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Journal articles on the topic 'And Personalized Learning'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Nadira, Benmedakhene, Derdour Makhlouf, and Mohamed Amroune. "Personalized Online Learning." International Journal of Web-Based Learning and Teaching Technologies 16, no. 6 (2021): 1–15. http://dx.doi.org/10.4018/ijwltt.20211101.oa8.

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The success of MOOC (massive open online courses) is rapidly increasing. Most educational institutions are highly interested in these online platforms, which embrace intellectual and educational objectives and provide various opportunities for lifelong learning. However, many limitations, such as learners' diversity, lack of motivation, affected learners' outcomes, which unfortunately raised the dropout rate. Thus, multiple solutions were afforded on MOOC platforms to tackle these common problems. This paper suggests a model outline of a customizable system Context-Driven Massive Open Online C

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Zhuhadar, Leyla, Phillip Coleman, and Scarlett Marklin. "Applicative Personalized Learning." International Journal of Knowledge Society Research 7, no. 4 (2016): 24–37. http://dx.doi.org/10.4018/ijksr.2016100103.

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As technology has advanced, educational establishments are faced with this question: Are we investing enough funds and resources to provide college students with effective learning tools that could help them succeed not only by alerting them when they are at risk, but also providing them with personalized remedies while studying? Recently, students became more familiar with the notion of recommendation systems through their experience with social media tools, but does education fit this paradigm? In this paper, the authors review the recent advancement of technologies in personalized learning.

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Johnsen, Susan K. "Implementing Personalized Learning." Gifted Child Today 39, no. 2 (2016): 73. http://dx.doi.org/10.1177/1076217516631073.

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Jeong, Kwangson. "The inquiring characters of Personalized Learning as a Model of Schooling." Journal of Curriculum Integration 14, no. 1 (2020): 1–21. http://dx.doi.org/10.35304/jci.14.1.01.

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Romero, Lucila, Milagros Gutierrez, and Laura Caliusco. "Learning path based-portfolio for personalized learning." Journal on Advances in Theoretical and Applied Informatics 3, no. 1 (2017): 62. http://dx.doi.org/10.26729/jadi.v3i1.2447.

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Nowadays, lot of human activities have been affected by the use of technology needed to change in order to adapted. Particularly, in education domain the changes that occurred were not enough to break the traditional schemas of teaching. In this way, more and more people claim for real remodeling. Mainly, one of the most important subject is the necessity to adapt the teaching process to the particular characteristics of students, in a king of personalized learning. This work presents a conceptual framework, which proposes learning path based-portfolio as a way to personalize education. This f

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Shvardak, M., and O. Popovych. "PERSONALIZED LEARNING USING ARTIFICIAL INTELLIGENCE." Scientific Herald of Sivershchyna. Series: Education. Social and Behavioural Sciences 2025, no. 1 (2025): 139–56. https://doi.org/10.32755/sjeducation.2025.01.139.

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The relevance of the topic lies in the need to modernize the educational process in accordance with the individual needs of education seekers. Artificial intelligence creates new opportunities for personalizing learning, which is a key direction in the development of modern education. The purpose of the article: to explore the possibilities of using artificial intelligence to personalize learning in today’s conditions. Methodology: analysis and synthesis of scientific literature to determine the essence of personalized learning; analysis of educational strategies using artificial intelligence;

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Bhatt, Umang, Valerie Chen, Katherine M. Collins, et al. "Learning Personalized Decision Support Policies." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14203–11. https://doi.org/10.1609/aaai.v39i13.33555.

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Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when will each form of support yield better outcomes? In this work, we posit that personalizing access to decision support tools can be an effective mechanism for instantiating the appropriate use of AI assistance. Specifically, we propose the general problem of learning a decision support policy that, for a given input, chooses which form of support to provide to decision-makers for whom we initially have no prior information. We develop Modiste, an interactive tool to learn personal

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Mithun, M. S., R. Gayathri, B. R. Nithish, Beegum R. Adabiya, and S. Safiya. "Personalized Learning Assistance for Slow Learners." Recent Trends in Androids and IOS Applications 6, no. 3 (2024): 33–37. https://doi.org/10.5281/zenodo.13304360.

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<em>The "Personalized Learning Assistance for Slow Learners" initiative introduces a groundbreaking platform meticulously crafted to meet the unique requirements of students in need of extra academic support. Featuring an intuitive interface equipped with secure facial recognition login, learners gain access to tailored subject lists aligned with their individual preferences and academic goals. Interactive quizzes and dynamically generated study materials adapt in real-time according to the student's performance, fostering deeper comprehension and addressing areas of weakness proactively. Adva

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McKenna, Barbara J. "Personalized Learning in the Era of Personalized Medicine." Critical Values 2, no. 3 (2009): 6–8. http://dx.doi.org/10.1093/criticalvalues/2.3.6.

