UNLOCKING THE POWER OF RECOMMENDATION SYSTEMS:PERSONALIZED LEARNING IN EDUCATION

##article.authors##

  • Doniyor G’ulomov Muallif
  • Alpamis Kutlimuratov Muallif

##article.abstract##

The modern educational landscape is undergoing a significant transformation driven by the advent of technology, primarily through recommendation systems that harness artificial intelligence (AI) and machine learning (ML). These systems, pivotal in driving personalized learning, analyze vast amounts of student data, including academic performance, learning styles, and interests. Through this data-driven approach, they produce tailored recommendations, ensuring individualized learning pathways and resources, thus creating unique educational experiences for each student. This article delves into the benefits of such systems in fostering personalized learning, addressing the inherent diversity among learners, boosting engagement, and equipping students with crucial skills for the 21st century. The ultimate goal is to harmonize the capabilities of AI and human educators to craft a more engaging, inclusive, and effective educational landscape.

##plugins.themes.default.displayStats.downloads##

##plugins.themes.default.displayStats.noStats##

##submission.citations##

Ilyosov, A.; Kutlimuratov, A.; Whangbo, T.-K. Deep-Sequence–Aware Candidate Generation for e-Learning System. Processes 2021, 9, 1454. https://doi.org/10.3390/pr9081454.

Safarov F, Kutlimuratov A, Abdusalomov AB, Nasimov R, Cho Y-I. Deep Learning Recommendations of E-Education Based on Clustering and Sequence. Electronics. 2023; 12(4):809. https://doi.org/10.3390/electronics12040809

Kutlimuratov, A.; Abdusalomov, A.; Whangbo, T.K. Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions. Symmetry 2020, 12, 1930.

Kutlimuratov, A.; Abdusalomov, A.B.; Oteniyazov, R.; Mirzakhalilov, S.; Whangbo, T.K. Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization. Sensors 2022, 22, 8224. https://doi.org/10.3390/s22218224.

Makhmudov, F.; Kutlimuratov, A.; Akhmedov, F.; Abdallah, M.S.; Cho, Y.- I. Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders. Electronics 2022, 11, 4047. ttps://doi.org/10.3390/electronics1123404 A. Abdusalomov, A. Kutlimuratov, R. Nasimov and T. K. Whangbo, "Improved speech emotion recognition focusing on high-level data representations and swift feature extraction calculation," Computers, Materials & Continua, vol. 77, no.3, pp. 2915–2933, 2023.

Alpamis Kutlimuratov, Jamshid Khamzaev, Dilnoza Gaybnazarova. (2023). THE PROCESS OF DEVELOPING PERSONALIZED TRAVEL RECOMMENDATIONS. https://doi.org/10.5281/zenodo.7858377

Alpamis Kutlimuratov, Nozima Atadjanova. (2023). MOVIE RECOMMENDER SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS ALGORITHM. https://doi.org/10.5281/zenodo.7854603

Alpamis Kutlimuratov, Makhliyo Turaeva. (2023). MUSIC RECOMMENDER SYSTEM. https://doi.org/10.5281/zenodo.7854462

Alpamis Kutlimuratov, and Jamshid Khamzaev. 2023. “TRAFFIC MANAGEMENT WITH ARTIFICIAL INTELLIGENCE”. Лучшие интеллектуальные исследования 11 (1):51-57. http://web- journal.ru/index.php/journal/article/view/2202.

##submission.downloads##

##submissions.published##

2024-03-20

##issue.issue##

##section.section##

Technical Sciences

##plugins.generic.shariff.share##