IMPACT PERCEPTION IN EDUCATIONAL ROBOTS IN THE DEVELOPMENT OF PHYSIC EDUCATION
Main Article Content
Keywords
Educational robots, learning activities, cognitive adaptation, curricular adaptation, technological adaptation, physical education, artificial intelligence
Abstract
Educational robots are collaborative robots designed to work with students and teachers to enhance learning. They can develop skills, such as problem solving, collaboration, interactivity and creativity. This study was applied to 72 physical education teachers in the department of Sucre, Colombia and 118 scientific articles. By working with these robots, students can learn teamwork, innovation and effective communication. The aim of this scientific article is to analyze bibliometric the research concerning educational robots in the development of physical education. The methodology used for the systematic analysis through bibliometric review and digital search of experiences on the application of educational robots in physical education, allowing to implement the system of analysis of authors, journals and contributions. The methodology promotes theoretical and practical contributions that demonstrate the need for the implementation of educational robots to achieve better learning outcomes in environments such as physical education. It is a current topic, led by authors and journals from developed countries. The main results show the need to update classroom plans, curricula, and educational systems for the development of learning based on these new technologies. In addition, smart classrooms are a reality and educational robots are an example of this.
References
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8. Djuric, A., Rickli, J. L., Jovanovic, V. M., & Foster, D. (2017). Hands-on learning environment and educational curriculum on collaborative robotics. Paper presented at the ASEE Annual Conference and Exposition, Conference Proceedings, 2017-June.
9. El Zaatari, S., Li, W., & Usman, Z. (2021). Ring gaussian mixture modelling and regression for collaborative robots. Robotics and Autonomous Systems, 145 doi:10.1016/j.robot.2021.103864.
10. Gao, C., & Cheng, S. (2023). The deep learning model for physical intelligence education and its functional realization path. Soft Computing, 27(15), 10827-10838. doi:10.1007/s00500-023-07835-4.
11. Heredia-Marin, I. B., Prado-Chapa, D., Segovia-Gamez, S., Elizondo-Valladares, M., Garza-Martinez, A. L., & Vazquez-Hurtado, C. (2022). Work in progress: Design of a human-machine interface supplied by robots as technology for project-based learning. Paper presented at the EDUNINE 2022 - 6th IEEE World Engineering Education Conference: Rethinking Engineering Education After COVID-19: A Path to the New Normal, Proceedings, doi:10.1109/EDUNINE53672.2022.9782368.
12. Higashi, R., Harpstead, E., Solyst, J., Kemper, J., Odili Uchidiuno, J., & Hammer, J. (2021). The design of co-robotic games for computer science education. Paper presented at the CHI PLAY 2021 - Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play, 111-116. doi:10.1145/3450337.3483472.
13. Islas, L. R., Paredes Orta, C. A., & Chavez, F. M. (2021). Forward and inverse kinematics control of a collaborative 6-DOF robot for an augmented reality educational system. Paper presented at the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021, doi:10.1109/ICECCME52200.2021.9590903.
14. Kommula, V. P., Uziak, J., Oladiran, M. T., & Tjiparuro, Z. (2023). Challenges and opportunities of using iot in engineering education: The african perspective. Advancements in artificial intelligence, blockchain technology, and IoT in higher education: Mitigating the impact of COVID-19 (pp. 229-259).
15. Li, F., & Wang, C. (2023). Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology. Journal of Cloud Computing, 12(1) doi:10.1186/s13677-023-00418-6.
16. López Belmonte, J., Pozo Sánchez, S., Fuentes Cabrera, A., & Vicente Bújez, M. R. (2020). Escenarios innovadores en Educación Física: El trabajo de la expresión corporal y musical mediado por la robótica (Innovative scenarios in Physical Education: body and musical expression work mediated by robotics). Retos, 38, 567–575. https://doi.org/10.47197/retos.v38i38.77372
17. Matthew, J., Pagea, J.E., Mckenziea, P.M., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., ... Moher, D. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. doi.org/10.1016/j.recesp.2021.06.016. Merkouris, A., Garneli, V., & Chorianopoulos, K. (2021). Programming human-robot interactions for teaching robotics within a collaborative learning open space: Robots playing capture the flag game: Programming human-robot interactions within a collaborative learning open space. Paper presented at the ACM International Conference Proceeding Series, doi:10.1145/3489410.3489422.
