox & Snell R²	Nagelkerke R²	McFadden R²
General Hypothesis	Intersection only	205.361				0.704	0.714	0.286
	End	101,941	103.420	18	0.000
Specific Hypotheses
Disciplinary Knowledge	Intersection only	20,800				0.723	0.783	0.499
	End	18.139	2.661	3	0.447
Didactic Aspects	Intersection only	20.656				0.691	0.709	0.319
	End	17.913	2.743	3	0.433
Didactic Thinking	Intersection only	131.157				0.279	0.294	0.110
	End	103.308	27,849	18	0.064
Motivation	Intersection only	130.204				0.224	0.237	0.088
	End	108,695	21,509	18	0.255
Self-efficacy	Intersection only	152.248				0.201	0.207	0.064
	End	133.209	19.040	18	0.389

Source: Own elaboration, 2025.

For the specific hypotheses, results vary across dimensions. Disciplinary knowledge and didactic aspects show non-significant improvements in model fit, with chi-square values of 2.661 (p = 0.447) and 2.743 (p = 0.433), respectively, despite their relatively high Nagelkerke R² values (0.783 and 0.709). Didactic thinking shows a moderate improvement in model fit (Chi-Square = 27.849, p = 0.064) with a lower Nagelkerke R² value (0.294), while motivation and self-efficacy exhibit weak improvements, with Chi-Square values of 21.509 (p = 0.255) and 19.040 (p = 0.389), and Nagelkerke R² values of 0.237 and 0.207, respectively. These results suggest that some dimensions may have limited predictive power, requiring further refinement or alternative approaches to better capture their impact.

Overall, the data suggest that the general hypothesis model provides a robust framework for understanding the relationship between variables, as evidenced by its good fit and explanatory power. However, the variability in the results for the specific hypotheses highlights differences in the predictive strength of individual dimensions. Disciplinary knowledge and didactic aspects exhibit high potential for explanatory value, although their lack of statistical significance requires further investigation into model specification or the inclusion of additional variables.

On the other hand, the weaker fit and lower R² values for dimensions such as motivation and self-efficacy indicate that these aspects may require alternative theoretical or methodological approaches to better clarify their influence within the overall framework of teacher performance. These findings underscore the need for a nuanced and multidimensional perspective in the analysis and interpretation of teacher competencies.

As can be seen, this research explored the influence of digital competencies (DC) on teacher performance (TP) in the educational field. The results reveal valuable insights into the general and specific objectives of the study, highlighting areas of overlap with existing literature and potential implications for practice.

Regarding the general relationship between Digital Competencies and Teacher Performance, the results confirm the hypothesis that digital competencies significantly influence teacher performance. The Nagelkerke Pseudo R-Square value for the general hypothesis was 0.714, indicating that 71.4% of the variability in teacher performance can be explained by digital competencies. These findings are consistent with those of Sanchez (2022), who reported a strong positive correlation (ρ = 0.766) between these variables. Similarly, Guidotti (2022) found a moderate positive correlation (ρ = 0.563), reinforcing the idea that higher levels of digital competence are associated with better teacher performance.

However, the contrasting results of Chávez (2023), who did not find a significant relationship (ρ = 0.609, p < 0.05), suggest that this influence may vary depending on educational contexts, methodologies, or the degree of technological integration within institutions. These discrepancies underline the complexity of the relationship and highlight the importance of contextual factors in mediating the effects of digital competencies on teacher performance.

Regarding the Specific Dimensions of Teaching Performance, Disciplinary Knowledge. The specific hypothesis related to disciplinary knowledge did not yield significant results (χ ² = 2.661, df = 3, p = 0.447), despite a high Nagelkerke R-Square value of 0.783. This suggests that, although digital competences may contribute to variability in this dimension, they are not a decisive factor. These results are consistent with those of Pérez-Escoda et al. (2019); Caena & Redecker (2019), who argue that professional experience and traditional training influence disciplinary expertise more strongly than digital skills.

Similarly, for the didactic aspects, the model did not show a significant improvement (χ ² = 2.743, df = 3, p = 0.433), with a Nagelkerke R-Square value of 0.709. This indicates that digital competences are not a main determinant of teaching techniques or knowledge transmission. Previous studies, such as those by Siemens (2004); and Fernández-Batanero et al. (2020), highlight that pedagogical training and classroom experience play a more significant role in shaping teaching skills. These results coincide with those of Viñoles-Cosentino et al. (2022), who highlight the importance of continuous professional development to improve teaching practices.

