OntoStrength: An Ontology for Psychomotor Strength Development

Laurentiu-Marian Neagu, Eric Rigaud, Vincent Guarnieri, Emanuel Ioan Radu, Sébastien Travadel, Mihai Dascalu and Razvan Rughinis

pp.  101 - 118, download






An ontology is a formal, explicit description of concepts and relations from a domain while considering underlying properties, restrictions, and instances.With the advancement of the Semantic Web, the rise of the Educational Semantic Web, and the lack of uniformity between approaches for knowledge representation, ontologies are becoming more and more popular, including adaptive learning environments such as the Intelligent Tutoring Systems (ITSs). OntoStrength is an ontology developed to support the Selfit ITS, a platform that aims to improve the fundamental human psychomotor skills and, more specifically, bio-motor strength abilities. The goal of Selfit is to prevent the negative consequences of a sedentary lifestyle and accidents involving inadequate strength skills. Most ontologies in the sports domain support the development of digital solutions for sports performance and data collected during competitions. In contrast, OntoStrength’s goal is to contribute to the development of digital solutions dedicated to bio-motor strength ability analysis. OntoStrength considers other bio-motor skills like speed, endurance, or flexibility, as well as other activities like muscle analysis, movement patterns, or training load management, to support sport, professional and daily-life activities. OntoStrength enables the personalization of strength development programs in the Selfit ITS by providing a comprehensive data layer for its student, domain, and tutoring models.


Keywords: Intelligent Tutoring SystemOntology, Strength skills, Strength Development, Personalization, Selfit 





1. Fenza, G., Orciuoli, F.: Building pedagogical models by formal concepts analysis. Proc 13th Int. Conf. Intelligent Tutoring Systems (ITS), 9684, 144-153 (2016) 

2. Carbonell, J.R.: AI in CAI: An Artificial-Intelligence Approach to ComputerAssisted Instruction. IEEE Transactions on Man-Machine Systems, MMS-11(4), 190-202 (1970) https://doi.org/10.1109/TMMS.1970.299942
3. Sottilare, R.A., Graesser, A., Hu, X., Olney, A., Nye, B., Sinatra, A.M.: Introduction to Domain Modeling and GIFT. Design Recommendations for Intelligent Tutoring Systems, Vol. 4 - Domain Modeling, pp. 1–15. US Army Research Laboratory, Orlando, Florida (2016)
4. Zouaq, A., Nkambou, R.: A survey of domain ontology engineering: Methods and tools. Advances in Intelligent Tutoring Systems, Springer-Verlag, Berlin Heidelberg, 103–119 (2010) https://doi.org/10.1007/978-3-642-14363-2_6
5. LaViola, J., Williamson, B., Brooks, C., Veazanchin, S., Garrity, P., Sottilare, R.: Using Augmented Reality to Tutor Military Tasks in the Wild. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), 1–10 (2015)
6. Polson, M.C., Richardson, J.J.: Foundations of Intelligent Tutoring Systems. Lawrence Erlbaum Associates, Hillsdale, (1988)
7. Aroyo, L., Dicheva, D.: The New Challenges for E-learning: The Educational Semantic Web. Educational Technology & Society, 7(4), 59-69 (2004)
8. Nkambou, R., Bourdeau, J., Mizoguchi, R.: Introduction: What Are Intelligent Tutoring Systems, and Why This Book? In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, pp. 1–12. Springer, Berlin, Germany (2010) https://doi.org/10.1007/978-3-642-14363-2_1
9. Fensel, D.: Ontologies. Springer, Heidelberg, 11-18 (2001)

10. Noy, N.F., McGuinness, D.L.: Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory, Stanford, CA, USA (2001)
11. Neagu, L.-M., Rigaud, E., Guarnieri, V., Travadel, S., Dascalu, M.: Selfit – An Intelligent Tutoring System for Psychomotor Development. International Conference on Intelligent Tutoring Systems, pp. 282–286. Springer, Athens, Greece (Online) (2021)

12. Devedzic, V., Jerinic, L., Radovic, D.: The GET-BITS Model of Intelligent Tutoring Systems. Journal of Interactive Learning Research, 11(3), 411-434 (2000)
13. Ali Ahmed, G.H., Kovacs, L.: Ontology Domain Model for E-Tutoring System. Software Engineering & Intelligent Systems, 5(1) (2020)
14. Dermeval, D., Albuquerque, J., Bittencourt, I.I., Isotani, S., Silva, A.P., Vassileva, J.: GaTO: An Ontological Model to Apply Gamification in Intelligent Tutoring Systems. Frontiers Artificial Intelligence, 2 (2019) https://doi.org/10.3389/frai.2019.00013
15. Fernández-López, M., Gómez-Pérez, A., Juristo, N.: Methontology: from ontological art towards ontological engineering. (1997)
16. Panagiotopoulos, I., Kalou, A., Pierrakeas, C., Kameas, A.: An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning. IFIP International Conference on Artificial Intelligence Applications and Innovations, 296-305 (2012) https://doi.org/10.1007/978-3-642-33409-2_31
17. Brawner, K., Hoffman, M., Nye, B.: Architecture and Ontology in the Generalized Intelligent Framework for Tutoring: 2018 Update. In: 7th Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium, pp. 11. US Army Combat Capabilities Development Command–Soldier Center (2019)
18. Ramkumar, S., Poorna, B.: Development of Ontology for Sports Domain. International Journal for Research in Applied Science & Engineering Technology, 5 (2017)

19. Diaz-Rodriguez, N., Wikstrom, R., Lilius, J., Cuellar, M.P., Flores, M.D.C.: Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors. In: Ubiquitous Computing and Ambient Intelligence. Context Awareness and Context-Driven Interaction (UCAmI 2013), pp. 254–261. Springer, Carrillo, Costa Rica (2013)

20. Clement B., Roy D., Oudeyer P-Y., M, L.: Multi-Armed Bandits for Intelligent Tutoring Systems. Journal of Educational Data Mining (JEDM), 7 (2015) 

21. Biørn-Hansen, A., Majchrzak, T.A., Grønli, T.-M.: Progressive web apps: The possible web-native unifier for smobile development. Proceedings of the 13th International Conference on Web Information Systems and Technologies, 1, 344–351 (2017)

22. Neagu, L.-M., Rigaud, E., Travadel, S., Dascalu, M., Rughinis, R.-V.: Intelligent Tutoring Systems for Psychomotor Training – A Systematic Literature Review. In: 16h Int. Conf. on Intelligent Tutoring Systems (ITS 2020). Springer, Online (2020) https://doi.org/10.1007/978-3-030-49663-0_40
23. Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF. W3C Recommendation (2008). World Wide Web Consortium (2017)
24. Bompa, T., & Buzzichelli, C.: Periodization: Theory and Methodology of Training. Human Kinetics Publishers, Sixth édition (2017)
25. Goodway, J.D., Ozmun, J.C., Gallahue, D.L.: Understanding Motor Development: Infants, Children, Adolescents, Adults. Jones & Bartlett Learning, Burlington, MA, USA (2019)
26. Issurin, V.B.: Building the Modern Athlete: Scientific Advancements and Training Innovations. Muskegon Heights: Ultimate Training Concepts, (2015)
27. Pitchers, G., K., E.-S.: Considerations for coaches training female athletes. Professional strength & conditioning, Training Female Athletes (2019)



back to Table of Contents