Impact des applications éducatives sur les mécanismes neurocognitifs de l'apprentissage

Sahar BOUHAMIDI; Ayoub KABBACHI

Sahar BOUHAMIDI, Ayoub KABBACHI

Pages 90–97 · Université Cadi Ayyad, Marrakech, Morocco · Université Ibn Zohr Agadir, Morocco

Résumé

Cette étude s'intéresse à l'impact des technologies éducatives en santé sur la charge cognitive et les processus d'apprentissage, en examinant si leur utilisation favorise ou entrave l'efficacité cognitive. Réalisée auprès des étudiants en rééducation à Agadir au Maroc, elle analyse les relations entre charge cognitive, usage du numérique, charge cognitive, stratégies d'apprentissage et croyances mnésiques à l'aide d'un questionnaire structuré et de corrélations de Spearman. Les résultats révèlent une charge cognitive modérée (2,80/5) et une corrélation négative inattendue entre usage intensif des outils numériques et charge perçue (ρ = -0,85, p < 0,001). Malgré une forte adhésion aux principes de consolidation de la mémoire (3,50/5), l'engagement dans les pratiques reste moyen (2,91/5), indiquant un décalage entre connaissances et comportements. L'usage des stratégies efficaces comme la répétition espacée est fortement associé à l'engagement (ρ = +0,80). L'ensemble met en évidence la nécessité d'une approche pédagogique neuro-informée.

Mots-clés : Neurosciences, Apprentissage, Engagement, Mémoire, Technologies éducatives

Références

Ahmed, A. A. E., Biswas, A., Bempong-Ahun, N., Perić, I., & O'Flynn, E. P. (2025). Barriers and Enablers to the Production of Open Access Medical Education Platforms : Scoping Review. JMIR Medical Education, 11, e65306. https://doi.org/10.2196/65306
De Bruin, A. B. H. (2016). The potential of neuroscience for health sciences education : Towards convergence of evidence and resisting seductive allure. Advances in Health Sciences Education, 21(5), 983-990. https://doi.org/10.1007/s10459-016-9733-2
Flores, R., Siami Namin, A., Tavakoli, N., Siami-Namini, S., & Jones, K. S. (2021). Using experiential learning to teach and learn digital forensics : Educator and student perspectives. Computers and Education Open, 2, 100045. https://doi.org/10.1016/j.caeo.2021.100045
Gellisch, M., Morosan-Puopolo, G., Brand-Saberi, B., & Schäfer, T. (2024). Adapting to new challenges in medical education : A three-step digitization approach for blended learning. BMC Medical Education, 24(1), 585. https://doi.org/10.1186/s12909-024-05503-1
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging Cognitive Load Theory : The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsc15020203
Hadie, S. N. H., Tan, V. P. S., Omar, N., Nik Mohd Alwi, N. A., Lim, H. L., & Ku Marsilla, K. I. (2021). COVID-19 Disruptions in Health Professional Education : Use of Cognitive Load Theory on Students' Comprehension, Cognitive Load, Engagement, and Motivation. Frontiers in Medicine, 8, 739238. https://doi.org/10.3389/fmed.2021.739238
Hartelt, T., & Martens, H. (2024). Self-regulatory and metacognitive instruction regarding student conceptions : Influence on students' self-efficacy and cognitive load. Frontiers in Psychology, 15, 1450947. https://doi.org/10.3389/fpsyg.2024.1450947
Heiko Dietrich, T. E. (2022). Traditional lectures versus active learning - A false dichotomy? STEM Education, 2(4), 275-292. https://doi.org/10.3934/steem.2022017
Ibrahim, R. K., Al Marar, Y. A., Salman, M., Jehad, S., Hamza, M. G., Abouelnasr, A. S., Abdelaliem, S. M. F., Alahmedi, S. H., & Hendy, A. (2025). Impact of multiple educational technologies on well-being : The mediating role of digital cognitive load. BMC Nursing, 24(1), 1028. https://doi.org/10.1186/s12912-025-03655-z
Jalali, A., Harbi Houssein, K., & Fotsing, S. (2025). Twelve Practical Tips for Integrating AI Into Medical Education : Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains. JMIR Medical Education, 11, e81297. https://doi.org/10.2196/81297
Kim, M., Duncan, C., Yip, S., & Sankey, D. (2025). Beyond the theoretical and pedagogical constraints of cognitive load theory, and towards a new cognitive philosophy in education. Educational Philosophy and Theory, 57(7), 662-673. https://doi.org/10.1080/00131857.2024.2441389
Kyaw, B. M., Saxena, N., Posadzki, P., Vseteckova, J., Nikolaou, C. K., George, P. P., Divakar, U., Masiello, I., Kononowicz, A. A., Zary, N., & Tudor Car, L. (2019). Virtual Reality for Health Professions Education : Systematic Review and Meta-Analysis by the Digital Health Education Collaboration. Journal of Medical Internet Research, 21(1), e12959. https://doi.org/10.2196/12959
Serra, M. J., Kaminske, A. N., Nebel, C., & Coppola, K. M. (2025). The Use of Retrieval Practice in the Health Professions : A State-of-the-Art Review. Behavioral Sciences, 15(7), 974. https://doi.org/10.3390/bs15070974
Simmons, C., Lendrum, R., Perkins, Z., Grier, G., & Marsden, M. (2025). A scoping review of cognitive load assessment tools suitable for clinicians performing REBOA. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 33(1), 121. https://doi.org/10.1186/s13049-025-01408-0
Sriram, A., Ramachandran, K., & Krishnamoorthy, S. (2025). Artificial Intelligence in Medical Education : Transforming Learning and Practice. Cureus. https://doi.org/10.7759/cureus.80852
Tene, T., Vique Lopez, D. F., Valverde Aguirre, P. E., Orna Puente, L. M., & Vacacela Gomez, C. (2024). Virtual reality and augmented reality in medical education : An umbrella review. Frontiers in Digital Health, 6, 1365345. https://doi.org/10.3389/fdgth.2024.1365345
Tokuno, J., Knobovitch, R. M., Botelho, F., Fried, H. B., Carver, T. E., & Fried, G. M. (2025). Immersive Virtual Reality Simulation for Medical Student Procedural Training : Assessment of Cognitive Load and Usability. Surgical Innovation, 32(4), 378-384. https://doi.org/10.1177/15533506251339920
Wei, Y., Soderstrom, N. C., Meade, M. L., & Scott, B. G. (2024). Metacognition About Collaborative Learning : Students' Beliefs Are Inconsistent with Their Learning Preferences. Behavioral Sciences, 14(11), 1104. https://doi.org/10.3390/bs14111104
Wilson, A. B., Bay, B. H., Byram, J. N., Carroll, M. A., Finn, G. M., Hammer, N., Hildebrandt, S., Krebs, C., Wisco, J. J., & Organ, J. M. (2024). Journal recommended guidelines for survey-based research. Anatomical Sciences Education, 17(7), 1389-1391. https://doi.org/10.1002/ase.2499

