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May 10, 2025
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Abstract

The eCRONY project hypothesises the development and ongoing experimentation of a digital educational tool aimed at enhancing motor sciences teaching. Traditionally, motor skills education relies on demonstration and imitation, often limited to describing visible movements. This approach does not delve into biomechanical causes or proprioceptive sensations as primary learning tools, aspects typically left to practical internships. However, the growing adoption of digital technologies and distance learning highlights the need for an approach that integrates these elements to better support online students with limited guided practice.
eCRONY, structured in four progressive levels (proprioceptive exploration, biomechanical analysis, comparison of causes and effects, and simulation in the absence of terrestrial forces), aims to serve as a valuable educational supplement. The educational pathway promotes a deep understanding of movements, exploring causal forces and enhancing both sensory learning and autonomous feedback abilities. The proposed experimentation aims to assess the effectiveness of this innovative approach compared to traditional methods, hypothesising improvements in proprioceptive awareness, biomechanical understanding, and critical self-assessment abilities among students. If confirmed, the expected outcomes could position eCRONY as a valuable tool for a more scientific and accessible approach to motor skills teaching.

Keywords

Didactics Training Motor Sciences Proprioception Biomechanics

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How to Cite
Fogliata, A., Ambretti, A., & Tardini, S. (2025). eCRONY: hypothesis and experimentation of a new educational tool in motor skills teaching. Journal of E-Learning and Knowledge Society, 21(1), 100-107. https://doi.org/10.20368/1971-8829/1136185