18. Michaelis, J. E., Siebert-Evenstone, A., Shaffer, D. W., & Mutlu, B. (2020). Collaborative or simply uncaged? understanding human-cobot interactions in automation. Paper presented at the Conference on Human Factors in Computing Systems - Proceedings, doi:10.1145/3313831.3376547.
19. PISA (2022). El Programa para la Evaluación Internacional de Alumnos. Ministerio de educación Nacional de Colombia. https://www.mineducacion.gov.co/portal/micrositios-preescolar-basica-y-media/PISA/Generalidades/363487:Que-es-la-prueba-PISA. Consultado el 16 de julio de 2023 a las 6:12pm.
20. Sanabria-Navarro, J., Silveira-Pérez, Y., Pérez-Bravo, D., & de-Jesús-Cortina-Núñez, M. (2023). Incidences of artificial intelligence in contemporary education. [Incidencias de la inteligencia artificial en la educación contemporánea]. Comunicar, 77. https://doi.org/10.3916/C77-2023-08.
21. Sun, D., Zhang, P., & Chen, M. (2023). A versatile interaction framework for robot programming based on hand gestures and poses. Robotics and Computer-Integrated Manufacturing, 84 doi:10.1016/j.rcim.2023.102596.
22. Tabuenca, B., Moreno-Sancho, J. -., Arquero-Gallego, J., Greller, W., & Hernández-Leo, D. (2023). Generating an environmental awareness system for learning using IoT technology. Internet of Things (Netherlands), 22 doi:10.1016/j.iot.2023.100756.
23. Taesi, C., Aggogeri, F., & Pellegrini, N. (2023). COBOT Applications—Recent advances and challenges. Robotics, 12(3) doi:10.3390/robotics12030079.
24. Taplin, R. (2023). Artificial intelligence, intellectual property, cyber risk and robotics: An overview. Artificial intelligence, intellectual property, cyber risk and robotics: A new digital age (pp. 1-13) doi:10.4324/9780367857561-1.
25. Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational robots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712. doi:10.1007/s40593-016-0095-y.
26. Tóth, A., Nagy, L., Kennedy, R., Bohuš, B., Abonyi, J., & Ruppert, T. (2023). The human-centric industry 5.0 collaboration architecture. MethodsX, 11 doi:10.1016/j.mex.2023.102260.
27. Xu, F., & Xia, Y. (2023). Development of speech recognition system for remote vocal music teaching based on markov model. Soft Computing, 27(14), 10237-10248. doi:10.1007/s00500-023-08277-8.
28. Zhang, W., Joseph, J., Chen, Q., Koz, C., Xie, L., Regmi, A., . . . Kara, L. B. (2024). A data augmentation method for data-driven component segmentation of engineering drawings. Journal of Computing and Information Science in Engineering, 24(1) doi:10.1115/1.4062233.
29. Blindheim, K., Solberg, M., Hameed, I.A. and Alnes, R.E (2023). Promoting activity in long-term care facilities with the social robot Pepper: a pilot study. Informatics for Health and Social Care, 48(2), pp. 181–195
30. Kalaitzidou, M.; Pachidis, T.P. (2023). Recent Robots in STEAM Education. Education. Sciences. 13, 272. https://doi.org/10.3390/educsci13030272
2. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics,11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007.
3. Campos, V. M., & Muñoz, F. J. R. (2023). Design and piloting of a proposal for intervention with educational robotics for the development of lexical relationships in early childhood education. Smart Learning Environments, 10(1) doi:10.1186/s40561-023-00226-0.
4. Canfield, S. L., Owens, J. S., & Zuccaro, S. G. (2021). Zero moment control for lead-through teach programming and process monitoring of a collaborative welding robot. Journal of Mechanisms and Robotics, 13(3) doi:10.1115/1.4050102.