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Ma, Dongjun. "Enhancing University Course Evaluation and Personalized Learning through Artificial Intelligence Technology." Yixin Publisher 1, no. 4 (2023): 83–89. http://dx.doi.org/10.59825/jet.2023.1.4.83.

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With continuous advancements in education and the rapid proliferation of artificial intelligence technology, leveraging AI to improve university course evaluation and personalize learning experiences has garnered significant interest. This article aims to investigate the effective utilization of artificial intelligence technology in enhancing university course evaluation and achieving personalized learning outcomes. Firstly, it provides an overview of the current landscape of university course evaluation and personalized learning while highlighting the limitations of existing methodologies. It

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Naphatsanan, Suwannawong, Nilsook Prachyanun, and Wannapiroon Panita. "Personalized Cognitive Counseling Process to Promote Digital Health." Education Quarterly Reviews 5, no. 4 (2022): 326–37. https://doi.org/10.31014/aior.1993.05.04.594.

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The objective of this research was as follows: 1) to synthesise the Personalized Cognitive Counseling Process to Promote Digital Health. 2) to develop the Personalized Cognitive Counseling Process to Promote Digital Health. 3) to evaluate the Personalized Cognitive Counseling Process to Promote Digital Health. The documentary research method was used in this study. 4) to adapt the Personalized Cognitive Counseling model for Digital Health. The results showed a model of Personalized Cognitive Counseling Process to Promote Digital Health which consisted of four steps: Step 1: Synthesis of the Pe

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محمد حسن خلیفه, زینب. "شخصنة التعلم Personalized learning". دراسات فى التعلیم الجامعى 39, № 39 (2018): 453–60. http://dx.doi.org/10.21608/deu.2018.49236.

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Richter, Florian. "Personalized Learning and Autonomy." Lecture Notes in Education Psychology and Public Media 1, no. 1 (2021): 349–53. http://dx.doi.org/10.54254/lnep.iceipi.2021234.

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In smart classrooms intelligent systems are embedded in the learning environment. The implementation of these systems is widely accepted to introduce a more personalized learning experience, but the educational context has yet not been fully scrutinized. The examined approaches fail to capture the complexity of the learning experience and underdetermine the pedagogical aspect. Therefore, directions are indicated and discussed to clarify the educational context of technological implementations.

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Soler Costa, Rebeca, Qing Tan, Frédérique Pivot, Xiaokun Zhang, and Harris Wang. "Personalized and adaptive learning." Texto Livre: Linguagem e Tecnologia 14, no. 3 (2021): e33445. http://dx.doi.org/10.35699/1983-3652.2021.33445.

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Education Technology advances many aspects of learning. More and more learning is taking place online. Learners’ learning behaviors, style, and performance can be easily profiled through learning analytics which collects their online learning footage. It enables and encourages educational research, learning software application development, and online education practices towards personalized and adaptive learning. As we continue to see personalized and adaptive learning progress, we must also pay attention to the negative impacts that feed into our research. In this paper, we will present our

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Walkington, Candace, Milan Sherman, and Elizabeth Howell. "Personalized Learning in Algebra." Mathematics Teacher 108, no. 4 (2014): 272–79. http://dx.doi.org/10.5951/mathteacher.108.4.0272.

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Jenkins, John M. "Properties of Personalized Learning." International Journal of Educational Reform 7, no. 1 (1998): 66–71. http://dx.doi.org/10.1177/105678799800700107.

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Kinshuk. "Guest editorial: personalized learning." Educational Technology Research and Development 60, no. 4 (2012): 561–62. http://dx.doi.org/10.1007/s11423-012-9248-3.

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Chen, Yibo, Chanle Wu, Xiaojun Guo, and Jiyan Wu. "Semantic Learning Service Personalized." International Journal of Computational Intelligence Systems 5, no. 1 (2012): 163–72. http://dx.doi.org/10.1080/18756891.2012.670528.

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Jian, Maher Joe Khan Omar. "Personalized learning through AI." Advances in Engineering Innovation 5, no. 1 (2023): None. http://dx.doi.org/10.54254/2977-3903/5/2023039.

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The realm of education is witnessing a transformative integration with Artificial Intelligence (AI), poised to redefine the contours of pedagogical strategies. Central to this transformation is the emergence of personalized learning experiences, where AI endeavors to tailor educational content and interactions to resonate with individual learners' unique needs, preferences, and pace. This paper delves into the multifaceted dimensions of AI-driven personalized learning, from its potential to enhance e-learning modules, the advent of AI-powered virtual tutors, to the ethical challenges it surfac

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Elazab, Mohamed. "AI-driven personalized learning." International Journal of Internet Education 22, no. 3 (2024): 6–19. http://dx.doi.org/10.21608/ijie.2024.350579.

