Exploiting the knowledge engineering paradigms for designing smart learning systems

Authors

  • Abdel Badeeh Mohamed M. Salem University of Economics – Varna Research Institute of the University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002, Bulgaria
  • Silvia Parusheva University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002, Bulgaria https://orcid.org/0000-0002-7050-3514

DOI:

https://doi.org/10.15587/1729-4061.2018.128410

Keywords:

Knowledge engineering, Smart Learning Systems, Artificial Intelligence, Intelligent Agents, Data Mining, Case-Based Reasoning, Smart Computing

Abstract

Knowledge engineering (KE) is a subarea of artificial intelligence (AI). Recently, KE paradigms have become more widespread within the fields of smart education and learning. Developing of Smart learning Systems (SLS) is very difficult from the technological perspective and a challenging task. In this paper, three KE paradigms, namely: case-based reasoning, data mining, and intelligent agents are discussed. This article demonstrates how SLS can take advantage of the innovative KE paradigms. Therefore, the paper addresses the pros of such smart computing approaches for the industry of SLS. Moreover, we concentrate our discussion on the challenges faced by knowledge engineers and software developers in developing and deploying efficient and robust SLS. Overall, this study introduces the reader the KE techniques, approaches and algorithms currently in use and the open research issues in designing the smart learning systems.

Author Biographies

Abdel Badeeh Mohamed M. Salem, University of Economics – Varna Research Institute of the University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002

PhD, Professor

Silvia Parusheva, University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002