The didactic thinking dimension showed a moderate trend (χ ² = 27.849, df = 18, p = 0.064), with a Nagelkerke R-Square value of 0.294, explaining 29.4% of the variability. Although not statistically significant, the results suggest a possible influence of digital competencies on reflective and adaptive teaching strategies. These findings are consistent with studies by Caena & Redecker (2019); and Pozo et al. (2020), which suggest that digital skills can support teachers in rethinking pedagogical approaches and incorporating innovative methods.

The results for motivation were not significant (χ ² = 21.509, df = 18, p = 0.255), with a Nagelkerke R-Square value of 0.237, representing 23.7% of the variability. Motivation seems to be more influenced by intrinsic and extrinsic factors, such as passion for teaching and working conditions, as highlighted in the studies by Viñoles-Cosentino et al. (2022); and Sanabria-Navarro et al. (2023). Research by Baca (2021) and Varas (2022), emphasizes that the work environment and professional recognition are more critical drivers of motivation than digital skills.

For self-efficacy, the results showed that there was no significant relationship (p = 0.389), with a Nagelkerke R-Square value of 0.207, accounting for 20.7% of the variability. Self-efficacy, defined as the belief in one’s ability to perform teaching roles effectively, appears to be more dependent on classroom success and institutional support, as noted by Heidari (2021); and Mercader & Gairín (2021). Research by Che Had & Ab Rashid (2019); and Van Laar et al. (2020) underscores the importance of ongoing support and professional development in enhancing self-efficacy.

The findings demonstrate that while digital competencies have a significant overall impact on teacher performance, their influence on specific dimensions such as disciplinary knowledge, didactic aspects, and motivation is less pronounced. These results suggest that professional training and experience continue to play a key role in shaping these dimensions.

To address these gaps, institutions should consider reviewing the measurement of variables and adopting a more integrated approach that captures the nuanced application of digital skills in educational contexts. Furthermore, incentivizing continuous improvement programs that combine digital competencies with pedagogical and managerial skills could provide a more holistic framework for teacher development. This integrated strategy can improve teacher performance while addressing the diverse challenges of modern educational environments.

Conclusions

This study explored the influence of digital competencies on teacher performance, offering valuable insights into their interconnected dynamics. The results confirm that digital competencies have a significant impact on overall teacher performance, as evidenced by the Nagelkerke Pseudo R-squared value of 0.714. This indicates that digital skills play a crucial role in improving educators’ effectiveness in the classroom. However, the analysis also revealed varying levels of influence on specific dimensions of teacher performance.

While the overall relationship between digital competencies and teaching performance was significant, specific dimensions —such as disciplinary knowledge, didactic aspects, motivation, and self-efficacy— showed limited or nonsignificant results. These findings are aligned with existing literature, which suggests that factors such as traditional training, professional experience, and intrinsic motivation are key drivers in shaping these specific dimensions. Thus, although digital competencies are important, they are not the only determinants of teaching excellence.

The results highlight the need for a comprehensive approach to teacher development. Institutions must integrate digital competencies with pedagogical and management training to address the multifaceted challenges educators face. Continuous professional development programs, which balance the enhancement of digital and traditional teaching skills, can better prepare teachers for the changing demands of the classroom.

Future research should delve deeper into the contextual factors that mediate the impact of digital competencies on teacher performance. Exploring the differences between educational systems, technological infrastructures, and institutional support mechanisms can provide a more complete understanding of this complex relationship. Furthermore, refining the methodologies used to assess digital competencies and their application in the classroom will offer more practical insights. This study underscores the crucial role of digital competencies in modern education, while recognizing that teacher performance depends on a variety of factors.

As can be seen, the study contributes by examining the influence of digital skills on teaching performance, providing critical insights into this reality. The findings demonstrate a significant relationship between digital skills and overall teaching performance. However, limitations are acknowledged, as the study did not find a significant impact of digital skills on specific dimensions of teaching performance, such as disciplinary knowledge, didactic aspects, motivation, and self-efficacy.

For this reason, for future lines of research, we suggest delving deeper into the differences between educational systems, technological infrastructures, and institutional support mechanisms. Furthermore, we recommend reviewing methodologies for evaluating the application of digital skills in the classroom, with the aim of obtaining a more comprehensive analysis of this relationship.