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Yash, Vardhan, and Kumar Pandey Harsh. "AI in Personalized Learning." Journal of Research and Innovation in Technology, Commerce and Management Volume 2, Issue 4 (2025): 2462–66. https://doi.org/10.5281/zenodo.15302329.

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During my research, I discovered something fascinating about how AI is changing education. I looked at how smart technology is creating learning experiences that adapt to each student's needs - kind of like having a personal tutor that understands exactly how you learn best. I spent months gathering information from different schools and talking to students and teachers who use AI-powered learning tools. What I found was eye-opening: when AI helps personalize learning, students tend to learn faster and remember more. In fact, my research showed that students using AI-powered learning tools gen

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Gao, Jie, and Bo Song. "A Personalized e-Learning System Based on EGL." International Journal of Machine Learning and Computing 4, no. 5 (2014): 411–16. http://dx.doi.org/10.7763/ijmlc.2014.v4.446.

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Gupta, Charul, and Dr Srikanth V. "Machine Learning- Recommendation System for Personalized Investment Portfolios." International Journal of Research Publication and Reviews 5, no. 2 (2024): 3690–94. http://dx.doi.org/10.55248/gengpi.5.0224.0630.

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Naliaka, Edith. "Leveraging Artificial Intelligence For Personalized Lifelong Learning Pathways." International Journal of Research Publication and Reviews 6, no. 6 (2025): 11662–68. https://doi.org/10.55248/gengpi.6.0625.2368.

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Ramazonovich, Mirtursunov Obid. "Personalized learning in pathophysiology: adapting education to student needs." American Journal of Medical Sciences and Pharmaceutical Research 7, no. 3 (2025): 32–38. https://doi.org/10.37547/tajmspr/volume07issue03-05.

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Pathophysiology, a foundational subject in medical and health sciences education, explores the mechanisms underlying disease processes. Despite its importance, the complexity and volume of content often pose significant challenges for students, leading to varied learning outcomes. Traditional teaching methods, which adopt a uniform approach for all learners, frequently fail to address the diverse needs, backgrounds, and learning styles of students. Personalized learning, an innovative educational strategy that tailors instruction to individual learners, offers a promising solution to these cha

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Zhang, Jin, and Kamsin Amirrudin. "Personalized learning model based on machine learning algorithms." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 470–75. https://doi.org/10.11591/ijict.v13i3.pp470-475.

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Machine learning algorithms have been widely applied in the field of personalized learning within educational information technology. By leveraging big data analysis and data mining techniques, machine learning can help identify patterns and trends in students' learning behaviors, preferences, and performance. This information can then be used to tailor educational resources and experiences to meet the individual needs and unique characteristics of each learner. Machine learning has made great progress and achievements in the teaching process of universities, but there are also some shortcomin

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Marinagi, Catherine, and Christos Skourlas. "Blended Learning in Personalized Assistive Learning Environments." International Journal of Mobile and Blended Learning 5, no. 2 (2013): 39–59. http://dx.doi.org/10.4018/jmbl.2013040103.

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In this paper, the special needs/requirements of disabled students and cost-benefits for applying blended learning in Personalized Educational Learning Environments (PELE) in Higher Education are studied. The authors describe how blended learning can form an attractive and helpful framework for assisting Deaf and Hard-of-Hearing (D-HH) students to learn and communicate. The described blended learning experiment integrates face-to-face, synchronous, and asynchronous e-learning, bilingual teaching (oral language and Sign Language) in the mainstream class, teaching in parallel classes, and person

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Hackmann, Donald G., Joel R. Malin, Asia N. Fuller Hamilton, and Laura O’Donnell. "Supporting Personalized Learning through Individualized Learning Plans." Clearing House: A Journal of Educational Strategies, Issues and Ideas 92, no. 1-2 (2019): 63–70. http://dx.doi.org/10.1080/00098655.2019.1571990.

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Aldyandra, Indra Ari Irvan, and Saipul Annur. "ENHANCING PERSONALIZED LEARNING THROUGH ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION 5.0: A FRAMEWORK FOR ADAPTIVE LEARNING ENVIRONMENTS." Proceeding of International Conference on Education and Sharia 1 (August 31, 2024): 670–80. https://doi.org/10.62097/ices.v124.104.

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The digital transformation in education spurred by the Fourth Industrial Revolution has advanced teaching and learning methods through automation and digital technologies. However, Education 5.0 emphasizes the balance between technology and humanistic aspects, aiming to enhance personalized learning through human-machine collaboration. This article examines the application of artificial intelligence (AI) in creating a framework for adaptive learning environments that enable personalized learning. This study utilizes literature analysis to design and evaluate a personalized AI-based framework.