5. Casey, E., Jocz, J., Peterson, K. A., Pfeif, D., & Soden, C. (2023). Motivating youth to learn STEM through a gender inclusive digital forensic science program. Smart Learning Environments, 10(1) doi:10.1186/s40561-022-00213-x.
6. Constantinescu, M., Uszkai, R., & Vicǎ, C. (2023). Robo-education and the pedagogical divide doi:10.3233/FAIA220616
7. Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1) doi:10.1186/s40561-023-00231-3.
8. Djuric, A., Rickli, J. L., Jovanovic, V. M., & Foster, D. (2017). Hands-on learning environment and educational curriculum on collaborative robotics. Paper presented at the ASEE Annual Conference and Exposition, Conference Proceedings, 2017-June.
9. El Zaatari, S., Li, W., & Usman, Z. (2021). Ring gaussian mixture modelling and regression for collaborative robots. Robotics and Autonomous Systems, 145 doi:10.1016/j.robot.2021.103864.
10. Gao, C., & Cheng, S. (2023). The deep learning model for physical intelligence education and its functional realization path. Soft Computing, 27(15), 10827-10838. doi:10.1007/s00500-023-07835-4.
11. Heredia-Marin, I. B., Prado-Chapa, D., Segovia-Gamez, S., Elizondo-Valladares, M., Garza-Martinez, A. L., & Vazquez-Hurtado, C. (2022). Work in progress: Design of a human-machine interface supplied by robots as technology for project-based learning. Paper presented at the EDUNINE 2022 - 6th IEEE World Engineering Education Conference: Rethinking Engineering Education After COVID-19: A Path to the New Normal, Proceedings, doi:10.1109/EDUNINE53672.2022.9782368.
12. Higashi, R., Harpstead, E., Solyst, J., Kemper, J., Odili Uchidiuno, J., & Hammer, J. (2021). The design of co-robotic games for computer science education. Paper presented at the CHI PLAY 2021 - Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play, 111-116. doi:10.1145/3450337.3483472.
13. Islas, L. R., Paredes Orta, C. A., & Chavez, F. M. (2021). Forward and inverse kinematics control of a collaborative 6-DOF robot for an augmented reality educational system. Paper presented at the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021, doi:10.1109/ICECCME52200.2021.9590903.
14. Kommula, V. P., Uziak, J., Oladiran, M. T., & Tjiparuro, Z. (2023). Challenges and opportunities of using iot in engineering education: The african perspective. Advancements in artificial intelligence, blockchain technology, and IoT in higher education: Mitigating the impact of COVID-19 (pp. 229-259).
15. Li, F., & Wang, C. (2023). Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology. Journal of Cloud Computing, 12(1) doi:10.1186/s13677-023-00418-6.
16. López Belmonte, J., Pozo Sánchez, S., Fuentes Cabrera, A., & Vicente Bújez, M. R. (2020). Escenarios innovadores en Educación Física: El trabajo de la expresión corporal y musical mediado por la robótica (Innovative scenarios in Physical Education: body and musical expression work mediated by robotics). Retos, 38, 567–575. https://doi.org/10.47197/retos.v38i38.77372
17. Matthew, J., Pagea, J.E., Mckenziea, P.M., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., ... Moher, D. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. doi.org/10.1016/j.recesp.2021.06.016. Merkouris, A., Garneli, V., & Chorianopoulos, K. (2021). Programming human-robot interactions for teaching robotics within a collaborative learning open space: Robots playing capture the flag game: Programming human-robot interactions within a collaborative learning open space. Paper presented at the ACM International Conference Proceeding Series, doi:10.1145/3489410.3489422.
18. Michaelis, J. E., Siebert-Evenstone, A., Shaffer, D. W., & Mutlu, B. (2020). Collaborative or simply uncaged? understanding human-cobot interactions in automation. Paper presented at the Conference on Human Factors in Computing Systems - Proceedings, doi:10.1145/3313831.3376547.
