Cloud eLearning - A New Paradigm of eLearning System Using Artificial Intelligence Techniques for Generating Personalized Learning Paths

Background/purpose. With advancements in technology, particularly in Artificial Intelligence (AI), personalized and adaptive systems are increasingly being integrated into conventional educational environments. These technologies create opportunities to place learners at the center of the educational experience through personalized learning. However, the lack of standardization among online educational learning resources limits their adaptability and re-use across different systems and contexts. To address this challenge, this study introduces a novel idea for an adaptable artificial intelligence system, namely the Cloud eLearning system (CeL), which includes Cloud eLearning Learning Objects (CeLLO) —standardized learning objects tagged with semantic metadata for flexible re-use.

Materials/methods. The system architecture features a Cloud eLearning Recommender System, which ranks educational learning resources using a vector space model and hierarchical clustering, and a Cloud eLearning Automated Planner, which generates personalized learning paths based on the recommender output. The study employed a two-phase evaluation methodology. First, the functionality of the AI-based Cloud eLearning prototype was assessed. Second, a user evaluation was conducted through a questionnaire administered to 17 undergraduate and postgraduate students selected from five Computer Science courses. The questionnaire collected both quantitative and qualitative data, including learner satisfaction and perceptions of personalization services.

Results. Quantitative data were analyzed through correlation analysis, revealing significant correlations: 99% between system performance and personalization services, 99% between system performance and learner satisfaction, 90% between system performance and learners’ initial background, 95% between learners’ background and personalization services, and 99% between learner satisfaction and personalization services.

Conclusion. These findings highlight the effectiveness of CeL in delivering personalized learning experiences and suggest that future work should focus on further validating the system across broader educational contexts and refining the algorithms output.

Keywords: Artificial Intelligence, eLearning, Cloud eLearning, Recommender Systems, Automated Planner, Personalisation.

References

Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., & others. (2009). Above the clouds: A Berkeley view of cloud computing. University of California, Berkeley. Retrieved from https://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf

Barker, P. (2005). What is IEEE learning object metadata/IMS learning resource metadata? CETIS Standards Briefing Series, JISC (Joint Information Systems Committee of the Universities’ Funding Councils), Retrieved from https://standict.eu/standards-repository/ieee-1484121-2020-ieee-standard-learning-object-metadata?utmsource = chatgpt.com

Brika, S. K. M., Chergui, K., Algamdi, A., Musa, A. A., & Zouaghi, R. (2022). E-learning research trends in higher education in light of covid-19: a bibliometric analysis. Frontiers in Psychology, 12, 762819, https://doi.org/10.3389/ fpsyg.2021.762819

Clark, R. C., & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. john Wiley & sons. Retrieved from https://onlinelibrary.wiley.com/doi/book/10.1002/9781119239086

Conole, G., & Oliver, M. (2006). Contemporary perspectives in e-learning research. Routledge London. Retrieved from https://doi.org/10.4324/9780203966266

Core, D. (2004). Dublin core metadata initiative. http://www. dublincore. org

Duval, E., Wason, T., & Hodgins, W. (2002). IEEE standard for learning object metadata. IEEE Standards Document, 1484 (1), 1-10, Retrieved from https://standards.ieee.org/ieee/1484.12.1/7699/

Fan, H., & Poole, M. S. (2006). What is personalization? perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce, 16(3-4), 179–202, https://doi.org/https://doi.org/10.1080/10919392.2006.9681199

Felder, R. M., & Soloman, B. A. (2000). Learning styles and strategies. North Carolina State University. Retrieved from: https://engr.ncsu.edu/wp-content/uploads/drive/1WPAfj3 j5o5OuJMiHorJ-lv6fON1C8kCN/styles.pdf

Gandhi, B. (2011). Business process as a service (bpaas) delivered from the cloud. IBM Cloud Computing News, 435–440, https://doi.org/10.1007/978-3-319-25013-7 35

Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: a literature review. Education Sciences, 13(12), 1216, https://doi.org/10.3390/educsci13121216

Gupta, M. (2016). A study on the impact of ICT (information and communication technology) in higher education with special reference to higher education institutions in Nagpur city. EDUCATE: Journal of Educational Research ISSN 2394-9422, 24, 41,

Hodgins, H. W. (2006). The future of learning objects. Educational Technology, 49–54, Retrieved from https://www.jstor.org/stable/44429269

Hooshyar, D., Azevedo, R., & Yang, Y. (2024). Augmenting deep neural networks with symbolic educational knowledge: Towards trustworthy and interpretable ai for education. Machine Learning and Knowledge Extraction, 6(1), 593–618, https://doi.org/10.3390/make6010028

Jena, K. K., Bhoi, S. K., Malik, T. K., Sahoo, K. S., Jhanjhi, N., Bhatia, S., & Amsaad, F. (2022). E-learning course recommender system using collaborative filtering models. Electronics, 12(1), 157, https://doi.org/10.3390/electronics12010157

King, F. B., Young, M. F., Drivere-Richmond, K., & Schrader, P. (2001). Defining distance learning and distance education. AACE Review (Formerly AACE Journal), 9(1), 1–14, Retrieved from https://www.learntechlib.org/primary/p/17786/

Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z., & Jain, L. C. (2016). E-learning systems: Intelligent techniques for personalization (Vol. 112). Springer. Retrieved from https://link.springer.com/book/10.1007/978-3-319-41163-7

Koohang, A., Riley, L., Smith, T., & Schreurs, J. (2009). E-learning and constructivism: From theory to application. Interdisciplinary Journal of E-Learning and Learning Objects, 5(1), 91–109, https://doi.org/10.28945/66

