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Articles de revues sur le sujet « Personalization of Learning »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Alzain, Alzain, Steve Clark, Gren Ireson, and Ali Jwaid. "Learning Personalization Based on Learning Style instruments." Advances in Science, Technology and Engineering Systems Journal 3, no. 3 (2018): 108–15. http://dx.doi.org/10.25046/aj030315.

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Pushkar, Mehendale. "Personalization in Education through AI." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 60–65. https://doi.org/10.5281/zenodo.12789606.

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Personalized learning, aided by artificial intelligence (AI), is a groundbreaking shift in education that abandons traditional one-size-fits-all approaches. This paper investigates the impact of AI on personalized education, particularly emphasizing how adaptive learning systems can enhance student engagement, motivation, and academic performance. A comprehensive review of current research and case studies highlights the technological, pedagogical, and ethical implications of implementing AI-driven personalized learning. The findings underscore the potential of AI to revolutionize education by

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Ginns, Paul, and Jennifer Fraser. "Personalization enhances learning anatomy terms." Medical Teacher 32, no. 9 (2010): 776–78. http://dx.doi.org/10.3109/01421591003692714.

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Yoganarasimhan, Hema. "Search Personalization Using Machine Learning." Management Science 66, no. 3 (2020): 1045–70. http://dx.doi.org/10.1287/mnsc.2018.3255.

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Komilova, M.O, and L.D Kholmurodova. "EFFECTIVE PERSONALIZATION IN BLENDED LEARNING." International Journal of Education, Social Science & Humanities. FARS Publishers 11, no. 1 (2023): 974–79. https://doi.org/10.5281/zenodo.7590956.

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Amulya, Mrs A., Abhishek Sai Dubey, B. Ainesh, and K. Vinay Kumar. "Immersive Learning: Revolutionizing Education Through Sense Stimulation and Personalization." International Journal of Research Publication and Reviews 6, no. 5 (2025): 16805–9. https://doi.org/10.55248/gengpi.6.0525.19113.

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Zhang, Chuxu, Huaxiu Yao, Lu Yu, et al. "Inductive Contextual Relation Learning for Personalization." ACM Transactions on Information Systems 39, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3450353.

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Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to rel

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Buha, Vesna, Verica Vasiljevic, and Slobodanka Djenic. "Distance learning and process of personalization." Tehnika 72, no. 4 (2017): 591–600. http://dx.doi.org/10.5937/tehnika1704591b.

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Rigou, Maria, Spiros Sirmakessis, and Athanasios Tsakalidis. "Integrating Personalization in E-Learning Communities." International Journal of Distance Education Technologies 2, no. 3 (2004): 47–58. http://dx.doi.org/10.4018/jdet.2004070105.

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Bezza, Assma, Amar Balla, and Farhi Marir. "Improving Personalization In E-Learning Systems." International Journal of Technology and Educational Marketing 4, no. 2 (2014): 75–84. http://dx.doi.org/10.4018/ijtem.2014070107.

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Individual learners have different requirements and characteristics, and as a result learning content should be able to be personalized and adaptable to the e-learner' profile. Little research work undertaken to tackle this issue, and it has been limited to ad-hoc work on personalizing, and adapting learning content in e-Learning. This paper presents two methods for modeling user profile and for personalizing and adapting a given content to match that profile: inductive (without user intervention) and deductive (with user intervention). These methods will be used as a base to review and classi

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Essalmi, Fathi, Leila Jemni Ben Ayed, Mohamed Jemni, Kinshuk, and Sabine Graf. "Selection of appropriates E-learning personalization strategies from ontological perspectives." Interaction Design and Architecture(s), no. 9_10 (September 20, 2010): 65–84. http://dx.doi.org/10.55612/s-5002-009_10-005.

