Main Article Content

Abstract

Smart learning environments (SLE) have been greatly enhanced lately by the adoption of cutting-edge technologies such as Internet-of-Things (IoT), Artificial Intelligence, Augmented Reality, Cloud Computing and Learning Analytics among others. Huge amounts of heterogeneous data are being exchanged between numerous devices, sensors and “things” used by students, educators and educational institutions. This heterogeneity hinders seamless communication among different systems pertaining to SLE. A smart campus is an example of a smart learning environment involving different systems such as smart learning management system, personalized learning, e-learning, assessment, smart classroom and smart library system among others. These systems often need to collaborate to enhance the teaching and learning process. To allow seamless communication among these systems, semantic interoperability has to be tackled by the adoption of a shared common data model. Ontologies are viewed as a potential way to ensure semantic interoperability. Several ontologies exist in the smart learning domain. However, none of them represents a smart learning environment for an IoT-enabled smart campus. This paper presents a semantic model entitled SmartLearningOnto that aims to model different aspects of a smart learning environment in a smart campus. The proposed ontology facilitates exchange of data among several systems in a smart campus by defining the concepts related to smart learning in an appropriate way. Furthermore, it infers new knowledge to enrich the learning experience of learners. SPARQL queries have been used to answer competency questions. Furthermore, several metrics along with expert evaluation have been used to evaluate SmartLearningOnto.

Keywords

Internet-of-Things Smart Learning Environment Smart Learning IoT Semantic Interoperability Ontology

Article Details

How to Cite
Nagowah, S., Ben Sta, H., & Gobin-Rahimbux, B. (2025). Conceptual Knowledge Representation: a semantic model for Smart Learning Environments in an IoT-enabled Smart Campus. Journal of E-Learning and Knowledge Society, 21(2), 44-60. https://doi.org/10.20368/1971-8829/1136004

