Ontology Technique and Meaningful Learning Support Environments

  • Jingyun Wang


In this chapter, we present two ontology-driven learning support systems, which intend to provide meaningful learning environment: a customizable language learning support system (CLLSS) and a visualization learning support system for e-book users (VSSE). CLLSS was built to provide an interface for the learning objects arrangement which displays the visual representation of knowledge points and their relations. The intention underlying the development of CLLSS is to encourage instructors to orient their teaching materials to specific knowledge points and even directly to relations between knowledge points. With these orientations, CLLSS is able to provide an environment in which learners can readily distinguish between related knowledge points. In the other hand, VSSE is designed and developed to help e-book learners to effectively construct their knowledge frameworks. Making use of e-book logs, VSSE supports not only meaningful receptive learning but also meaningful discovery learning. In other words, two learning modes are provided in VSSE: (a) reception comparison mode, in which learners are provided directly with complete versions of relation maps; and (b) cache-cache comparison mode, where all information concerning relations is hidden at the first stage of learning, and in the second stage learners are encouraged to actively create them.


Meaningful learning Ontology Digital textbooks Discovery learning Visualization 



The research is supported by KAKENHI Grant Number 17K17936, the Research and Development on Fundamental Utilization Technologies for Social Big Data (No. 178A03), and the Commissioned Research of National Institute of Information and Communications Technology, Japan.


  1. Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18.CrossRefGoogle Scholar
  2. Ausubel, D. P. (1963). The psychology of meaningful verbal learning. New York, NY: Grune and Stratton.Google Scholar
  3. Ausubel, D. P. (1968). Educational psychology: A cognitive view. New York, NY: Holt.Google Scholar
  4. Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational psychology: A cognitive view (2nd ed.). New York, NY: Holt, Rinehart and Winston.Google Scholar
  5. Bransford, J., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy.Google Scholar
  6. Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31(1), 21–32.Google Scholar
  7. Bruner, J. S. (2009). The process of education. Cambridge: Harvard University Press.Google Scholar
  8. Chu, K. K., Lee, C. I., & Tsai, R. S. (2011). Ontology technology to assist learners’ navigation in the concept map learning system. Expert Systems with Applications, 38, 11293–11299.CrossRefGoogle Scholar
  9. Dicheva, D., & Dichev, C. (2006). TM4L: creating and browsing educational topic maps. British Journal of Educational Technology, 37(3), 391–404.CrossRefGoogle Scholar
  10. Gomez-Albarran, M., & Jimenez-Diaz, G. (2009). Recommendation and students’ authoring in repositories of learning objects: A case-based reasoning approach. International Journal of Emerging Technologies in Learning (IJET), 4, 35–40.CrossRefGoogle Scholar
  11. Gruber, T. R. (1993). A translation approach to portable ontologies. Knowledge Acquisition, 5(2), 199–220.CrossRefGoogle Scholar
  12. Hayashi, Y., Bourdeau, J., & Mizoguchi, R. (2009). Using ontological engineering to organize learning/instructional theories and build a theory-aware authoring system. International Journal of Artificial Intelligence in Education, 19(2), 211–252.Google Scholar
  13. Holsapple, C. W., & Joshi, K. D. (2004). A formal knowledge management ontology: Conduct, activities, resources and influences. Journal of the American Society for Information Science and Technology, 55(7), 593–612.CrossRefGoogle Scholar
  14. Kirschner, P. A., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.CrossRefGoogle Scholar
  15. Lee, J. H., & Segev, A. (2012). Knowledge maps for e-learning. Computers & Education, 59(2), 353–364.CrossRefGoogle Scholar
  16. Lim, K. Y., Lee, H. W., & Grabowski, B. (2009). Does concept-mapping strategy work for everyone? The levels of generativity and learners’ self-regulated learning skills. British Journal of Educational Technology, 40(4), 606–618.CrossRefGoogle Scholar
  17. Mansur, A. B. F., & Yusof, N. (2013). Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning. Computers & Education, 63, 73–86.CrossRefGoogle Scholar
  18. Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59(1), 14–19.CrossRefGoogle Scholar
  19. Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct them. Technical report IHMC CmapTools 2006-01 Rev 01-2008. Florida Institute for Human and Machine Cognition. Available at Scholar
  20. Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Development, 32(1), 1–25.Google Scholar
  21. O’Donnell, A. M., Dansereau, D. F., & Hall, R. H. (2002). Knowledge maps as scaffolds for cognitive processing. Educational Psychology Review, 14(1), 71–86.CrossRefGoogle Scholar
  22. Oltramari, A., Gangemi, A., Guarino, N., & Masolo, C. (2002). Restructuring WordNet’s top-level: The OntoClean approach. In Workshop Proceedings of OntoLex’2, Ontologies and Lexical Knowledge Bases (LREC2002), Spain (pp. 17–26).Google Scholar
  23. Sosnovsky, S., & Gavrilova, T. (2006). Development of educational ontology for C-programming. International Journal Information, Theories & Applications, 13(4), 303–307.Google Scholar
  24. Tsien, J. Z. (2007). The memory. Scientific American, 297(July), 52–59.CrossRefGoogle Scholar
  25. Wang, J., Brendan, F., & Ogata, H. (2017). Semi-automatic construction of ontology based on data mining technique. In International Conference on Learning Technologies and Learning Environments (LTLE) 2017, IEEE CPS (pp. 511–515). Washington, DC: IEEE.Google Scholar
  26. Wang, J., & Mendori, T. (2012). A course-centered ontology of Japanese grammar for a language learning support system. Frontiers in Artificial Intelligence and Applications (KES2012), 243, 654–663.Google Scholar
  27. Wang, J., Mendori, T., & Xiong, J. (2013). A customizable language learning support system using ontology-driven engine. International Journal of Distance Education Technologies, 11(4), 81–96.CrossRefGoogle Scholar
  28. Wang, J. Y., & Mendori, T. (2015). An evaluation of the learning attitude and motivation in a language learning support system. In Proceedings of Advanced Learning Technologies and Technology-enhanced Learning (ICALT 2015), Hualien, Taiwan. Washington, DC: IEEE.Google Scholar
  29. Wang, J. Y., Mendori, T., & Xiong, J. (2014). A language learning support system using course-centered ontology and its evaluation. Computer & Education, 78, 278–293.CrossRefGoogle Scholar
  30. Wang, J. Y., Mendori, T., & Hoel, T. (2018). Strategies for multimedia learning object recommendation in a language learning support system: Verbal learners vs. Visual learners. International Journal of Human-Computer Interaction, 1–11.Google Scholar
  31. Wang, J. Y., Ogata, H., & Shimada, A. (2017). A meaningful discovery learning environment for e-book learners. In IEEE Global Engineering Education Conference, April 25–28, 2017. Washington, DC: IEEE.Google Scholar
  32. Zhong, X., Fu, H., Xia, H., Yang, L., & Shang, M. (2015). A hybrid cognitive assessment based on ontology knowledge map and skills. Knowledge-Based Systems, 73, 52–60.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jingyun Wang
    • 1
  1. 1.Research institute for information technologyKyushu universityFukuokaJapan

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