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When there are several personalization strategies of E-learning, authors of courses need to be supported for deciding which strategy will be applied for personalizing each course. In fact, the time, the efforts and the learning objects needed for preparing personalized learning scenarios depend on the personalization strategy to be applied. This paper presents an approach for selecting personalization strategies according to the feasibility of generating personalized learning scenarios with minimal intervention of the author. Several metrics are proposed for putting in order and selecting usef

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Woo, Mihye, and Kyounghee Ko. "Personalization as a Teaching and Learning Device to Enhance Communication Competence and Personalization in the Elementary English Curriculum and Textbook." Korea Association of Primary English Education 29, no. 1 (2023): 79–109. http://dx.doi.org/10.25231/pee.2023.29.1.79.

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Acknowledging the value of personalization as an effective teaching and learning device to enhance competence in communication, this study aims to examine the 2015 Revised Primary English Curriculum and textbooks to investigate the contents and activities related to personalization. The study examined the curriculum related to speaking and writing for the contents that can be linked to personalization. It also examined three most popular textbook series of grades 3 through 6 to analyze the frequency, language function areas, the periods of a unit, and the types of personalization activities. T

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Vorobchikova, E. O. "Personalization as a key principle of adult learning in digitalization conditions." Vestnik of Minin University 13, no. 1 (2025): 1. https://doi.org/10.26795/2307-1281-2025-13-1-1.

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Introduction. The article is devoted to the study of the principle of personalization and its application in the design of educational programs for adult learners. During the research scientific sources, experience and speeches of educational content developers were analyzed in order to clarify the interpretation of the term «personalization». The relevance of educational programs of additional professional education, including those implemented using distance learning technologies, is considered.Materials and methods. In the process of the research, the scientific literature was analyzed and

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Chris, Ruhupatty. "Technology as the Embodiment of the Personalized Essence of Nature and Its Evolution in Learning Machines." International Journal of Recent Innovations in Academic Research 9, no. 2 (2025): 1–5. https://doi.org/10.5281/zenodo.15151710.

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This article examines the philosophy of technology through the lens of the principle of personalization. It explores the terminology of technology, tracing its linguistic origins and the evolution of its concept from the classical era to modern and contemporary contexts. According to the principle of personalization, technology does not replicate the structure of nature but rather embodies its personalization. Consequently, technology does not carry the essence of nature itself but retains residual traces from the process of personalization. Within this perspective, learning machines are not d

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Graf, Alex. "Exploring the Role of Personalization in Adaptive Learning Environments." International Journal Software Engineering and Computer Science (IJSECS) 3, no. 2 (2023): 50–56. http://dx.doi.org/10.35870/ijsecs.v3i2.1200.

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: In recent years, the integration of technology in education has sparked interest in enhancing learning experiences. Adaptive learning environments, which customize educational content and strategies to individual learners, offer a promising approach. Personalization within adaptive learning environments is crucial for addressing learner diversity and maximizing engagement and learning outcomes. This research project aims to explore the role of personalization in adaptive learning environments and its impact on the user experience and educational outcomes. By investigating the effectiveness o

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Graf, Alex. "Exploring the Role of Personalization in Adaptive Learning Environments." International Journal Software Engineering and Computer Science (IJSECS) 3, no. 2 (2023): 50–56. http://dx.doi.org/10.35870/ijsecs.v3i2.1200.

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: In recent years, the integration of technology in education has sparked interest in enhancing learning experiences. Adaptive learning environments, which customize educational content and strategies to individual learners, offer a promising approach. Personalization within adaptive learning environments is crucial for addressing learner diversity and maximizing engagement and learning outcomes. This research project aims to explore the role of personalization in adaptive learning environments and its impact on the user experience and educational outcomes. By investigating the effectiveness o

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Sfenrianto and A. Hasibuan Zainal. "Step-Function Approach for E-Learning Personalization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 3 (2017): 1362–67. https://doi.org/10.12928/TELKOMNIKA.v15i3.4457.

