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Journal of Intelligent Information Systems

, Volume 52, Issue 1, pp 165–190 | Cite as

Lightweight domain modeling for adaptive web-based educational system

  • Marian SimkoEmail author
  • Maria Bielikova
Article
  • 164 Downloads

Abstract

Support for adaptive learning with respect to increased interaction and collaboration over the educational content in state-of-the-art models of web-based educational systems is limited. Explicit formalization of such models is necessary to facilitate extendibility, reusability and interoperability. Domain models are the most fundamental parts of adaptive web-based educational systems providing a basis for majority of other functional components such as content recommenders or collaboration widgets and tools. We introduce a collaboration-aware lightweight domain modeling for adaptive web-based learning, which provides a suitable representation for learning resources and metadata involved in educational processes beyond individual learning. It introduces the concept of user annotations to the domain model, which enrich educational materials and facilitate collaboration. Lightweight domain modeling is beneficial from the perspective of automated course semantics creation, while providing support towards automated semantic description of learner-generated content. We show that the proposed model can be effectively utilized for intelligent processing of learning resources such as recommendation and can form a basis for interaction and collaboration supporting components of adaptive systems. We provide the experimental evidence on successful utilization of lightweight domain model in adaptive educational platform ALEF over the period of five years involving more than 1,000 real-world students.

Keywords

Domain modeling Adaptive educational system Lightweight semantics Annotations Collaboration 

Notes

Acknowledgements

This work was partially supported by the Slovak Research and Development Agency under the contracts No. APVV-15-0508 and No. APVV-16-0213, the Scientific Grant Agency of the Slovak Republic, grant No. VG 1/0646/15 and the Cultural and Educational Grant Agency of the Slovak Republic, grant No. KEGA 028STU-4/2017.