References

  1. Albert, S. T., & Shadmehr, R. (2016). The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System. The Journal of Neuroscience, 36(17), 4832–4845. https://doi.org/10.1523/JNEUROSCI.0159-16.2016
  2. Andrews, S., Huerta Casado, I., Komura, T., Sigal, L., & Mitchell, K. (2016). Real-time physics-based motion capture with sparse sensors. Proceedings of the 13th European Conference on Visual Media Production (CVMP 2016). https://doi.org/10.1145/2998559.2998564
  3. Aoyama, T., & Kohno, Y. (2020). Temporal and quantitative variability in muscle electrical activity decreases as dexterous hand motor skills are learned. PLoS ONE, 15(7), e0236254. https://doi.org/10.1371/journal.pone.0236254
  4. Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall
  5. Batcho, C., Gagné, M., Bouyer, L., Roy, J., & Mercier, C. (2016). Impact of online visual feedback on motor acquisition and retention when learning to reach in a force field. Neuroscience, 336, 93-103. https://doi.org/10.1016/j.neuroscience.2016.09.020
  6. Bennet, S., Wiley, S., Veltkamp, J., & McKeefrey, R. (2006). Sport specificity: How far do you take it? Strength and Conditioning Journal, 28(4), 29–30.
  7. Bernardi, N. F., Darainy, M., & Ostry, D. J. (2015). Somatosensory Contribution to the Initial Stages of Human Motor Learning. The Journal of Neuroscience, 35(42), 14316–14326. https://doi.org/10.1523/JNEUROSCI.1344-15.2015
  8. Bogert, A. J., Geijtenbeek, T., Even-Zohar, O., Steenbrink, F., & Hardin, E. (2013). A real-time system for biomechanical analysis of human movement and muscle function. Medical & Biological Engineering & Computing, 51(10), 1069-1077. https://doi.org/10.1007/s11517-013-1076-z
  9. Borner H., Carboni G., Cheng X., Takagi A., Hirche S., Endo S., Burdet E. (2023). Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception. J Neurophysiol. Feb 1;129(2):494-499. doi: 10.1152/jn.00420.2022. PMID: 36651649; PMCID: PMC994289
  10. Bransford, J., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school: Expanded edition. National Academy Press. https://doi.org/10.17226/9853
  11. Chiviacowsky, S. (2020). The motivational role of feedback in motor learning: Evidence, interpretations, and implications. In A. M. Williams & N. Hodges (Eds.), Skill acquisition in sport: Research, theory and practice (pp. 44-56). Routledge. https://doi.org/10.4324/9780429025112
  12. Cho, W., Barradas, V. R., Schweighofer, N., & Koike, Y. (2022). Design of an isometric end-point force control task for electromyography normalization and muscle synergy extraction from the upper limb without maximum voluntary contraction. Frontiers in Human Neuroscience, 16, 805452. https://doi.org/10.3389/fnhum.2022.805452
  13. Cos, I., Khamassi, M., & Girard, B. (2013). Modelling the learning of biomechanics and visual planning for decision-making of motor actions. Journal of Physiology-Paris, 107(5), 399-408. https://doi.org/10.1016/j.jphysparis.2013.07.004
  14. Cowin, J., Nimphius, S., Fell (2022). A Proposed Framework to Describe Movement Variability within Sporting Tasks: A Scoping Review. Sports Med - Open 8, 85 https://doi.org/10.1186/s40798-022-00473-4
  15. De Bernardi F. (2008). Sincrony: movement education. Red Edizioni.
  16. Dewi, F. I., Wibowo, N. A., Sudjito, D. N., & Rondonuwu, F. S. (2020). The design of one-dimensional motion and two-dimensional motion learning media using digital camera and tracker-based air track. Jurnal Penelitian & Pengembangan Pendidikan Fisika, 6(1), 81–86. https://doi.org/10.21009/1.06107
  17. Dimitriou, M. (2016). Enhanced muscle afferent signals during motor learning in humans. Current Biology, 26(8), 1062-1068. https://doi.org/10.1016/j.cub.2016.02.030
  18. Drews, R., Pacheco M., Bastos, F., & Tani, G. (2021). Effects of normative feedback on motor learning are dependent on the frequency of knowledge of results. Psychology of Sport and Exercise, 53, 101950. https://doi.org/10.1016/J.PSYCHSPORT.2021.101950
  19. Enoka, R. M. (2015). Neuromechanics of Human Movement. Human Kinetics.
  20. Fogliata A., Mazzilli D., Borghini R., Ambretti A., Martinello L. (2022). Performance change due to the optimization of motor programs through a specific sport methodology. Journal of Neurology and Neurophysiology 2022, Vol. 13, Issue 11, 001-004
  21. Guilhem, G., Giroux, C., Couturier, A., & Maffiuletti N. (2014). Validity of trunk extensor and flexor torque measurements using isokinetic dynamometry. Journal of Electromyography and Kinesiology, 24(6), 986-993. https://doi.org/10.1016/j.jelekin.2014.07.006
  22. Heald, J. B., Franklin D. W., & Wolpert D. (2018). Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning. Scientific Reports, 8, 16355. https://doi.org/10.1038/s41598-018-34737-5
  23. Hebert, E.P., Landin D., Solmon M.A. (1996). Practice schedule effects on the performance and learning of low- and high-skilled students: an applied study. Res Q Exerc Sport. 67(1):52-8. doi: 10.1080/02701367.1996.10607925. PMID: 8735994.9
  24. Henriques, D., & Cressman, E. K. (2012). Visuomotor Adaptation and Proprioceptive Recalibration. Journal of Motor Behavior, 44(6), 435–444. https://doi.org/10.1080/00222895.2012.659232
  25. Hodges, N. J., & Franks, I. M. (2002). Learning and Performance in Sports: Research, Theory, and Practice. Routledge.
  26. Johnson, C.A., Reinsdorf, D.S., Reinkensmeyer, D.J., Farrens, A.J. (2023). Robotically quantifying finger and ankle proprioception: Role of range, speed, anticipatory errors, and learning. Annu Int Conf IEEE Eng Med Biol Soc. 1-5. doi: 10.1109/EMBC40787.2023.10340566. PMID: 38083762
  27. Kurita, Y., Sato, J., Tanaka, T., Shinohara, M., & Tsuji, T. (2014). Unloading muscle activation enhances force perception. In Proceedings of the 5th Augmented Human International Conference 4, 4. Association for Computing Machinery. https://doi.org/10.1145/2582051.2582055
  28. Latash, M. L. (2008). Neurophysiological Basis of Movement. Human Kinetics.