[1] Ahmed, A. A. E., Biswas, A., Bempong-Ahun, N., Perić, I., & O'Flynn, E. P. (2025). Barriers and Enablers to the Production of Open Access Medical Education Platforms : Scoping Review. JMIR Medical Education, 11, e65306. https://doi.org/10.2196/65306

[2] De Bruin, A. B. H. (2016). The potential of neuroscience for health sciences education : Towards convergence of evidence and resisting seductive allure. Advances in Health Sciences Education, 21(5), 983-990. https://doi.org/10.1007/s10459-016-9733-2

[3] Flores, R., Siami Namin, A., Tavakoli, N., Siami-Namini, S., & Jones, K. S. (2021). Using experiential learning to teach and learn digital forensics : Educator and student perspectives. Computers and Education Open, 2, 100045. https://doi.org/10.1016/j.caeo.2021.100045

[4] Gellisch, M., Morosan-Puopolo, G., Brand-Saberi, B., & Schäfer, T. (2024). Adapting to new challenges in medical education : A three-step digitization approach for blended learning. BMC Medical Education, 24(1), 585. https://doi.org/10.1186/s12909-024-05503-1

[5] Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging Cognitive Load Theory : The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsc15020203

[6] Hadie, S. N. H., Tan, V. P. S., Omar, N., Nik Mohd Alwi, N. A., Lim, H. L., & Ku Marsilla, K. I. (2021). COVID-19 Disruptions in Health Professional Education : Use of Cognitive Load Theory on Students' Comprehension, Cognitive Load, Engagement, and Motivation. Frontiers in Medicine, 8, 739238. https://doi.org/10.3389/fmed.2021.739238