References

  1. Alshammari, M., Anane, R., Hendley, R. J. (2014). Adaptivity in E-learning systems. In Proceedings of 8th international conference on complex, intelligent and software intensive systems, CISIS 2014 (pp. 79–86). IEEE.Google Scholar
  2. Barla, M., Bielikova, M., Bou-Ezzedine, A., Kramar, T., Simko, M., Vozar, O. (2010). On the impact of adaptive test question selection for learning efficiency. Computers & Education, 55(2), 846–857.CrossRefGoogle Scholar
  3. Berners-Lee, T., Hendler, J., Lassila, O. (2001). The Semantic Web. Scientific American.Google Scholar
  4. Bielikova, M., Simko, M., Barla, M., Tvarozek, J., Labaj, M., Moro, R., Srba, I., Sevcech, J. (2014). ALEF: from application to platform for adaptive collaborative learning. In N. Manouselis, et al. (Eds.), Recommender systems for technology enhanced learning (pp. 195–225). New York: Springer LLC.Google Scholar
  5. Brusilovsky, P. (2012). Adaptive hypermedia for education and training. In P.J. Durlach, & A.M. Lesgold (Eds.), Adaptive technologies for training and education (pp. 46–67). New York: Cambridge University Press.Google Scholar
  6. Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: a literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.CrossRefGoogle Scholar
  7. Cimiano, P. (2006). Ontology learning and population from text: algorithms, evaluation and applications, (p. 347). Berlin: Springer.Google Scholar
  8. Cook, R., Kay, J., Kummerfeld, B. (2015). MOOClm: user modelling for MOOCs. In User modeling, adaptation and personalization (pp. 80–91). Berlin: Springer.Google Scholar
  9. Cristea, A. I., & de Mooij, A. (2003). LAOS: layered WWW AHS authoring model and their corresponding algebraic operators. In Proceedings of the 12th international World Wide Web conference. WWW’03. Alternate track on education.Google Scholar
  10. Cristea, A. I., & Stewart, C. (2005). Authoring of adaptive hypermedia. In G.D. Magoulas, & S.Y. Chen (Eds.), Advances in web-based education: personalized learning environments (pp. 225–250). Information Science Publishing.Google Scholar
  11. Cristea, A. I., Ghali, F., Joy, M. (2011). Social, personalized lifelong learning. In E-In-frastructures and technologies for lifelong learning: next generation environments (pp. 90—125).Google Scholar
  12. De Bra, P., Houben, G. J., Wu, H. (1999). AHAM: a Dexter-based reference model for adaptive hypermedia applications. In Proceedings of the 10th ACM conference on hypertext and hypermedia (pp. 147–156). ACM.Google Scholar
  13. De Bra, P., Smits, D., Van Der Sluijs, K., Cristea, A. I., Foss, J., Glahn, C., Steiner, C. M. (2013). GRAPPLE: learning management systems meet adaptive learning environments. In Intelligent and adaptive educational-learning systems (pp. 133–160). Berlin: Springer.Google Scholar
  14. Downes, S. (2005). E-learning 2.0. eLearn magazine. No. 10. ACM.Google Scholar
  15. El Bachari, E., Abelhawed, E. H., El Adnani, M. (2011). E-learning personalization based on dynamic learners’ preference. International Journal of Computer Science & Information Technology (IJCSIT), 3(3), 200–216.CrossRefGoogle Scholar
  16. Gaffney, C., Staikopoulos, T., O’Keeffe, I., Conlan, O., Wade, V. (2014). A training framework for adaptive educational hypermedia authoring tools. In Open learning and teaching in educational communities, EC-TEL 2014 (pp. 416–421). Berlin: Springer.Google Scholar
  17. Halasz, F., & Schwartz, M. (1990). The Dexter hypertext reference model. In Proceedings of the NIST hypertext standardization workshop (pp. 95–133). Gaithersburg.Google Scholar
  18. Harinek, J., & Simko, M. (2013). Improving term extraction by utilizing user annotations. In Proceedings of 13th ACM symposium on document engineering (pp. 185–188). ACM.Google Scholar
  19. Hatala, M. et al. (2012). Ontology extraction tools: an empirical study with educators. IEEE Transactions on Learning Technologies, 5(3), 275–289.CrossRefGoogle Scholar
  20. Hendrix, M., & Cristea, A. I. (2009). Design of the CAM model and authoring tool. A3H. In Proceedings of 7th authoring of adaptive and adaptable hypermedia workshop of ECTEL 2009.Google Scholar
  21. Hsiao, I. H., Bakalov, F., Brusilovsky, P., Konig-Ries, B. (2013). Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2), 112–131.CrossRefGoogle Scholar
  22. Juskeviciene, A., & Kurilovas, E. (2014). On recommending Web 2.0 Tools to personalise learning. Informatics in Education, 13(1), 17–32.Google Scholar
  23. Kahraman, H. T., Sagiroglu, S., Colak, I. (2013). A novel model for web?based adaptive educational hypermedia systems: SAHM (Supervised Adaptive Hypermedia Model). Computer Applications in Engineering Education, 21(1), 60–74.CrossRefGoogle Scholar
  24. Knutov, E., De Bra, P., Pechenizkiy, M. (2009). AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia, 15(1), 5–38.CrossRefGoogle Scholar