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Personalization is an alternative to improve the learning process for an e-Learning environment. It is a useful strategy to adjust the student' needs based on their characteristics to make learning more effectively. In this study, we propose the step-function approach for personalization in e-learning. It provides the students with adopting the knowledge-ability factor (Novice, Average, or Good category) that matches with their learning materials levels (Level1, Level2, or Level3). The approach implemented into an e-learning which called SCELE-PDE and used as the experimental group in two

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Atanasov, Nikolay. "ABOUT THE ECONOMIC IDENTITY, LEARNING AND DIGITAL PERSONALISATION." International Conference on Technics, Technologies and Education, ICTTE 2019 (2019): 184–91. http://dx.doi.org/10.15547/ictte.2019.03.068.

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In the report is considered a few substantial issues of economic identity, learning and digital personalization in the contemporary society. There are interdisciplinary reflections to economic uncertainty, risk management and need to a new economic digital identity for optimal searches, analysis, substantial knowledge and smart innovative digital skills and personalization. By dialectical and cognitive phenomenological methods are revealed important points of value and analyzed critical determinants for development of knowledge management, digital personalization and economic identity. The res

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CZECZOTKOVÁ, Blanka, and Kateřina KOSTOLÁNYOVÁ. "PERSONALIZATION OF INSTRUCTION IN ELECTRONIC LEARNING ENVIRONMENT." Journal of Technology and Information 3, no. 2 (2011): 14–17. http://dx.doi.org/10.5507/jtie.2011.019.

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Suteja, Bernard Renaldy, Suryo Guritno, Retantyo Wardoyo, and Ahmad Asharit. "Ontology implementation within e-Learning Personalization System." CCIT Journal 3, no. 3 (2010): 322–44. http://dx.doi.org/10.33050/ccit.v3i3.702.

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Website adalah realisasi dari teknologi internet. Saat ini, seperti yang terlihat dari tren penggunaan, situs web telah berevolusi. Pada awalnya, situs hanya mengadopsi kebutuhan untuk mencari dan browsing informasi. Langkah awal peningkatan website sering dikenal sebagai teknologi 1,0 web. Saat ini, teknologi web 2.0 telah memungkinkan interaksi baik antar web-ke-web. Jenis interaksi seperti mengubah in formasi (sharing), dalam bentuk dokumen (SlideShare), gambar flickr), atau video (Youtube), informasi eksploitasi (wikipedia), dan juga penciptaan komunitas online (weblog, forum web) terutama

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Klašnja-Milićević, Aleksandra, and Mirjana Ivanović. "E-learning Personalization Systems and Sustainable Education." Sustainability 13, no. 12 (2021): 6713. http://dx.doi.org/10.3390/su13126713.

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In the World Declaration on Higher Education, the concept of higher education is defined as “all types of studies, training or research training at the postsecondary level, provided by universities or other educational establishments that are approved as institutions of higher education by the competent state authorities” [...]

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Sfenrianto, Sfenrianto, and Zainal A. Hasibuan. "Step-Function Approach for E-Learning Personalization." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 3 (2017): 1362. http://dx.doi.org/10.12928/telkomnika.v15i3.4457.

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Lee, Yugyung, Krishna Veerubhotla, Myung Ho Jeong, and Chi H. Lee. "Deep Learning in Personalization of Cardiovascular Stents." Journal of Cardiovascular Pharmacology and Therapeutics 25, no. 2 (2019): 110–20. http://dx.doi.org/10.1177/1074248419878405.

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Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principles of DL algorithms, existing DL software, and designing strategies of DL models. Latest progresses in cardiovascular devices, especially DL-based cardiovascular stent used for angioplasty, differential and advanced diagnostic means, and the treatment outcomes involved with coronary artery disease (CAD), are discussed. Also presented is DL-based di

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Ferretti, Stefano, Silvia Mirri, Catia Prandi, and Paola Salomoni. "Automatic web content personalization through reinforcement learning." Journal of Systems and Software 121 (November 2016): 157–69. http://dx.doi.org/10.1016/j.jss.2016.02.008.