Martinez-Garcia, A., Horrach-Rossell´o, P., & Mulet-Forteza, C. (2023). Evolution and current state of research into e-learning. Heliyon, 9(10), 2405-8440. Retrieved from: https://doi.org/10.1016 /j.heliyon.2023.e21016

Miranda, J., Navarrete, C., Noguez, J., Molina-Espinosa, J. M., Ramírez-Montoya, M. S., Navarro-Tuch, S. A., & Molina, A. (2021). The core components of education 4.0 in higher education: Three case studies in engineering education. Computers & Electrical Engineering, 93, 107278. Retrieved from: https://doi.org/10.1016/j.compeleceng.2021.107278

Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-learning, online learning, and distance learning environments: Are they the same? The Internet and higher education, 14(2), 129–135, https://doi.org/10.1016/j.iheduc.2010.10.001

Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning approaches in digital education: A systematic revision. Information, 13(4), 203, https://doi.org/10.3390/info13040203

Naidu, S. (2006). E-learning: A guidebook of principles, procedures and practices. Commonwealth Educational Media Centre for Asia (CEMCA). Retrieved from https://oasis.col.org/handle/11599/53

Noesgaard, S. S., & Ørngreen, R. (2015). The effectiveness of e-learning: An explorative and integrative review of the definitions, methodologies and factors that pro-mote e-learning effectiveness. Electronic Journal of E-learning, 13(4), 278–290, Retrieved from https://eric.ed.gov/?id=EJ1062121

Oye, N. D., Salleh, M., Iahad, N. A. (2012). E-learning methodologies and tools. International Journal of Advanced Computer Science and Applications, 3(2), 48-52, https://doi.org/10.14569/IJACSA.2012.030208

Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative ai for cus-tomizable learning experiences. Sustainability, 16(7), 3034, https://doi.org/ 10.3390/su16073034

Pireva, K., & Kefalas, P. (2013). An innovative web application for managing academic documents. International Journal of Business and Technology, 1(2), 39–46, https://doi.org/10.33107/ijbte.2013.1.2.04

Pireva, K., & Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 235–245, https://doi.org/10.1007/978-3-319-66379-1 21

Pireva, K., Kefalas, P., & Cowling, A. (2017). A review of automated planning and its application to cloud e-learning. Work in progress, Paper Submitted, 1-14,

Pireva, K., Kefalas, P., Dranidis, D., Hatziapostolou, T., & Cowling, A. (2014). Cloud e-learning: A new challenge for multi-agent systems. Agent and multi-agent systems: Technologies and Applications: Proceedings of the 8th international conference kes-amsta 2014 Chania, Greece, June 2014 (pp. 277–287). Retrieved from https://doi.org/10.1007/978-3-319-07650-828

Pireva, K., Kefalas, P., & Stamatopoulou, I. (2017). Representation of learning objects in cloud e-learning. 2017 8th international conference on informa-tion, intelligence, systems & applications (iisa) (pp. 1–6). Retrieved from https://doi.org/10.1109/IISA.2017.8316435

Ruttenbur, B. W., Spickler, G. C., & Lurie, S. (2000). elearning: The engine of the knowledge economy. Morgan Keegan.

Sharma, S., Gautam, S., & Agarwal, A. (2023). E-learning: Research and applications. AITC-2023 and CSSP-2023, 23,

Stamatis, D., Kefalas, P., & Kargidis, T. (1999). A multi-agent framework to assist networked learning. Journal of computer assisted learning, 15(3), 201–210, https://doi.org/10.1046/j.1365-2729.1999.153094.x

Sultan, N. (2010). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116, https://doi.org/10.1016/ j.ijinfomgt.2009.09.004

Teixeira, C., Azevedo, R., Pinto, J. S., & Batista, T. (2010). User pro-vided cloud computing. 2010 10th ieee/acm international conference on cluster, cloud and grid computing (pp. 727–732). Retrieved from https://doi.org/10.1109/CCGRID.2010.104

Trafford, P., & Shirota, Y. (2011). An introduction to virtual learning environments. Gakushuin Econ. Pap, 48(10), 143–151,

Triacca, L., Bolchini, D., Botturi, L., & Inversini, A. (2004). Mile: Systematic usability evaluation for e-learning web applications. Edmedia+ innovate learning (pp. 4398–4405). Retrieved from https://core.ac.uk/download/pdf/20638806.pdf

Tuˇsevljak, M., Majcen, L., Mervar, L., Stepankina, T., & Cater, B. (2016). E-learning in higher education: Focus groups and survey among students in central europe. Journal of Educational Technology, 13(2), 11–20, Retrieved from https://files.eric.ed.gov/fulltext/EJ1131833.pdf

Villegas-Ch, W., & García-Ortiz, J. (2023). Enhancing learning personalization in educational environments through ontology-based knowledge representation. Computers, 12(10), 199. Retrieved from: https://doi.org/10.3390/computers12100199

Wilen-Daugenti, T. (2009). edu: Technology and learning environments in higher education. Peter Lang. Retrieved from https://doi.org/10.30829/vis.v17i2.1153

Wiley, D. A. (2008). The learning objects literature. Handbook of research on edu-cational communications and technology (pp. 345–353). Routledge. Retrieved from https://doi.org/10.4324/9780203880869-32

Wu, D., Lu, J., & Zhang, G. (2015). A fuzzy tree matching-based personalized e-learning recommender system. IEEE transactions on fuzzy systems, 23(6), 2412–2426, https://doi.org/10.1109/TFUZZ.2015.2426201

Zhang, J. (2015). Research on e-learning in college education: A model on personalized resource recommendation based on learning situation. 2015 IEEE 12th intl conf on ubiquitous intelligence and computing and 2015 (pp. 1264–1268). Retrieved from https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.230

Announcement

EDUPIJ Citation Metrics

EDUPIJ News!