Exploiting the knowledge engineering paradigms for designing smart learning systems

Abdel Badeeh Mohamed M. Salem, Silvia Parusheva

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.


Keywords


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

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References


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

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.

Greer, J. (Ed.) (1995). Artificial intelligence in education. Proceedings of AI-ED 95-7th World Conference on Artificial Intelligence in Education. Washington, DC.

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.

Clarke, A. (2004). e-Learning Skills. Palgrave Macmillan.

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

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

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.

Holsapple, C. W., Whinston, A. B. (1989). Business Expert Systems, Computer science series. Galgotia Publication Pvt. Ltd.

Kalibova, P., Milkova, E. (2016). Internet Addictive Behavior of Adolescents. International journal of education and information technologies, 10, 139–143.

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

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.

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.

Kolonder, J. (1993). Case-Based Reasoning. San Francisco, California, 668.

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.

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.

Bigus, J. P., Bigus, J. (1998). Constructing Intelligent Agents with Java: A programmer's Guide to Smarter Applications. Wiley Computer Publishing, 416.

Cios, K. J., Pedrycz, W., Swiniarski, R. W. (1998). Data Mining Methods for Knowledge Discovery. Springer. doi: 10.1007/978-1-4615-5589-6

Witten, I. H., Frank, E. (2005). Data Mining – Practical Machine Learning Tools and Techniques. Elsevier.

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

Romero, C., Ventura, S. (Eds.) (2006). Data mining in e-Learning. Southampton, UK: Wit Press. doi: 10.2495/1-84564-152-3

Feldman, R., Sanger, J. (2006). The text mining handbook. Cambridge University Press. doi: 10.1017/cbo9780511546914

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.

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

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.

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

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

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

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

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


GOST Style Citations


Knowledge Engineering and Planning for the Automated Synthesis of Customized Learning Designs / Castillo L., Morales L., González-Ferrer A., Fernández-Olivares J., García-Pérez Ó. // Lecture Notes in Computer Science. 2007. P. 40–49. doi: 10.1007/978-3-540-75271-4_5 

Salem A.-B. M. 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, 2007. P. 1–9.

Artificial intelligence in education / J. Greer (Ed.) // Proceedings of AI-ED 95-7th World Conference on Artificial Intelligence in Education. Washington, DC, 1995.

Mazza R., Milani C. 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, 2005. P. 1–6.

Clarke A. e-Learning Skills. Palgrave Macmillan, 2004.

Widenská E. Efficiency of practicing with materials using ICT and paper ones in mathematics // Journal on Efficiency and Responsibility in Education and Science. 2014. Vol. 7, Issue 2. P. 37–43. doi: 10.7160/eriesj.2014.070203 

El-Hmoudova D. Assessment of Individual Learning Style Preferences with Respect to the Key Language Competences // Procedia – Social and Behavioral Sciences. 2015. Vol. 171. P. 40–48. doi: 10.1016/j.sbspro.2015.01.086 

Milkova E., Korinek O. Future ICT Teachers – Programming Aptitude // Proceedings of the 11th International Conference Efficiency and Responsibility in Education (ERIE 2014). Prague, 2014. P. 456–462.

Holsapple C. W., Whinston A. B. Business Expert Systems, Computer science series. Galgotia Publication Pvt. Ltd., 1989.

Kalibova P., Milkova E. Internet Addictive Behavior of Adolescents // International journal of education and information technologies. 2016. Vol. 10. P. 139–143.

Milkova E., Pekarkova S., Salem A.-B. M. Information and Communication Technology in Education – Current Trends // MATEC Web of Conferences. 2016. Vol. 76. P. 04022. doi: 10.1051/matecconf/20167604022 

Cakula S., Salem A.-B. M. 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, 2011. P. 98–105.

Salem A.-B. M., Roushdy M. Case-Based and Ontology Learning Approaches for Developing e-Learning Systems // WSEAS Transactions on Information Science and Applications. 2005. Vol. 2, Issue 6. P. 795–804.

Kolonder J. Case-Based Reasoning. San Francisco, California, 1993. 668 p.

Salem A.-B. M. Case Based Reasoning Technology for Medical Diagnosis // Proceedings of World Academy of Science, Engineering and Technology. CESSE, Venice, Italy, 2007. P. 9–13.

Hans-Dieter Salem A.-B. M., Bagoury B. M. E. 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, 2007. P. 100–106.

Bigus J. P., Bigus J. Constructing Intelligent Agents with Java: A programmer's Guide to Smarter Applications. Wiley Computer Publishing, 1998. 416 p.

Cios K. J., Pedrycz W., Swiniarski R. W. Data Mining Methods for Knowledge Discovery. Springer, 1998. doi: 10.1007/978-1-4615-5589-6 

Witten I. H., Frank E. Data Mining – Practical Machine Learning Tools and Techniques. 2nd ed. Elsevier, 2005.

Jain A. K., Murty M. N., Flynn P. J. Data clustering: a review // ACM Computing Surveys. 1999. Vol. 31, Issue 3. P. 264–323. doi: 10.1145/331499.331504 

Data mining in e-Learning / C. Romero, S. Ventura (Eds.) // Southampton, UK: Wit Press, 2006. doi: 10.2495/1-84564-152-3 

Feldman R., Sanger J. The text mining handbook. Cambridge University Press, 2006. doi: 10.1017/cbo9780511546914 

Zaıane O., Luo J. Web usage mining for a better web-based learning environment // Proceedings of Conference on advanced technology for education. Banff, Alberta, 2001. P. 60–64.

Perez L., Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread // International Journal of Health Geographics. 2009. Vol. 8, Issue 1. P. 50. doi: 10.1186/1476-072x-8-50 

Skvortsov R. B., Connell P., Dawson R. G. Epidemic Modelling: Validation of Agent-based Simulation by Using Simple Mathematical Models // Proceedings of Land Warfare Conference. 2007. P. 221–227.

Bonabeau E. Agent-based modeling: Methods and techniques for simulating human systems // Proceedings of the National Academy of Sciences. 2002. Vol. 99. P. 7280–7287. doi: 10.1073/pnas.082080899 

Gąsior J., Seredyński F. A Decentralized Multi-agent Approach to Job Scheduling in Cloud Environment // Advances in Intelligent Systems and Computing. 2015. P. 403–414. doi: 10.1007/978-3-319-11313-5_36 

Yim J., Kim S. Review of the Techniques for Smart Learning Systems // Advanced Science and Technology Letters. 2016. Vol. 127. P. 1–5. doi: 10.14257/astl.2016.127.01 

Lalingkar A., Ramnathan C., Ramani S. Ontology-based Smart Learning Environment for Teaching Word Problems in Mathematics // Lecture Notes in Educational Technology. 2014. P. 251–258. doi: 10.1007/978-3-662-44188-6_35 

Lu J., Xu Q. Ontologies and Big Data Considerations for Effective Intelligence // Advances in Information Quality and Management. IGI Global, 2017. doi: 10.4018/978-1-5225-2058-0 



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

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Copyright (c) 2018 Abdel Badeeh Mohamed M. Salem, Silvia Parusheva

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061