  25. Koch, N., & Wirsing, M. (2002). The Munich reference model for adaptive hyperm. Applications. In Adaptive hypermedia and adaptive web-based systems, LNCS 2347 (pp. 213–222). Springer.Google Scholar
  26. Kompan, M., & Bielikova, M. (2016). Enhancing existing e-learning systems by single and group recommendations. International Journal of Continuing Engineering Education and Lifelong Learning, 26(4), 386–404.CrossRefGoogle Scholar
  27. Michlik, P., & Bielikova, M. (2010). Exercises recommending for limited time learning. Procedia Computer Science, 1(2), 2821–2828.CrossRefGoogle Scholar
  28. Mihal, V., & Bielikova, M. (2011). Domain model relations discovering in educational texts based on user created annotations. In Proceedings of 14th international conference on interactive collaborative learning (ICL). IEEE (pp. 542–547). IEEE.Google Scholar
  29. Moro, R., Srba, I., Uncik, M., Bielikova, M., Simko, M. (2011). Towards collaborative metadata enrichment for adaptive web-based learning. In Proceedings of international workshop on computational social networks, web intelligence 2011 (pp. 106–109). IEEE.Google Scholar
  30. Sampson, D. G., Lytras, M. D., Wagner, G., Diaz, P. (2004). Ontologies and the Semantic Web for E-learning. Educational Technology & Society, 7(4), 26–28.Google Scholar
  31. Shi, L., Al Qudah, D., Qaffas, A., Cristea, A. I. (2013). Topolor: a social personalized adaptive e-learning system. In User modeling, adaptation, and personalization, LNCS 7899 (pp. 338–340). Berlin: Springer.Google Scholar
  32. Somyurek, S. (2015). The new trends in adaptive educational hypermedia systems. The International Review of Research in Open and Distributed Learning, 16(1), 221–241.CrossRefGoogle Scholar
  33. Srba, I., & Bielikova, M. (2014). Dynamic group formation as an approach to collaborative learning support. IEEE Transactions on Learning Technologies, 8(2), 173–186.CrossRefGoogle Scholar
  34. Svrcek, M., & Simko, M. (2014). Supporting educational content enrichment and learning via student-created definitions. In Y. Cao, et al. (Eds.), New horizons in web based learning, ICWL 2014 workshops, LNCS 8699 (pp. 44–54). Berlin: Springer.Google Scholar
  35. Simko, M., & Bielikova, M. (2009). Automatic concept relationships discovery for an adaptive E-course. In T. Barnes, et al. (Eds.), Proceedings of educational data mining 2009: 2nd international conference on educational data mining (pp. 171–179).Google Scholar
  36. Simko, M., Barla, M., Bielikova, M. (2010). ALEF: a framework for adaptive web-based learning 2.0. In N. Reynolds, & M. Turcsanyi-Szabo (Eds.), KCKS 2010, IFIP advances in information and communication technology (Vol. 324, pp. 367–378). Berlin: Springer.Google Scholar
  37. Simko, M., Barla, M., Mihal, V., Uncik, M., Bielikova, M. (2011). Supporting collaborative web-based education via annotations. In Proceedings of world conference on educational multimedia, hypermedia & telecommunications, ED-MEDIA 2011. AACE (pp. 2576–2585).Google Scholar
  38. Simko, J. et al. (2013). Classsourcing: crowd-based validation of question-answer learning objects. In Conference on computational collective intelligence (pp. 62–71). Berlin: Springer.Google Scholar
  39. Tadlaoui, M., Chikh, A., Bouamrane, K. (2013). ALEM: a reference model for educational adaptive web applications. In Intelligent and adaptive educational-learning systems (pp. 25–48). Berlin: Springer.Google Scholar
  40. Uschold, M., & Gruninger, M. (2004). Ontologies and semantics for seamless connectivity. ACM SIGMOD Record, 33(4), 58–64.CrossRefGoogle Scholar
  41. Uncik, M., & Bielikova, M. (2010). Annotating educational content by questions created by learners. In Proceedings of 5th international workshop on semantic media adaptation and personalization (pp. 13–18). IEEE.Google Scholar
  42. Vesin, B., Ivanovic, M., Klasnja-Milicevic, A., Budimac, Z. (2012). Protus 2.0: ontology-based semantic recommendation in programming tutoring system. Expert Systems with Applications, 39(15), 12229–12246.CrossRefGoogle Scholar
  43. Vrablecova, P., & Simko, M. (2016). Supporting semantic annotation of educational content by automatic extraction of hierarchical domain relationships. IEEE Transactions on Learning Technologies, 35(5), 1027–1049.Google Scholar
  44. Wang, H. C., & Huang, T. H. (2013). Personalized e-learning environment for bioinformatics. Interactive Learning Environments, 21(1), 18–38.CrossRefGoogle Scholar
  45. Wong, W., Liu, W., Bennamoun, M. (2012). Ontology learning from text: a look back and into the future. ACM Computing Surveys, 44(4), No. 20.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Informatics and Information TechnologiesSlovak University of Technology in BratislavaBratislavaSlovakia

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