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Christudas, Beulah Christalin La, Sujni Paul, Kirubakaran Ezra, and Saravanan Venkatraman. "Personalization of e-Learning Using Data Mining." International Journal of Learning: Annual Review 17, no. 4 (2010): 585–94. http://dx.doi.org/10.18848/1447-9494/cgp/v17i04/46972.

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Prasetya, Rizky Eka, and Novanda Alim Setya Nugraha. "ENGLISH EDUCATIONAL MOODLE-BASED ENVIRONMENT ON ACTUALIZING PERSONALIZATION VIRTUAL COURSE." Premise Journal of English Education 11, no. 2 (2022): 362. http://dx.doi.org/10.24127/pj.v11i2.4911.

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The study aims to explore, analyze, and determine the possibility of Moodle platform accommodating English learning personalization. The study applied a convergent parallel design mixed-method and was concerned with a quantitative survey and qualitative case study (deductive approach). The findings indicated and determined the personalization of English learning Moodle-based, and those elements included adjustable material and mechanism, concentrated instruction, component-based decisions, and English language learner observation. The adaptability Moodle system accommodated the personalization

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Gaikhe, Rushikesh. "Virtual Fashion with AI Personalization." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1170–79. https://doi.org/10.22214/ijraset.2025.70326.

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Abstract: The rise of e-commerce in the fashion industry hasledtoanincreaseddemandforvirtualtry-onsolutions that enhance user experience. This paper presents a Virtual Fashion with AI Personalization system leveraging Generative Adversarial Networks(GANs)and deep learning techniques to provide a realistic and interactive digital try-on experience. The system enables users to upload images, select garments, and visualize theminreal-timewithpersonalizedfitting.Thisapproach addresses challenges in online apparel shopping, including size misfit and lack of customization, through AI-driven garment

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Wistow, Gerald, Margaret Perkins, Martin Knapp, Annette Bauer, and Eva-Maria Bonin. "Circles of Support and personalization." Journal of Intellectual Disabilities 20, no. 2 (2016): 194–207. http://dx.doi.org/10.1177/1744629516637997.

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Circles of Support aim to enable people with learning disabilities (and others) to live full lives as part of their communities. As part of a wider study of the economic case for community capacity building conducted from 2012 to 2014, we conducted a mixed methods study of five Circles in North West England. Members of these Circles were supporting adults with moderate to profound learning disabilities and provided accounts of success in enabling the core member to live more independent lives with improved social care outcomes within cost envelopes that appeared to be less than more traditiona

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Thuan, Tran Quang, Nguyen De, and Nguyen Văn Toai. "Developing an adaptive learning platform based on artificial intelligence (AI) to personalize the learning experience." International Journal of Management and Organizational Research 3, no. 3 (2024): 34–38. http://dx.doi.org/10.54660/ijmor.2024.3.3.34-38.

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This study explores the development and user experience of an artificial intelligence (AI)-based adaptive learning platform to personalize the learning experience. The main objective of the study is to elucidate the factors that influence the success of platform implementation and evaluate its impact on learning personalization. Through qualitative research methods, including in-depth interviews, observations, and document analysis, data was collected from stakeholders such as developers, teachers, and students. The results show that close collaboration between technology, education, and user

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Cabrera-Amaiquema, Jefferson Eduardo, Irflin Yoiner Quintero-Moreira, Cinthya Marbel Oviedo-Bonilla, and Renata Paola Cervantes-Avilés. "EXPERIENTIAL LEARNING AND THE OPTIMIZATION OF LEARNING IN STUDENTS WITH NEURODEVELOPMENTAL DISORDERS THROUGH EDUCATIONAL PERSONALIZATION." Sapiendus 1, no. 1 (2025): 1–12. https://doi.org/10.70335/sapiendus.1.1.5.

