E-Learning Intelligence Model with Artificial Intelligence to Improve Learning Performance of Students

Guangju Li

doi:10.69996/jcai.2023002

Authors

Guangju Li School of Finance and Trade,Zhengzhou Shengda University of Economics, Business & Management, Zhengzhou,451191,China. Author

DOI:

https://doi.org/10.69996/jcai.2023002

Keywords:

Artificial Intelligence, Hidden Chain Fuzzy Model, Mean Squared Error, education, hiddenmarkov model

Abstract

In recent year, artificial intelligence is significantly evolving model for the estimation of the vast range of advanced model. This paper introduces a novel approach for predicting learning styles in the realm of education through the application of a Hidden Chain Fuzzy Model (HCFM). Learning styles, encompassing auditory, visual, and kinesthetic preferences, play a pivotal role in shaping effective teaching strategies and personalized educational experiences. The HCFM, an innovative extension of traditional fuzzy models, is designed to capture the intricate relationships and dependencies inherent in learning behaviors. The model’s learning process is rigorously examined, evaluating its ability to discern diverse learning styles through a dataset of instances characterized by varying aptitudes and preferences. Comprehensive classification metrics, including accuracy, precision, recall, F1 Score, AUC-ROC, and Mean Squared Error (MSE), are employed to assess the model’s performance across distinct learning styles. The HCFM’s predictive capabilities are further demonstrated through a detailed analysis of individual instances, highlighting its effectiveness in tailoring predictions to the unique characteristics of learners. The results suggest that the HCFM holds promise as a powerful tool for personalized education, paving the way for more adaptive and tailored learning environments. The paper concludes by discussing potential avenues for future research, emphasizing the importance of further validation and exploration of the model’s applicability in diverse educational settings.

References

[1] K. Y. Tang, C. Y. Chang and G. J. Hwang, “Trends in artificial intelligence-supported e-learning: a systematic review and co-citation network analysis (1998–2019),” Interactive Learning Environments, vol.31, no.4, pp.2134-2152, 2023.

[2] P. Gao, J. and S. Liu, “An introduction to key technology in artificial intelligence and big data driven e-learning and e-education,” Mobile Networks and Applications, vol.26, no.5, pp.2123-2126, 2021.

[3] K. Jia, P. Wang, Y. Li, Z. Chen, X. Jiang, C.L. Lin and T. Chin, “Research landscape of artificial intelligence and e-learning: bibliometric research,” Frontiers in Psychology, vol.13, pp.795039, 2022.

[4] W. Bagunaid, N. Chilamkurti and P. Veeraraghavan, “AISAR: artificial intelligence-based student assessment and recommendation system for e-learning in big data,” Sustainability, vol.14, no.17, pp.10551, 2022.

[5] M. A. Khan, M.Khojah and Vivek, “Artificial intelligence and big data: The advent of new pedagogyin the adaptive e-learning system in the higher educational institutions of Saudi Arabia,” Education Research International, vol.2022, pp.1-10, 2022.

[6] H.C. Lin, C.F.Ho and H. Yang, “Understanding adoption of artificial intelligence-enabled language elearning system: an empirical study of utaut model,” International Journal of Mobile Learning and Organisation, vol.16, no.1, pp.74-94, 2022.

[7] U. Arun Kumar, G. Mahendran and S. Gobhinath, “A review on artificial intelligence-based e-learning system,” Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, pp.659-671, 2022.

[8] M.A. Almaiah, R. Alfaisal, S.A. Salloum, F. Hajjej, S. Thabit, F.A. Qirem et al., “Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: Students’ perceptions at the university level,” Electronics, vol.11, no.22, pp.3662, 2022.

[9] A.M.A. Alnaqbi and A.M. Yassin, “Evaluation of success factors in adopting artificial intelligence in e-learning environment,” International Journal of Sustainable Construction Engineering and Technology, vol.12, no.3, pp.362-369, 2021.

[10] H. Ahmad, R. Hanandeh, F. Alazzawi, A. Daradkah, A. ElDmrat et al., “The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: evidence from Jordanian telecommunication firms,” International Journal of Data and Network Science, vol.7, no.1, pp.35-40, 2023.

[11] M. Rakhimov, A. Yuldashev and D. Solidjonov, “The role of artificial intelligence in the management of e-learning platforms and monitoring knowledge of students,” Oriental renaissance: Innovative, educational, natural and social sciences, vol.1, no.9, pp.308-314, 2021.

[12] N. Rohde, N. Flindt, C. Rietz and G.K. Kassymova, “How e-learning programs can be more individualized with artificial intelligence–a theoretical approach from a pedagogical point of view,” Muallim Journal of Social Sciences and Humanities, pp.1-17, 2023.

[13] M. Murtaza, Y. Ahmed, J.A. Shamsi, F. Sherwani and M. Usman, “AI-based personalized e-learning systems: Issues, challenges, and solutions,” IEEE Access, 2022.

[14] X. Fu, K. Lokesh Krishna and R. Sabitha, “Artificial intelligence applications with e-learning system for China’s higher education platform,” Journal of Interconnection Networks, vol.22, no. Supp02,pp.2143016, 2022.

[15] M. M. E. I.Bali, M. P. Kumalasani and D. Yunilasari, “Artificial intelligence in higher education: perspicacity relation between educators and students,” Journal of Innovation in Educational and Cultural Research, vol.3, no.2, pp.146-152, 2022.

[16] S. S. M. Gutierrez, S. L. Pérez and M. G. Munguía, “Artificial intelligence in e-learning plausible scenarios in Latin America and new graduation competencies,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol.17, no.1, pp.31-40, 2022.

[17] A. K. Milićević and M. Ivanović, “E-learning personalization systems and sustainable education,” Sustainability, vol.13, no.12, pp.6713, 2021.

[18] T. Liu, Q. Wu, L. Chang and T. Gu, “A review of deep learning-based recommender system in elearning environments,” Artificial Intelligence Review, vol.55, no.8, pp.5953-5980, 2022.

[19] N. N. Hamadneh, S. Atawneh, W.A. Khan, K.A. Almejalli and A. Alhomoud, “Using artificial intelligence to predict students’ academic performance in blended learning,” Sustainability, vol.14, no.18, pp.11642, 2022.

[20] M. R. M. Veeramanickam and P. Ramesh, “Analysis on quality of learning in e-Learning platforms,” Advances in Engineering Software, vol.172, pp.103168, 2022.

[21] F. Rasheed and A. Wahid, “Learning style detection in E-learning systems using machine learning techniques,” Expert Systems with Applications, vol.174, pp.114774, 2021.

[22] V. Kuleto, M. Ilić, M. Dumangiu, M. Ranković, O.M. Martins, D. Păun and L. Mihoreanu, “Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions,” Sustainability, vol.13, no.18, pp.10424. 2001.