  29. Le Naour, T., Hamon, L., & Bresciani, J. (2019). Superimposing 3D Virtual Self + Expert Modeling for Motor Learning: Application to the Throw in American Football. Frontiers in ICT, 6, 16. https://doi.org/10.3389/fict.2019.00016
  30. Leite, C.M.F., Profeta, V.L.D.S., Chaves, S.F.N., Benine, R.P.C., Bottaro, M., Ferreira-Júnior, J.B. (2019). Does exercise-induced muscle damage impair subsequent motor skill learning? Hum Mov Sci. 67:102504. doi: 10.1016/j.humov.2019.102504. Epub 2019 Jul 27. PMID: 31362262
  31. Lieberman, J., & Breazeal, C. (2007). Development of a Wearable Vibrotactile Feedback Suit for Accelerated Human Motor Learning. Proceedings of the IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ROBOT.2007.364093
  32. Lin, Y.N., Hsia, L.H., & Hwang, G.J. (2022). Fostering motor skills in physical education: A mobile technology-supported ICRA flipped learning model. Computers & Education, 174, 104380. https://doi.org/10.1016/j.compedu.2021.104380
  33. Lindberg, S., Hasselhorn, M., & Lehmann, M. (2013). Overregulation in physical education - Teaching behavior effects on self-regulated motor learning. International Journal of Learning and Development, 3(3), 72-88. https://doi.org/10.5296/IJLD.V3I3.3557
  34. Marchal-Crespo, L., López-Olóriz, J., Jaeger L., Riener, R. (2014). Optimizing learning of a locomotor task: amplifying errors as needed. Annu Int Conf IEEE Eng Med Biol Soc. 2014:5304-7. doi: 10.1109/EMBC.2014.6944823. PMID: 25571191
  35. Martay, J.L.B., Martay, H., Carpes F.P. (2021). BodyWorks: interactive interdisciplinary online teaching tools for biomechanics and physiology teaching. Adv Physiol Educ. 45(4):715-719. doi: 10.1152/advan.00069.2021. PMID: 34498937
  36. Mirdamadi, J.L., Block H.J. (2020). Somatosensory changes associated with motor skill learning. J Neurophysiol. 2020 Mar 1;123(3):1052-1062. doi: 10.1152/jn.00497. PMID: 31995429
  37. Porter, J. M., Wu, W. F., & Partridge, J. A. (2010). Focus of attention and verbal instructions: Strategies to enhance performance. Journal of Athletic Training, 45(1), 63-71. DOI: 10.2478/v10237-011-0018-7
  38. Proske, U., & Gandevia, S. (2012). The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiological Reviews, 92(4), 1651-1697. https://dx.doi.org/10.1152/physrev.00048.2011
  39. Schmidt, R. A., & Lee, T. D. (2019). Motor Control and Learning: A Behavioral Emphasis (6th ed.). Human Kinetics.
  40. Schmidt, R. A., & Wrisberg, C. A. (2008). Motor learning and performance: A situation-based learning approach. Human Kinetics.
  41. Seidler, RD, Kwak, Y, Fling, BW, Bernard, JA. (2013) Neurocognitive mechanisms of error-based motor learning. Adv Exp Med Biol. 782:39-60. doi: 10.1007/978-1-4614-5465-6_3. PMID: 23296480; PMCID: PMC3817858.
  42. Sigrist R., Rauter G., Riener R., & Wolf P. (2013). Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin & Review, 20(1), 21-53. https://doi.org/10.3758/s13423-012-0333-8
  43. Simons, D.J., Chabris, C.F. (1999). Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception. 28(9):1059-74. doi: 10.1068/p281059. PMID: 10694957
  44. Simons, D.J., Rensink R.A. (2005). Change blindness: past, present, and future. Trends Cogn Sci. Jan;9(1):16-20. doi: 10.1016/j.tics.2004.11.006. PMID: 15639436
  45. Souissi, M., Ammar, A., Trabelsi, O., Glenn, J., Boukhris, O., Trabelsi, K., Bouaziz, B., Żmijewski, P., Souissi, H., Chikha, A., Driss, T., Chtourou, H. & Hoekelmann, A. (2021). Distance motor learning during the COVID-19 induced confinement: Video feedback with a pedagogical activity improves the snatch technique in young athletes. International Journal of Environmental Research and Public Health, 18(6), 3069 https://dx.doi.org/10.3390/ijerph18063069
  46. Stergiou, N. (2020). Biomechanics and Gait Analysis. Elsevier.
  47. Syaputra, M., & Warni, H. (2023). Penerapan model problem base learning dalam pembelajaran gerak dasar manipulatif. Multilateral: Jurnal Pendidikan Jasmani dan Olahraga, 22(4), 76 https://doi.org/10.20527/multilateral.v22i4.16365
  48. Tang, Z.M., Oouchida, Y., Wang, M., Dou, Z.L., & Izumi, S. (2022). Observing errors in a combination of error and correct models favors observational motor learning. BMC Neuroscience, 23(1) https://doi.org/10.1186/s12868-021-00685-6
  49. Tezel, F., Colak, S., & Ekinci, I. (2024). The relation of motor skills and proprioception in children with learning difficulties. Advances in Applied Science Journal, https://dx.doi.org/10.61186/aassjournal.1238
  50. Toma, S., & Lacquaniti, F. (2016). Mapping muscles activation to force perception during unloading. PLOS ONE, 11(3), e0152552. https://dx.doi.org/10.1371/journal.pone.0152552
  51. Urgesi, C., Moro, V., Candidi, M., & Aglioti, S. (2006). Mapping implied body actions in the human motor system. Journal of Neuroscience, 26(30), 7942-7949. https://dx.doi.org/10.1523/JNEUROSCI.1289-06.2006
  52. Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using artificial intelligence for assistance systems to bring motor learning principles into real world motor tasks. Sensors, 22(7), 2481. https://doi.org/10.3390/s22072481
  53. Willis, J., Gibson, A., Kelly, N., Spina, N., Azordegan, J. M., & Crosswell, L. (2021). Towards faster feedback in higher education through digitally mediated dialogic loops. Australasian Journal of Educational Technology, 37(1), 76–90. https://doi.org/10.14742/AJET.5977
  54. Wong, J.D., Wilson, E.T., Gribble, P.L. (2011) Spatially selective enhancement of proprioceptive acuity following motor learning. J Neurophysiol. 2011 May;105(5):2512-21. doi: 10.1152/jn.00949.2010. Epub Mar 2. PMID: 21368000; PMCID: PMC3094168
  55. Wulf, G., & Lewthwaite, R. (2016). Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychonomic Bulletin & Review, 23, 1382-1414. https://doi.org/10.3758/s13423-015-0999-9
  56. Zhan, X., Chen, C., Niu, L., Du, X., Lei, Y., Dan, R., Wang, Z.W., Liu, P. (2023). Locomotion modulates olfactory learning through proprioception in C. elegans. Nat Commun.;14(1):4534. doi: 10.1038/s41467-023-40286-x. PMID: 37500635; PMCID: PMC10374624 https://www.shanghairanking.com/rankings/grsssd/2024