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Introduction: This study examines the impact of experiential learning and educational personalization on students with neurodevelopmental disorders, such as ADHD, ASD, SLI, and dyscalculia, through innovative methodologies focused on educational inclusion. Objective: To evaluate how educational personalization, through strategies such as learning through play and plastic expression games, optimizes the learning process and reduces barriers in the educational environment. Methodology: The research was conducted in educational institutions in Guayaquil, employing a quantitative approach with dat

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Tudor, Ivan, Martina Holenko Dlab, and Nataša Hoić-Božić. "Personalized Learning in Secondary and Higher Education: A Systematic Literature Review of Technology-Enhanced Approaches." International Journal of Educational Methodology 11, no. 3 (2025): 359–75. https://doi.org/10.12973/ijem.11.3.359.

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The personalization of learning and teaching processes represents an advanced approach to education that adapts content, pace, and teaching methods to the individual needs and preferences of students. This approach relies on analyzing diverse student characteristics, such as their knowledge level, progress, learning style, and interests. Achieving these goals is significantly supported by the use of information and communication technology, which facilitates and enhances the implementation of personalization in technology-enhanced learning (TEL). The primary objective of personalization is to

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Webster, Ray, and Fay Sudweeks. "Personalization and learning: innovative approaches to teaching for e-learning." Psicologia Escolar e Educacional 11, spe (2007): 93–101. http://dx.doi.org/10.1590/s1413-85572007000300009.

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This paper suggests that for academics to be good teachers, especially in the context of e-Learning, they need to understand learning. This is especially important with the associated changes in higher education as we move towards the knowledge society. E-Learning is embedded in learning and, without an understanding of what learning encompasses, it can be difficult for academics to develop into good teachers. It is suggested that, although this may appear to be a simple aim, it is not necessarily understood or applied by university academics in their teaching. One inference is that university

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den Hengst, Floris, Eoin Martino Grua, Ali el Hassouni, and Mark Hoogendoorn. "Reinforcement learning for personalization: A systematic literature review." Data Science 3, no. 2 (2020): 107–47. http://dx.doi.org/10.3233/ds-200028.

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The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature re

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Mouri, Kousuke, Hiroaki Ogata, Noriko Uosaki, and Erdenesaikhan Lkhagvasuren. "Context-aware and Personalization Method based on Ubiquitous Learning Analytics." JUCS - Journal of Universal Computer Science 22, no. (10) (2016): 1380–97. https://doi.org/10.3217/jucs-022-10-1380.

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In the past decades, ubiquitous learning (u-learning) has been the focus of attention in educational research across the world. Majority of u-learning systems have been constructed using ubiquitous technologies such as RFID tags and cards, wireless communication, mobile phones, and wearable computers. There is also a growing recognition that it can be improved by utilizing ubiquitous learning logs collected by the u-learning system to enhance and increase the interactions among a learner, contexts, and context-based knowledge. One of the issues of analytics based on u-learning is how to detect

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Sai Swaroop Reddy, Venkata. "Leveraging Machine Learning for Personalization and Security in Content Management Systems." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 1943–46. https://doi.org/10.21275/sr24037110821.

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Benkovska, Natalia, Nataliia Kharchenko, Larysa Kulbach, Ganna Rabokorovka, and Tetiana Bolotnykova. "Analyzing pedagogical strategies for personalized learning to compensate for students' learning losses." Eduweb 18, no. 1 (2024): 235–44. http://dx.doi.org/10.46502/issn.1856-7576/2024.18.01.17.

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The article is a comprehensive study of current methods and approaches used in modern educational practice to support students facing educational difficulties. The purpose is to analyze the effectiveness of individualization and personalization of the educational process in restoring educational losses. The literature review allows us to cover the existing theoretical frameworks of individualization and personalization in education. The analysis of pedagogical practice includes case studies and expert interviews, which provides an overview of successful strategies in real educational scenarios

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Rohit Sharma. "Data-Driven Personalization : Revolutionizing User Experience." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 868–77. http://dx.doi.org/10.32628/cseit241051075.

