Competencia digital docente en Panamá : diagnóstico DigCompEdu y claves sistémicas para transformar el aprendizaje

Raúl Coronado Aguirre; Kiria I. Lam Valdés; Dalys Graell; Dayse D. Pérez

doi:10.48204/j.guacamaya.v10n2.a9799

Enviado abril 23, 2026

Publicado 2026-04-24

Artículos

Vol. 10 Núm. 2 (2026): Revista Científica Guacamaya

Competencia digital docente en Panamá : diagnóstico DigCompEdu y claves sistémicas para transformar el aprendizaje

Raúl Coronado Aguirre⁺⁻
Kiria I. Lam Valdés⁺⁻
Dalys Graell⁺⁻
Dayse D. Pérez⁺⁻

DOI https://doi.org/10.48204/j.guacamaya.v10n2.a9799

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DOI: 10.48204/j.guacamaya.v10n2.a9799

Publicado: 2026-04-24

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Coronado Aguirre, R., Lam Valdés, K. I., Graell, D., & Pérez, D. D. (2026). Competencia digital docente en Panamá : diagnóstico DigCompEdu y claves sistémicas para transformar el aprendizaje. Revista Científica Guacamaya, 10(2), 114–125. https://doi.org/10.48204/j.guacamaya.v10n2.a9799

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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Resumen

Este artículo presenta los resultados de un diagnóstico inicial de competencia digital docente aplicado a participantes del seminario-taller Didáctica Digital e Innovación en la Educación Superior: Competencias docentes para transformar el aprendizaje. Se analizó una muestra de N = 120 mediante un instrumento de 36 ítems en escala 1–5 (1 = Nunca; 5 = Siempre), organizado en seis secciones que corresponden a las áreas de DigCompEdu: (1) compromiso profesional, (2) recursos digitales, (3) enseñanza y aprendizaje, (4) evaluación, (5) empoderamiento del estudiante y (6) facilitación de la competencia digital del estudiante (Redecker, 2017). Los resultados muestran un desempeño global alto (M = 3.98; DE = 0.68; Mediana = 4.06; IC95% [3.86, 4.10]) y una distribución con predominio del nivel B2 (Avanzado). La mayor fortaleza se observa en Evaluación (M = 4.28), en línea con la evidencia sobre evaluación formativa, retroalimentación y uso de rúbricas (Black & Wiliam, 1998; Brookhart, 2018; Hattie & Timperley, 2007; Sadler, 1989; Wiliam, 2011). Las oportunidades de mejora relativa se concentran en Compromiso profesional (M = 3.78) y en Competencia digital del estudiante (M = 3.80). Con base en estos hallazgos se discuten implicaciones pedagógicas y sistémicas, y se propone un plan institucional por fases alineado al enfoque de UNESCO ICT-CFT (UNESCO, 2018).

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Bates, T. (2019). Teaching in a digital age: Guidelines for designing teaching and learning. BCcampus.
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Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Carnegie Foundation.
Brookhart, S. M. (2018). How to create and use rubrics for formative assessment and grading. ASCD.
CAST. (2018). Universal Design for Learning Guidelines (version 2.2). https://udlguidelines.cast.org/
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
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Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.

Content Top

[1] Barrett, H. (2007). Researching electronic portfolios and learner engagement. The REFLECT Initiative.

[2] Bates, T. (2019). Teaching in a digital age: Guidelines for designing teaching and learning. BCcampus.

[3] Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364.

[4] Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.

[5] Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Carnegie Foundation.

[6] Brookhart, S. M. (2018). How to create and use rubrics for formative assessment and grading. ASCD.

[7] CAST. (2018). Universal Design for Learning Guidelines (version 2.2). https://udlguidelines.cast.org/

[8] Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

[9] Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

[10] Kotter, J. P. (1996). Leading change. Harvard Business School Press.

[11] Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.

[12] Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

[13] NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.

[14] Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Publications Office of the European Union.

[15] Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144.

[16] Serrano Hidalgo, M., & Llorente Cejudo, M. del C. (2023). El modelo DIGCOMPEDU como base de la competencia digital docente en el contexto de una universidad latinoamericana. EDMETIC, Revista de Educación Mediática y TIC, 12(2). https://doi.org/10.21071/edmetic.v12i2.16011

[17] Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

[18] UNESCO. (2018). UNESCO ICT Competency Framework for Teachers (ICT-CFT). United Nations Educational, Scientific and Cultural Organization.

[19] UNESCO. (2019). Recommendation on Open Educational Resources (OER). UNESCO.

[20] UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.

[21] Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press.

[22] Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.

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