19. PISA (2022). El Programa para la Evaluación Internacional de Alumnos. Ministerio de educación Nacional de Colombia. https://www.mineducacion.gov.co/portal/micrositios-preescolar-basica-y-media/PISA/Generalidades/363487:Que-es-la-prueba-PISA. Consultado el 16 de julio de 2023 a las 6:12pm.
20. Sanabria-Navarro, J., Silveira-Pérez, Y., Pérez-Bravo, D., & de-Jesús-Cortina-Núñez, M. (2023). Incidences of artificial intelligence in contemporary education. [Incidencias de la inteligencia artificial en la educación contemporánea]. Comunicar, 77. https://doi.org/10.3916/C77-2023-08.
21. Sun, D., Zhang, P., & Chen, M. (2023). A versatile interaction framework for robot programming based on hand gestures and poses. Robotics and Computer-Integrated Manufacturing, 84 doi:10.1016/j.rcim.2023.102596.
22. Tabuenca, B., Moreno-Sancho, J. -., Arquero-Gallego, J., Greller, W., & Hernández-Leo, D. (2023). Generating an environmental awareness system for learning using IoT technology. Internet of Things (Netherlands), 22 doi:10.1016/j.iot.2023.100756.
23. Taesi, C., Aggogeri, F., & Pellegrini, N. (2023). COBOT Applications—Recent advances and challenges. Robotics, 12(3) doi:10.3390/robotics12030079.
24. Taplin, R. (2023). Artificial intelligence, intellectual property, cyber risk and robotics: An overview. Artificial intelligence, intellectual property, cyber risk and robotics: A new digital age (pp. 1-13) doi:10.4324/9780367857561-1.
25. Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational robots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712. doi:10.1007/s40593-016-0095-y.
26. Tóth, A., Nagy, L., Kennedy, R., Bohuš, B., Abonyi, J., & Ruppert, T. (2023). The human-centric industry 5.0 collaboration architecture. MethodsX, 11 doi:10.1016/j.mex.2023.102260.
27. Xu, F., & Xia, Y. (2023). Development of speech recognition system for remote vocal music teaching based on markov model. Soft Computing, 27(14), 10237-10248. doi:10.1007/s00500-023-08277-8.
28. Zhang, W., Joseph, J., Chen, Q., Koz, C., Xie, L., Regmi, A., . . . Kara, L. B. (2024). A data augmentation method for data-driven component segmentation of engineering drawings. Journal of Computing and Information Science in Engineering, 24(1) doi:10.1115/1.4062233.
29. Blindheim, K., Solberg, M., Hameed, I.A. and Alnes, R.E (2023). Promoting activity in long-term care facilities with the social robot Pepper: a pilot study. Informatics for Health and Social Care, 48(2), pp. 181–195
30. Kalaitzidou, M.; Pachidis, T.P. (2023). Recent Robots in STEAM Education. Education. Sciences. 13, 272. https://doi.org/10.3390/educsci13030272
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Published
Jun 6, 2023
Article Details
How to Cite
Claudia Patricia Rojas Martínez, Yahilina Silveira Pérez, & William Niebles Nuñez. (2023). IMPACT PERCEPTION IN EDUCATIONAL ROBOTS IN THE DEVELOPMENT OF PHYSIC EDUCATION. Journal of Population Therapeutics and Clinical Pharmacology, 30(14), 482-496. https://doi.org/10.53555/jptcp.v30i14.6224
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Author Biographies
Claudia Patricia Rojas Martínez
Universidad de Sucre (Colombia)
Yahilina Silveira Pérez
Universidad de Sucre (Colombia)
William Niebles Nuñez
Universidad de Sucre (Colombia)
How to Cite
Claudia Patricia Rojas Martínez, Yahilina Silveira Pérez, & William Niebles Nuñez. (2023). IMPACT PERCEPTION IN EDUCATIONAL ROBOTS IN THE DEVELOPMENT OF PHYSIC EDUCATION. Journal of Population Therapeutics and Clinical Pharmacology, 30(14), 482-496. https://doi.org/10.53555/jptcp.v30i14.6224