Intelligent E-Learning: Methodologies, Applications and Challenges

• Autorzy: Abdel-Badeeh M. Salem
• Języki publikacji: EN

Abstrakty

EN

Intelligent e-learning technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology. Researchers have been used the AI concepts and methodologies to develop a robust generation of intelligent tutoring and learning systems. Moreover, the convergence of AI and web science is enabling the creation of a new generation of web-based intelligent e-learning systems for all domains and tasks. This paper discusses the AI methodologies and techniques for developing the intelligent e-learning systems. Four most popular paradigms are discussed namely: case-based reasoning, ontological engineering, data mining and intelligent agents. Moreover, the paper addresses the challenges faced by the application developers and knowledge engineers in developing and deploying Al-based Learning systems. In addition, the paper presents some cases of intelligent learning systems/tools developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt. Keywords: knowledge engineering and management, intelligent e-learning systems, machine learning.(original abstract)

Słowa kluczowe

EN

Business informatics; Management; e-learning

PL

Informatyka ekonomiczna; Zarządzanie; Zdalne nauczanie

• Czasopismo: Scientific Publications / University of Economics in Katowice
• Rocznik: 2017
• Tom: Economics and management in information technology context : international week
• Strony: 19--34

Twórcy

autor

Abdel-Badeeh M. Salem

• Ain Shams University, Cairo, Egypt

Bibliografia

• Salem A.-B.M., Intellectual E-Learning Systems, Proc. 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, March 2011.
• Salem A.-B.M., The Role of Artificial Intelligence Technology in Education, Proc. of 5th International Conference on Emerging e-learning Technologies and Applications, Information and Communication Technologies in Learning, ICETA, The High Tatras, Slovakia 2007.
• Greer J. (ed.), Proc. of AI-ED '95, 7th World Conference on Artificial Intelligence in Education, Association for Advancement of Computing in Education (AACE), Washington 1995.
• Witten I.H., Frank E., Data Mining - Practical Machine Learning Tools and Techniques, 2nd ed., Elsevier, 2005.
• Pawlak Z., Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht 1991.
• Kolonder J., Case-Based Reasoning, Morgan Kaufmann, San Francisco 1993.
• Salem A.-B.M., Hodhod R.A., Developing a Hybrid Expert System Prototype for Diagnosis of Heart Diseases, Proc. of IFIP 17th World Computer Congress, Stream 8:IIP 2002 Intelligent Information Processing, Montreal, Canada, August 20-30, Poster Presentation 2002.
• Abdrabou E.A.M., Salem A.-B.M., A Breast Cancer Classifier based on a Combination of Case-Based Reasoning and Ontology Approach, Proc. of 2nd International Multi-conference on Computer Science and Information Technology, 1MCSIT 2010, Wisla, Poland 2010.
• Su X., Ilebrekke L., A Comparative Study of Ontology Languages and Tools, Proceedings of the 14th Conf. on Advanced Information Systems Engineering (CAiSE '02), Toronto, Canada 2002.
• Salem A.-B.M., Alfonse M., Ontological Engineering Approach for Breast Cancer Knowledge Management, Proc. of Med-e-Tel, the International eHealth, Telemedicine and Health ICT for Education, Networking and Business, Luxembourg, March 30-April 5, 2009.
• Moawad I.F., AL Marzoqi G., Salem A.-B.M., Building OBR-based OWL Ontology for Viral Hepatitis, "Egyptian Computer Science Journal", ECS, 2012, Vol. 36, No. 1.
• Salem A.-B,M,, Ontological Engineering in e-Learning, Proc. of 8th International Conference on Emerging e-learning Technologies and Applications, Information and Communication Technologies in Learning, (ICETA2010), Stara Lesna, The High Tatras, Slovakia 2010.
• Cakula S., Salem A.-B.M., Ontology-Based Collaborative Model for e-Learning, Proc. 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, 18 March 2011.
• Tantawi M., Revett K., Tolba M.F., Salem A.-B.M., ECG based Biometric Recognition using Wavelets and RBF Neural Network, Proceedings of the 7th European Computing Conference (ECC '13).
• Romero C., S. Ventura S., Data Mining in e-Learning, Wit Press, Southampton, UK 2006.
• Salem A.-B.M., Data Mining Technology in e-Learning, Proc. of 6th International Conference on Emerging e-leaming Technologies and Applications, Information and Communication Technologies in Learning, (ICETA2008), Stara Lesna, The High Tatras, Slovakia 2008.
• Viccari R.M., Ovalle D.A., Jiménez J.A., ALLEGRO: Teaching/Learning Multi-Agent Environment using Instructional Planning and Cases-Based Reasoning (CBR), "CLEI Electronic Journal", June 2007, Vol. 10, No. 1, Paper 4.
• Salem A.-B.M., Alfonse M., Building Web-Based Lung Cancer Ontology, "The International Journal of Soft Computing Applications" 2008, Iss. 2.
• Alfonse M., Aref M.M., Salem A.-B.M., Ontology-Based Knowledge Representation for Liver Cancer, Proc. of the International eHealth, Telemedicine and Health ICT Forum for Educational, Networking and Business, Luxembourg, G.D. of Luxembourg, April 18-20, 2012.
• Salem A.-B.M., Roushdy M., B.M. El Bagoory, An Expert System for Diagnosing Cancer Diseases, MENDEL 2001, Proc. of 7th International Conference on Soft Computing, Brno University of Technology, Czech Republic 2001.
• Salem A.-B.M., Roushdy M., Mahmoud S.A., Mining Patient Data Based on Rough Set Theory To Determine Thrombosis Disease, "International Journal On Artificial Intelligence and Machine Learning", AIML, Tubungen, Germany, 2004, Vol. 1.
• Tolba M.F., Salem A.-B.M., Amin S.E., Brain Tumor Classification Based on MRI Using Neural Networks, "International Journal of Intelligent Computing & Information Sciences", July 2001, Vol. 2, No. 2.
• Khalifa W.H., Roushdy M.I., Salem A.-B.M., User Identification System Based on EEG Signals, Proceeding of 6th International Conference on Intelligent Computing and Information Systems, Cairo, Egypt 2013.