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Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges,

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Ángel-Flores, Herminio Del, Eduardo López-Domínguez, Yesenia Hernández-Velázquez, Saúl Domínguez-Isidro, María Auxilio Medina-Nieto, and Jorge De La Calleja. "Mobile Learning Platform focused on Learning Monitoring and Customization: Usability Evaluation Based on a Laboratory Study." Proceedings of the Institute for System Programming of the RAS 34, no. 3 (2022): 89–110. http://dx.doi.org/10.15514/ispras-2022-34(3)-7.

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The learning customization and monitoring are considered key aspects of the teaching-learning processes. Some works have proposed mobile learning systems that provide teachers and students learning monitoring and personalization services. One of the main requirements of these kinds of systems in terms of software quality is usability; however, few works have addressed the usability issues using laboratory studies with users in real domains. In this work, we present a usability evaluation of the learning monitoring and personalization services of a mobile learning platform based on a laboratory

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Thajchayapong, Ploy, and Ashok K. Goel. "Personalized Learning through AI-Driven Data Pipeline." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 111–14. https://doi.org/10.1609/aaaiss.v5i1.35572.

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The integration of artificial intelligence (AI) in education holds significant promise for transforming personalized learning. By analyzing student learning data, AI systems can adapt instruction to meet individual needs through tailored content, adaptive learning paths, real-time feedback, and continuous improvement loops. However, effective personalization at scale demands not only access to large volumes of learner data but also robust data architectures to collect, organize, standardize, and analyze that data in a secure and meaningful way. However, note that the ability of AI to personali

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Kushnir, M. E., P. D. Rabinovich, K. E. Zavedenskiy, and I. S. Tsarkov. "Student’s Learning Profile as a Tool of Personal Learning Logistics." Vysshee Obrazovanie v Rossii = Higher Education in Russia 30, no. 12 (2022): 48–58. http://dx.doi.org/10.31992/0869-3617-2021-30-12-48-58.

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Increase of complexity and uncertainty as well as demand for personalization (including in education) urges universities to pay attention to educational subjectivity and its development; to transfer towards individual / collective-individual educational navigation and flexible systems of educational programs management (including formation of temporary learning groups, supply of required educational resources in due time, protocols of appraisal and mutual offset of educational results), what determines the relevance of the research. Usage of logistic approach enables to distinguish the pedagog

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Kushnir, Mikhail, Pavel Rabinovich, Kirill Zavedenskiy, and Igor Tsarkov. "Student's Learning Profile as a Tool of Personal Learning Logistics." Higher Education in Russia 30, no. 12 (2021): 48–58. https://doi.org/10.31992/0869-3617-2021-30-12-48-58.

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 Increase of complexity and uncertainty as well as demand for personalization (including in education) urges universities to pay attention to educational subjectivity and its development;to transfer towards individual / collective-individual educational navigation and flexible systems ofeducational programs management (including formation of temporary learning groups, supply ofrequired educational resources in due time, protocols of appraisal and mutual offset of educationalresults), what determines the relevance of the research. Usage of logistic approach enables to distinguish the pedag

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Burke, Victoria I., and Robin D. Burke. "Powerlessness and Personalization." International Journal of Applied Philosophy 33, no. 2 (2019): 319–43. http://dx.doi.org/10.5840/ijap202034131.

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Is privacy the key ethical issue of the internet age? This coauthored essay argues that even if all of a user’s privacy concerns were met through secure communication and computation, there are still ethical problems with personalized information systems. Our objective is to show how computer-mediated life generates what Ernesto Laclou and Chantal Mouffe call an “atypical form of social struggle.” Laclau and Mouffe develop a politics of contingent identity and transient articulation (or social integration) by means of the notions of absent, symbolic, hegemonic power and antagonistic transition

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Xiaoan, Zhan, Xu Yang, and Liu Yingchia. "Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience." International Journal of Engineering and Management Research 14, no. 5 (2024): 134–47. https://doi.org/10.5281/zenodo.14190245.