Doctor on Economic Sciences, Associate Professor

Department of Informatics, Faculty of Computer Science

References

  1. Castillo, L., Morales, L., González-Ferrer, A., Fernández-Olivares, J., García-Pérez, Ó. (2007). Knowledge Engineering and Planning for the Automated Synthesis of Customized Learning Designs. Lecture Notes in Computer Science, 40–49. doi: 10.1007/978-3-540-75271-4_5
  2. Salem, A.-B. M. (2007). The Role of Artificial Intelligence Technology in Education. Proceedings of 5th International Conference on Emerging e-Learning Technologies and Applications, Information and Communication Technologies in Learning, ICETA. Slovakia, 1–9.
  3. Greer, J. (Ed.) (1995). Artificial intelligence in education. Proceedings of AI-ED 95-7th World Conference on Artificial Intelligence in Education. Washington, DC.
  4. Mazza, R., Milani, C. (2005). Exploring usage analysis in learning systems: Gaining insights from visualizations. Workshop on usage analysis in learning systems at 12th International Conference on artificial intelligence in education. New York, USA, 1–6.
  5. Clarke, A. (2004). e-Learning Skills. Palgrave Macmillan.
  6. Widenská, E. (2014). Efficiency of practicing with materials using ICT and paper ones in mathematics. Journal on Efficiency and Responsibility in Education and Science, 7 (2), 37–43. doi: 10.7160/eriesj.2014.070203
  7. El-Hmoudova, D. (2015). Assessment of Individual Learning Style Preferences with Respect to the Key Language Competences. Procedia – Social and Behavioral Sciences, 171, 40–48. doi: 10.1016/j.sbspro.2015.01.086
  8. Milkova, E., Korinek, O. (2014). Future ICT Teachers – Programming Aptitude. Proceedings of the 11th International Conference Efficiency and Responsibility in Education (ERIE 2014). Prague, 456–462.
  9. Holsapple, C. W., Whinston, A. B. (1989). Business Expert Systems, Computer science series. Galgotia Publication Pvt. Ltd.
  10. Kalibova, P., Milkova, E. (2016). Internet Addictive Behavior of Adolescents. International journal of education and information technologies, 10, 139–143.
  11. Milkova, E., Pekarkova, S., Salem, A.-B. M. (2016). Information and Communication Technology in Education – Current Trends. MATEC Web of Conferences, 76, 04022. doi: 10.1051/matecconf/20167604022
  12. Cakula, S., Salem, A.-B. M. (2011). Ontology-based Collaborative Model for e-Learning. Proceedings of the Annual International Conference on “Virtual and Augmented Reality in Education” (VARE 2011) (combined with EEA and Norwegian Financial Instruments project practical conference “VR/AR Applications in Training”), Vidzeme University of Applied Sciences. Valmiera, Latvia, 98–105.
  13. Salem, A.-B. M., Roushdy, M. (2005). Case-Based and Ontology Learning Approaches for Developing e-Learning Systems. WSEAS Transactions on Information Science and Applications, 2 (6), 795–804.
  14. Kolonder, J. (1993). Case-Based Reasoning. San Francisco, California, 668.
  15. Salem, A.-B. M. (2007). Case Based Reasoning Technology for Medical Diagnosis. Proceedings of World Academy of Science, Engineering and Technology. CESSE, Venice, Italy, 9–13.
  16. Hans-Dieter Salem, A.-B. M., Bagoury, B. M. E. (2007). Ideas of Case-Based Reasoning for Key frame Technique. Proceedings of the XVIth International Workshop on the Concurrency Specification and Programming, CS & P 2007. Logow, Warsa, Poland, 100–106.
  17. Bigus, J. P., Bigus, J. (1998). Constructing Intelligent Agents with Java: A programmer's Guide to Smarter Applications. Wiley Computer Publishing, 416.
  18. Cios, K. J., Pedrycz, W., Swiniarski, R. W. (1998). Data Mining Methods for Knowledge Discovery. Springer. doi: 10.1007/978-1-4615-5589-6
  19. Witten, I. H., Frank, E. (2005). Data Mining – Practical Machine Learning Tools and Techniques. Elsevier.
  20. Jain, A. K., Murty, M. N., Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31 (3), 264–323. doi: 10.1145/331499.331504
  21. Romero, C., Ventura, S. (Eds.) (2006). Data mining in e-Learning. Southampton, UK: Wit Press. doi: 10.2495/1-84564-152-3
  22. Feldman, R., Sanger, J. (2006). The text mining handbook. Cambridge University Press. doi: 10.1017/cbo9780511546914
  23. Zaıane, O., Luo, J. (2001). Web usage mining for a better web-based learning environment. Proceedings of Conference on advanced technology for education. Banff, Alberta, 60–64.
  24. Perez, L., Dragicevic, S. (2009). An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 8 (1), 50. doi: 10.1186/1476-072x-8-50
  25. Skvortsov, R. B., Connell, P., Dawson, R. G. (2007). Epidemic Modelling: Validation of Agent-based Simulation by Using Simple Mathematical Models. Proceedings of Land Warfare Conference, 221–227.
  26. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99, 7280–7287. doi: 10.1073/pnas.082080899
  27. Gąsior, J., Seredyński, F. (2015). A Decentralized Multi-agent Approach to Job Scheduling in Cloud Environment. Advances in Intelligent Systems and Computing, 403–414. doi: 10.1007/978-3-319-11313-5_36
  28. Yim, J., Kim, S. (2016). Review of the Techniques for Smart Learning Systems. Advanced Science and Technology Letters, 127, 1–5. doi: 10.14257/astl.2016.127.01
  29. Lalingkar, A., Ramnathan, C., Ramani, S. (2014). Ontology-based Smart Learning Environment for Teaching Word Problems in Mathematics. Lecture Notes in Educational Technology, 251–258. doi: 10.1007/978-3-662-44188-6_35
  30. Lu, J., Xu, Q. (2017). Ontologies and Big Data Considerations for Effective Intelligence. Advances in Information Quality and Management. IGI Global. doi: 10.4018/978-1-5225-2058-0

Downloads

Published

2018-04-11

How to Cite

Salem, A. B. M. M., & Parusheva, S. (2018). Exploiting the knowledge engineering paradigms for designing smart learning systems. Eastern-European Journal of Enterprise Technologies, 2(2 (92), 38–44. https://doi.org/10.15587/1729-4061.2018.128410