Bibliographic references

Ahmed, S., Nielsen, I. E., Tripathi, A., Siddiqui, S., Ramachandran, R. P., y Rasool, G. (2023). Transformers in time-series analysis: A tutorial.
arXiv:2205.01138. https://doi.org/10.1007/s00034-023-02454-8

Alarcón-Rodríguez, N. C., Castro-Pinzón, G. H., y Nauzan-Ceballos, V. H. (2024). Impacto de la tasa representativa del mercado en el IPC en Colombia. Reflexiones Contables, 7(2), 36-52. https://doi.org/10.22463/26655543.4669   

Ayitey, M., Appiahene, P., Appiah, O., y Ninfaakang, C. N. (2023). Forex market forecasting using machine learning: Systematic literature review and meta-analysis. Journal of Big Data, 10, 9. https://doi.org/10.1186/s40537-022-00676-2  

Bahdanau, D., Cho, K., y Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Proceedings of the International Conference on Learning Representations, arXiv:1409.0473. https://doi.org/10.48550/arXiv.1409.0473

Blagov, B. (2019). Exchange rate uncertainty and import prices in the euro area. Review of International Economics, 27(5), 1537-1572. https://doi.org/10.1111/ROIE.12434  

Dhasmana, A. (2013). Real effective exchange rate and manufacturing sector performance: Evidence from Indian firms. IIM Bangalore Research Paper No. 412. https://doi.org/10.2139/SSRN.2284077  

Giramkar, N. S. (2020). Multivariable Regression Analysis for prediction of factors affecting car mileage. Journal of Emerging Technologies and Innovative Research, 7(9), 1258-1263. https://www.jetir.org/view?paper=JETIR2009171  

Lira, M. V., y Senhorinho, E. W. (2022). Aplicação do modelo ARIMA para previsão de vendas da rede de farmácias Rossmann. In T. Nepomuceno, R. Lima y N. Rodrigues (Eds.), Avanços na Análise de Séries Temporais Livro de Aplicações (Vol. 1, pp. 144-162). Editora Educacionista. https://doi.org/10.31235/osf.io/v4wce  

Maknickiene, N., Stankeviciene, J., y Maknickas, A. (2020). Comparison of forex market forecasting tools based on evolino ensemble and technical analysis indicators. Romanian Journal of Economic Forecasting, XXIII(3), 134-148. https://ipe.ro/new/rjef/rjef3_20/rjef3_2020p134-148.pdf  

Meneses, M., Toro, J. S., y Riascos, J. C. (2017). El comportamiento del precio del petróleo y la volatilidad en la tasa de cambio: Análisis de impacto de las variaciones del WTI y de la tasa de interés referencia sobre la tasa de cambio nominal en Colombia, periodo 2013-2015. Tendencias, 18(1), 13-40. https://doi.org/10.22267/rtend.171801.62

Mignon, V. (2024). Principles of Econometrics: Theory and applications. Springer International Publishing. https://doi.org/10.1007/978-3-031-52535-3

Molina, R., y Peña, D. M. (2021). Forecasting the future trend of the EUR/USD exchange rate, using advanced technical analysis tools. Centro Sur, 6(2), 74-97. https://doi.org/10.37955/cs.v6i2.241

Richardson, A. M., Joshy, G., y D’Este, C. A. (2018). Understanding statistical principles in linear and logistic regression. The Medical Journal of Australia, 208(8), 332-334. https://doi.org/10.5694/MJA17.00222

Sharma, A. (2023). Exchange rate shocks, multinational firms and access to finance. Review of International Economics, 32(3), 907-933. https://doi.org/10.1111/roie.12708  

Sprockel, J. J., Ramírez, A., y Jiménez-Canizales, C. E. (2023). Editorial. Inteligencia artificial y aprendizaje automatizado, ¿oportunidad o amenaza? Revista Colombiana de Endocrinología, Diabetes & Metabolismo, 10(2), 2-5. https://doi.org/10.53853/encr.10.2.797  

Superintendencia Financiera de Colombia – SFC (2015). Circular Reglamentaria Externa DODM-146. 18 de diciembre de 2015. https://www.banrep.gov.co/sites/default/files/reglamentacion/archivos/Boletin_64_18_dic_2015_%20DODM_146.pdf   

Yu, L., Wang, S., y Lai, K. K. (2005). A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers & Operations Research, 32(10), 2523-2541. https://doi.org/10.1016/j.cor.2004.06.024