[7] Hartelt, T., & Martens, H. (2024). Self-regulatory and metacognitive instruction regarding student conceptions : Influence on students' self-efficacy and cognitive load. Frontiers in Psychology, 15, 1450947. https://doi.org/10.3389/fpsyg.2024.1450947

[8] Heiko Dietrich, T. E. (2022). Traditional lectures versus active learning - A false dichotomy? STEM Education, 2(4), 275-292. https://doi.org/10.3934/steem.2022017

[9] Ibrahim, R. K., Al Marar, Y. A., Salman, M., Jehad, S., Hamza, M. G., Abouelnasr, A. S., Abdelaliem, S. M. F., Alahmedi, S. H., & Hendy, A. (2025). Impact of multiple educational technologies on well-being : The mediating role of digital cognitive load. BMC Nursing, 24(1), 1028. https://doi.org/10.1186/s12912-025-03655-z

[10] Jalali, A., Harbi Houssein, K., & Fotsing, S. (2025). Twelve Practical Tips for Integrating AI Into Medical Education : Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains. JMIR Medical Education, 11, e81297. https://doi.org/10.2196/81297

[11] Kim, M., Duncan, C., Yip, S., & Sankey, D. (2025). Beyond the theoretical and pedagogical constraints of cognitive load theory, and towards a new cognitive philosophy in education. Educational Philosophy and Theory, 57(7), 662-673. https://doi.org/10.1080/00131857.2024.2441389

[12] Kyaw, B. M., Saxena, N., Posadzki, P., Vseteckova, J., Nikolaou, C. K., George, P. P., Divakar, U., Masiello, I., Kononowicz, A. A., Zary, N., & Tudor Car, L. (2019). Virtual Reality for Health Professions Education : Systematic Review and Meta-Analysis by the Digital Health Education Collaboration. Journal of Medical Internet Research, 21(1), e12959. https://doi.org/10.2196/12959

[13] Serra, M. J., Kaminske, A. N., Nebel, C., & Coppola, K. M. (2025). The Use of Retrieval Practice in the Health Professions : A State-of-the-Art Review. Behavioral Sciences, 15(7), 974. https://doi.org/10.3390/bs15070974

[14] Simmons, C., Lendrum, R., Perkins, Z., Grier, G., & Marsden, M. (2025). A scoping review of cognitive load assessment tools suitable for clinicians performing REBOA. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 33(1), 121. https://doi.org/10.1186/s13049-025-01408-0

[15] Sriram, A., Ramachandran, K., & Krishnamoorthy, S. (2025). Artificial Intelligence in Medical Education : Transforming Learning and Practice. Cureus. https://doi.org/10.7759/cureus.80852

[16] Tene, T., Vique Lopez, D. F., Valverde Aguirre, P. E., Orna Puente, L. M., & Vacacela Gomez, C. (2024). Virtual reality and augmented reality in medical education : An umbrella review. Frontiers in Digital Health, 6, 1365345. https://doi.org/10.3389/fdgth.2024.1365345

[17] Tokuno, J., Knobovitch, R. M., Botelho, F., Fried, H. B., Carver, T. E., & Fried, G. M. (2025). Immersive Virtual Reality Simulation for Medical Student Procedural Training : Assessment of Cognitive Load and Usability. Surgical Innovation, 32(4), 378-384. https://doi.org/10.1177/15533506251339920

[18] Wei, Y., Soderstrom, N. C., Meade, M. L., & Scott, B. G. (2024). Metacognition About Collaborative Learning : Students' Beliefs Are Inconsistent with Their Learning Preferences. Behavioral Sciences, 14(11), 1104. https://doi.org/10.3390/bs14111104

[19] Wilson, A. B., Bay, B. H., Byram, J. N., Carroll, M. A., Finn, G. M., Hammer, N., Hildebrandt, S., Krebs, C., Wisco, J. J., & Organ, J. M. (2024). Journal recommended guidelines for survey-based research. Anatomical Sciences Education, 17(7), 1389-1391. https://doi.org/10.1002/ase.2499

📄 Sciences Methods and Technologies International Journal (SciMeTech)

Références

📄 Sciences Methods and Technologies
International Journal (SciMeTech)