An Intelligent Learning Support System

Mariia Gavriushenko, Oleksiy Khriyenko, Ari Tuhkala

2017

Abstract

Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies. In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.

References

  1. Aroyo, L. and Dicheva, D. (2004). The new challenges for e-learning: The educational semantic web. Educational Technology & Society, 7(4):59-69.
  2. Bendakir, N. and Aïmeur, E. (2006). Using association rules for course recommendation. In Proceedings of the AAAI Workshop on Educational Data Mining, volume 3.
  3. Berners-Lee, T. (2006). Linked data. design issues for the world wide web. World Wide Web Consortium. http://www. w3. org/DesignIssues/LinkedData. html.
  4. Berners-Lee, T., Hendler, J., Lassila, O., et al. (2001). The semantic web. Scientific american, 284(5):28-37.
  5. Brusilovsky, P. and Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13:159-172.
  6. Canales, A., Pen˜a, A., Peredo, R., Sossa, H., and Gutiérrez, A. (2007). Adaptive and intelligent web based education system: Towards an integral architecture and framework. Expert Systems with Applications, 33(4):1076-1089.
  7. Chen, C.-M. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2):787-814.
  8. Dicheva, D., Mizoguchi, R., and Greer, J. E. (2009). Semantic web technologies for e-learning, volume 4. Ios Press.
  9. Dolenc, K. and Abers?ek, B. (2015). Tech8 intelligent and adaptive e-learning system: Integration into technology and science classrooms in lower secondary schools. Computers & Education, 82:354-365.
  10. García-Barrios, V. M., Mödritscher, F., and Gütl, C. (2005). Personalisation versus adaptation? a user-centred model approach and its application. In Proceedings of the International Conference on Knowledge Management (I-KNOW), pages 120-127.
  11. Gavriushenko, M., Kankaanranta, M., and Neittaanmäki, P. (2015). Semantically enhanced decision support for learning management systems. In Semantic Computing (ICSC), 2015 IEEE International Conference on, pages 298-305. IEEE.
  12. Gavriushenko, M., Khriyenko, O., and Porokuokka, I. (2016). Adaptive vocabulary learning environment for late talkers. In CSEDU 2016: Proceedings of the 8th International Conference on Computer Supported Education. Vol. 2, ISBN 978-989-758-179-3. SCITEPRESS.
  13. Heath, T. and Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, 1(1):1-136.
  14. Henderson, L. K. and Goodridge, W. (2015). Adviseme: An intelligent web-based application for academic advising. International Journal of Advanced Computer Science & Applications, 1(6):233-243.
  15. Huang, M.-J., Huang, H.-S., and Chen, M.-Y. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3):551-564.
  16. Jeong, H.-Y., Choi, C.-R., and Song, Y.-J. (2012). Personalized learning course planner with e-learning dss using user profile. Expert Systems with Applications, 39(3):2567-2577.
  17. Jovanovic, J., Gasevic, D., Brooks, C., Devedzic, V., Hatala, M., Eap, T., and Richards, G. (2007). Using semantic web technologies to analyze learning content. IEEE Internet Computing, 11(5).
  18. Jovanovic, J., Gas?evic, D., and Devedz?ic, V. (2009). Tangram for personalized learning using the semantic web technologies. Journal of emerging technologies in web intelligence, 1(1):6-21.
  19. Jovanovic, J., Gas?evic, D., Knight, C., and Richards, G. (2007). Ontologies for effective use of context in elearning settings. Educational Technology & Society, 10(3):47-59.
  20. Jyothi, N., Bhan, K., Mothukuri, U., Jain, S., and Jain, D. (2012). A recommender system assisting instructor in building learning path for personalized learning system. In Technology for Education (T4E), 2012 IEEE Fourth International Conference On, pages 228-230. IEEE.
  21. Khriyenko, O. and Khriyenko, T. (2013). Innovative education environment and open data initiative: Steps towards user-powered society-oriented systems. GSTF Journal on Computing (JoC), 3(3):31.
  22. Leung, C. M., Tsang, E. Y., Lam, S., and Pang, D. C. (2010). Intelligent counseling system: A 24 x 7 academic advisor. Educause Quarterly, 33(4):n4.
  23. Myneni, L. S., Narayanan, N. H., Rebello, S., Rouinfar, A., and Pumtambekar, S. (2013). An interactive and intelligent learning system for physics education. IEEE Transactions on learning technologies, 6(3):228-239.
  24. Nguyen, T. B., Nguyen, D. N., Nguyen, H. S., Tran, H., and Hoang, T. A. D. (2008). An integrated approach for an academic advising system in adaptive credit-based learning environment.
  25. Nurjanah, D. (2016). Good and similar learners' recommendation n adaptive learning systems. pages 434- 440.
  26. Phobun, P. and Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e-learning systems. Procedia-Social and Behavioral Sciences, 2(2):4064- 4069.
  27. Ranganathan, G. R. and Brown, J. A. (2007). The use of ontologies and rules to assist in academic advising. In International Workshop on Rules and Rule Markup Languages for the Semantic Web, pages 207- 214. Springer.
  28. Shvaiko, P. and Euzenat, J. (2013). Ontology matching: state of the art and future challenges. IEEE Transactions on knowledge and data engineering, 25(1):158- 176.
  29. Uren, V., Cimiano, P., Iria, J., Handschuh, S., Vargas-Vera, M., Motta, E., and Ciravegna, F. (2006). Semantic annotation for knowledge management: Requirements and a survey of the state of the art. Web Semantics: science, services and agents on the World Wide Web, 4(1):14-28.
  30. Wen, F. L. S. L. D. and McGreal, F. Z. K. R. (2007). eadvisor: A multi-agent system for academic advising.
  31. Werghi, N. and Kamoun, F. K. (2009). A decisiontree-based system for student academic advising and planning in information systems programmes. International Journal of Business Information Systems, 5(1):1-18.
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Paper Citation


in Harvard Style

Gavriushenko M., Khriyenko O. and Tuhkala A. (2017). An Intelligent Learning Support System . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 217-225. DOI: 10.5220/0006252102170225


in Bibtex Style

@conference{csedu17,
author={Mariia Gavriushenko and Oleksiy Khriyenko and Ari Tuhkala},
title={An Intelligent Learning Support System},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={217-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006252102170225},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - An Intelligent Learning Support System
SN - 978-989-758-239-4
AU - Gavriushenko M.
AU - Khriyenko O.
AU - Tuhkala A.
PY - 2017
SP - 217
EP - 225
DO - 10.5220/0006252102170225