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This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental resu

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Kallus, Nathan, and Madeleine Udell. "Dynamic Assortment Personalization in High Dimensions." Operations Research 68, no. 4 (2020): 1020–37. http://dx.doi.org/10.1287/opre.2019.1948.

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Li, Shuyi, and Bairong Zhang. "Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning." ITM Web of Conferences 70 (2025): 03029. https://doi.org/10.1051/itmconf/20257003029.

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Hierarchical Federated Learning (FL) presents a novel approach that combines global model training with localized personalization fine-tuning to enhance the predictive accuracy of decentralized machine learning systems. Traditional FL methods, which allow multiple clients to collaboratively train a global model without sharing raw data, are hindered by issues such as non-independent and identically distributed (non-IID) data, communication overhead, and limited generalization across diverse client datasets. This study proposes a hierarchical model that mitigates these challenges by incorporati

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Mansor, Marzita, Wan Adilah Wan Adnan, and Natrah Abdullah. "Personalized Reading: Developing User-Describing Profile for Slow Learner Children." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 07 (2019): 103. http://dx.doi.org/10.3991/ijim.v13i07.10775.

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<p><span style="font-size: medium;"><span style="font-family: Times New Roman;">Personalization is a good supplement for learning process. It has been claimed that personalization has a huge potential of providing solution to facilitate the learning path based on children ability and preferences. Diverse research on personalized learning for children have been conducted which, are commonly concerns on the development and implementation of personalized learning products and services. However these researches have little emphasized in exploring slow learner personalized learnin

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Kravchenko, D. A., I. A. Bleskina, E. N. Kalyaeva, E. A. Zemlyakova, and D. F. Abbakumov. "Personalization in education: from programmed to adaptive learning." Современная зарубежная психология 9, no. 3 (2020): 34–46. http://dx.doi.org/10.17759/jmfp.2020090303.

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Adaptive learning is a learning service that adapts quickly and continuously to the individual characteristics of students. Our study is a literature review that includes a brief analysis of the history of development, the main modern approaches and methods of implementation, the educational potential of adaptive platforms and the directions of the future development of adaptive learning. The literature review allowed us to describe and analyze the main stages of learning development: from programmable to adaptive. Its results are aimed at helping researchers and developers gain a general and

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Aksenova, Alla. "Essential characteristics of personalization of learning: environmental approach." Man and Education, no. 4 (65) (2020): 43. http://dx.doi.org/10.54884/s181570410020462-3.

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The article discusses the essential characteristics of a personified learning environment as a condition and at the same time a result of personification of the educational process; reveals school practices of personification of teaching from the positions of organization of educational communication as a productive dialogue, nonlinearity of the educational process and eventfulness of teaching. The features of personification of education in the context of digitalization of education are analyzed.

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Bachari, Essaid El, El Hassan Abelwahed, and Mohammed El Adnani. "E-Learning personalization based on Dynamic learners' preference." International Journal of Computer Science and Information Technology 3, no. 3 (2011): 200–216. http://dx.doi.org/10.5121/ijcsit.2011.3314.

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Ertan Şevk, Kübra, and Selay Arkün Kocadere. "A Review on Personalization of Gamified Learning Environments*." Kuramsal Eğitimbilim 17, no. 4 (2024): 873–96. http://dx.doi.org/10.30831/akukeg.1478810.

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This study explores the personalization of gamification in educational contexts, particularly adaptive approaches to meet diverse learner needs. The study examines terminologies and offer definitions for adaptive, tailored, personalized, and customized gamification. Personalization serves as an umbrella term covering any changes in the process. Customization is the changes performed by the user/learner; tailored gamification involves adjustments made at the beginning and adaptive gamification involves system-driven changes as things progress. The analysis of adaptive gamification studies is st

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