References

  1. Al-Yahya, M., Al-Faries, A., & George, R. (2013, July). Curonto: An ontological model for curriculum representation. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education (pp. 358-358).
  2. Amrouch, S., & Mostefai, S. (2013). Semantic integration for automatic ontology mapping. Computer Science & Information Technology (CS & IT), Academy & Industry Research Collaboration Center (AIRCC), 387-396.
  3. Bagüés, M. I., Bermúdez, J., Illarramendi, A., Tablado, A., & Goni, A. (2006). Semantic interoperation among data systems at a communication level. In Journal on Data Semantics V (pp. 1-24). Springer Berlin Heidelberg.
  4. Banu, A., Fatima, S. S., & Ur Rahman Khan, K. (2013). Building OWL ontology: LMSO-library management system ontology. In Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India-Volume 3 (pp. 521-530). Springer Berlin Heidelberg.
  5. Bonte, P., Ongenae, F., De Backere, F., Schaballie, J., Arndt, D., Verstichel, S., & De Turck, F. (2017). The MASSIF platform: a modular and semantic platform for the development of flexible IoT services. Knowledge and Information Systems, 51, 89-126.
  6. Carbonaro, A. (2020). Enabling smart learning systems within smart cities using open data. Journal of e-Learning and Knowledge Society, 16(1), 72-77.
  7. Castellanos-Nieves, D., Fernández-Breis, J. T., Valencia-García, R., Martínez-Béjar, R., & Iniesta-Moreno, M. (2011). Semantic Web Technologies for supporting learning assessment. Information sciences, 181(9), 1517-1537.
  8. Çelik, F. & Baturay, M.H. (2024). Technology and innovation in shaping the future of education. Smart Learning Environments, 11(1), 54.
  9. Chatterjee, N., Kaushik, N., Gupta, D., & Bhatia, R. (2018). Ontology merging: A practical perspective. In Information and Communication Technology for Intelligent Systems (ICTIS 2017) 2, (pp. 136-145). Springer International Publishing.
  10. Chituc, C. M. (2020, June). Interoperability Standards in the IoT-enabled Future Learning Environments: An analysis of the challenges for seamless communication. In 2020 13th International Conference on Communications (COMM) (pp. 417-422). IEEE.
  11. Davis, B.G. (2002). Quizzes, tests, and exams. University of California, Berkeley. https://teaching. berkeley. edu/bgd/quizzes. html
  12. De Farias, T. M., Roxin, A., & Nicolle, C. (2016). SWRL rule-selection methodology for ontology interoperability. Data & Knowledge Engineering, 105, 53-72.
  13. Dong, Z. Y., Zhang, Y., Yip, C., Swift, S., & Beswick, K. (2020). Smart campus: definition, framework, technologies, and services. IET Smart Cities, 2(1), 43-54.
  14. Elsaleh, T., Enshaeifar, S., Rezvani, R., Acton, S. T., Janeiko, V., & Bermudez-Edo, M. (2020). IoT-Stream: A lightweight ontology for internet of things data streams and its use with data analytics and event detection services. Sensors, 20(4), 953.
  15. Euzenat, J. (2007, January). Semantic Precision and Recall for Ontology Alignment Evaluation. In IJCAI, 7, 348-353.
  16. Fernández-López, M., Gómez-Pérez, A., & Juristo, N. (1997). Methontology: from ontological art towards ontological engineering.
  17. Fleiner, R., Szász, B., & Micsik, A. (2017). OLOUD-an ontology for linked open university data. Acta Polytechnica Hungarica, 14(4), 63-82.
  18. Ghawi, R., & Cullot, N. (2007, September). Database-to-ontology mapping generation for semantic interoperability. In Third international workshop on database interoperability (InterDB 2007) (Vol. 91).
  19. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., & Wang, Z. (2014). HermiT: an OWL 2 reasoner. Journal of Automated Reasoning, 53, 245-269.
  20. Gruninger, M., & Fox, M. S. (1994). The design and evaluation of ontologies for enterprise engineering. In Workshop on Implemented Ontologies, European Conference on Artificial Intelligence (ECAI).
  21. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2), 199-220.
  22. Guzmán-Arenas, A., & Cuevas, A. D. (2010). Knowledge accumulation through automatic merging of ontologies. Expert Systems with Applications, 37(3), 1991-2005.
  23. Heflin, J., & Hendler, J. (2000). Semantic interoperability on the web. Maryland Univ College Park Dept of Computer Science.
  24. Hossenally, T., Subratty, U.K. & Nagowah, S.D. (2022). Learning Analytics for Smart Classroom System in a University Campus. In Machine Learning Techniques for Smart City Applications: Trends and Solutions, Cham: Springer International Publishing, 171-185.
  25. Iqbal, H.M., Parra-Saldivar, R., Zavala-Yoe, R. and Ramirez-Mendoza, R.A. (2020). Smart educational tools and learning management systems: supportive framework. International journal on interactive design and manufacturing (IJIDeM), 14(4), 1179-1193.
  26. Izza, S. (2009). Integration of industrial information systems: from syntactic to semantic integration approaches. Enterprise Information Systems, 3(1), 1-57.
  27. Jiménez-Ruiz, E., & Cuenca Grau, B. (2011). Logmap: Logic-based and scalable ontology matching. In The Semantic Web–ISWC 2011: 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I 10 (pp. 273-288). Springer Berlin Heidelberg.
  28. Kavashev, Z. (2024). Heutagogical design principles for online learning: a scoping review. American Journal of Distance Education, 1-18.
  29. Katsumi, M., & Grüninger, M. (2016). What is ontology reuse?. In FOIS, 9–22.
  30. Khdour, T. (2020). A semantic assessment framework for e-learning systems. International Journal of Knowledge and Learning, 13(2), 110-122.
  31. Kiljander, J., D’elia, A., Morandi, F., Hyttinen, P., Takalo-Mattila, J., Ylisaukko-Oja, A., & Cinotti, T. S. (2014). Semantic interoperability architecture for pervasive computing and internet of things. IEEE access, 2, 856-873.
  32. Kultsova, M., Anikin, A., Zhukova, I., & Dvoryankin, A. (2015). Ontology-based learning content management system in programming languages domain. Communications in Computer and Information Science, 535, 767-777.
  33. Litherland, K., Carmichael, P., & Martínez-García, A. (2013). Ontology-based e-assessment for accounting education. Accounting Education, 22(5), 498-501.
  34. Maria, K., Vasilis, E., & Grigoris, A. (2012). S-CRETA: Smart classroom real-time assistance. In Ambient Intelligence-Software and Applications: 3rd International Symposium on Ambient Intelligence (ISAmI 2012) (pp. 67-74). Springer Berlin Heidelberg.
  35. Martinez, G., Perry, J. and Biryukov, V. (2024, May). Automated IoT-Based Performance Assessments Through Activity Recognition and Semantic Evaluation in Smart Learning Environments. In 2024 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-6). IEEE.
  36. McDaniel, M., Storey, V. C., & Sugumaran, V. (2018). Assessing the quality of domain ontologies: Metrics and an automated ranking system. Data & Knowledge Engineering, 115, 32-47.
  37. Muhamad, W., Kurniawan, N. B., & Yazid, S. (2017, October). Smart campus features, technologies, and applications: A systematic literature review. In 2017 International conference on information technology systems and innovation (ICITSI) (pp. 384-391). IEEE.
  38. Nagowah, S. D., Ben Sta, H., & Gobin-Rahimbux, B. A. (2021, December). An Ontology for an IoT-enabled Smart Library in a University Campus. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (pp. 1952-1957). IEEE.
  39. Nagowah, S. D., Ben Sta, H., & Gobin-Rahimbux, B. A. (2019, December). An ontology for an IoT-enabled smart classroom in a university campus. In 2019 international conference on computational intelligence and knowledge economy (ICCIKE) (pp. 626-631). IEEE.
  40. Nagowah, S.D. & Nagowah, L. (2009). Assessment strategies to enhance students’ success. In Proceedings of the IASK International Conference “Teaching and Learning”, Porto, Portugal (pp. 7-9).
  41. Noy, N., & McGuinness, D. L. (2001). Ontology development 101. Knowledge Systems Laboratory, Stanford University, 2001.
  42. O'Connor, M. J., & Das, A. K. (2009, October). SQWRL: a query language for OWL. In OWLED (Vol. 529, No. 2009, pp. 1-8).
  43. Ouf, S., Abd Ellatif, M., Salama, S. E., & Helmy, Y. (2017). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior, 72, 796-818.
  44. Rhee, S. K., Lee, J., Park, M. W., Szymczak, M., Ganzha, M., & Paprzycki, M. (2009). Measuring semantic closeness of ontologically demarcated resources. Fundamenta Informaticae, 96(4), 395-418.
  45. Scrocca, M., Baroni, I. & Celino, I. (2021). Urban IoT ontologies for sharing and electric mobility. Semantic Web, (Preprint), 1-22.
  46. Sheeba, T., & Krishnan, R. (2015). Semantic retrieval based on SPARQL and SWRL for learner profile. Int J Appl Eng Res, 10, 34549-54.
  47. Shemshack, A. & Spector, J.M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1), 1-20.
  48. Sherimon, V., Sherimon, P. C., Mathew, R., Kumar, S. M., Nair, R. V., Shaikh, K., ... & Al Shuaily, H. S. (2020). Covid-19 ontology engineering-knowledge modeling of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). International Journal of Advanced Computer Science and Applications, 11(11).
  49. Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A., & Katz, Y. (2007). Pellet: A practical owl-dl reasoner. Journal of Web Semantics, 5(2), 51-53.
  50. Shi, Y., Qin, W., Suo, Y., & Xiao, X. (2010). Smart classroom: Bringing pervasive computing into distance learning. Handbook of ambient intelligence and smart environments, 881-910.
  51. Shvaiko, P. & Euzenat, J. (2008). Ten challenges for ontology matching. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 1164-1182). Berlin, Heidelberg: Springer Berlin Heidelberg.
  52. Suárez-Figueroa, M. C., Gómez-Pérez, A., & Fernández-López, M. (2012). The NeOn methodology for ontology engineering. In Ontology engineering in a networked world (pp. 9-34). Berlin, Heidelberg: Springer Berlin Heidelberg.
  53. Sungkur, Y. G., Ozeer, A. M., & Nagowah, S. D. (2021). Development of an IoT-enabled smart library system for a university campus. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 13(1), 27-36.
  54. Tabuenca, B., Uche-Soria, M., Greller, W., Hernández-Leo, D., Balcells-Falgueras, P., Gloor, P. and Garbajosa, J. (2024). Greening smart learning environments with Artificial Intelligence of Things. Internet of Things, 25, 101051.
  55. Uschold, M., & King, M. (1995). Towards a methodology for building ontologies (pp. 1-13). Edinburgh: Artificial Intelligence Applications Institute, University of Edinburgh.
  56. Uskov, V. L., Bakken, J. P., & Pandey, A. (2015). The ontology of next generation smart classrooms. In Smart education and smart e-learning (pp. 3-14). Springer International Publishing.
  57. Yu, Z., Nakamura, Y., Jang, S., Kajita, S., & Mase, K. (2007). Ontology-based semantic recommendation for context-aware e-learning. In Ubiquitous Intelligence and Computing: 4th International Conference, UIC 2007, Hong Kong, China, July 11-13, 2007. Proceedings 4 (pp. 898-907). Springer Berlin Heidelberg.
  58. Zhang, Y., Luo, X., Li, J., & Buis, J. J. (2013). A semantic representation model for design rationale of products. Advanced Engineering Informatics, 27(1), 13-26.