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Sivakumar, R. D. Assistant Professor Department of Computer Science, and S. Former Assistant Professor Department of Business Administration Brindha. "PERSONALIZED LEARNING THROUGH MICRO-LESSONS." Indian Journal of Research and Development Systems in Technologization 1, no. 3 (2024): 9–18. https://doi.org/10.5281/zenodo.11197319.

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<em>In the present time, the existence of new pedagogical methods has made personalized learning one of the most innovative approaches that are intended to cater to the personal needs and preferences of each learner. The core of personalized learning is the micro-lessons that are short and focused instructional modules that have specific learning objectives and the purpose to deliver them in a way that is efficient. This paper discusses the principles, advantages, difficulties, and consequences of the micro-lessons which are the micro-lessons at the time of the personalized learning. The facts

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Researcher. "D3ADVERT: DATA-DRIVEN DECISION MAKING FOR AD PERSONALIZATION THROUGH PERSONALITY ANALYSIS USING BILSTM NETWORK." International Journal of Artificial Intelligence and Data Science (IJADS) 1, no. 2 (2024): 24–39. https://doi.org/10.5281/zenodo.14059525.

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Personalized advertising holds greater potential for higher conversion rates compared to generic advertisements. However, its widespread application in the retail industry faces challenges due to complex implementation processes. These complexities impede the swift adoption of personalized advertisement on a large scale. Personalized advertisement, being a data-driven approach, necessitates consumer-related data, adding to its complexity. This paper introduces an innovative data-driven decision-making framework, D3Advert, which personalizes advertisements by analyzing personalities using a BiL

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Wang, Xizhe, Linjie Zhang, and Tao He. "Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning." Sustainability 14, no. 13 (2022): 7654. http://dx.doi.org/10.3390/su14137654.

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Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors to identify underachieving students in school-based online learning, which may obstruct timely guidance and impede learning performance. Exploring small-sample-supported learning performance prediction and personalized feedback methods is an urgent need to mitigate these shortcomings. Consequently, considering the problem of insufficient data, this

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Yao, Dixi, and Baochun Li. "PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16398–406. http://dx.doi.org/10.1609/aaai.v38i15.29576.

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Personalized federated learning is a new paradigm to address heterogeneous problems (e.g. issues with non-i.i.d. data) in federated learning. However, existing personalized federated learning methods lack standards for how personalized and shared parts of the models are designed. Sometimes, manual design can even lead to worse performance than non-personalization. As a result, we propose a new algorithm for personalized federated neural architecture search, called PerFedRLNAS, to automatically personalize the architectures and weights of models on each client. With such an algorithm, we can so

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Rafiqovna, Ashurova Nodiraxon. "THE IMPORTANCE OF PERSONALIZED LEARNING TECHNOLOGIES AND SOME RECOMMENDATIONS." American Journal of Philological Sciences 4, no. 6 (2024): 91–93. http://dx.doi.org/10.37547/ajps/volume04issue06-19.

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This article discusses personalized learning, which is now widely recognized by Methodists, educators, and mentors as a new approach to education that contributes to the professional development of all learners are conducted. In the personalized educational approach, it is taken into account that the student's personal experience, knowledge and habits are related to learning methods, and they can learn faster, understand new concepts more easily, and improve learning efficiency. Some recommendations are given that are expected to be effective as a result of such an approach to education.

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Jovana Jović, Jovana Jović, Miroslava Raspopović Milić Jovana Jović, and Svetlana Cvetanović Miroslava Raspopović Milić. "Multidimensional Concept Map Representation of the Learning Objects Ontology Model for Personalized Learning." 網際網路技術學刊 24, no. 5 (2023): 1043–54. http://dx.doi.org/10.53106/160792642023092405003.

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<p>This work presents the creation and representation of an ontology model for the domain knowledge used for learning objects. The purpose of the developed ontology model is to define relations between learning objects that can be applied for their effective search and visualization. As the number of learning objects increases, the representation of the knowledge domain becomes challenging. In this paper, the authors propose the application of multidimensional concept maps (MCMs) for domain knowledge representation. The definition of different attributes used in the ontology model allow

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Mudrák, Marián, Milan Turčáni, and Jaroslav Reichel. "Impact of Using Personalized E-Course in Computer Science Education." Journal on Efficiency and Responsibility in Education and Science 13, no. 4 (2020): 174–88. http://dx.doi.org/10.7160/eriesj.2020.130402.

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At current e-learning platforms, is often seen non-efficient usage of their possibilities when creating educational content. This article deals with the possibilities of using adaptive tools that are offered by learning management system (LMS) Moodle when creating a personalised e-course. The methodology created by the authors of the article for personalised e-course adjusts the study content based on characteristics of each student stated by his or her initial knowledge, learning style, and motivation. The article is aimed at the presentation of the created methodology and its impact on the l

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Alhawiti, Mohammed M., and Yasser Abdelhamid. "A Personalized e-Learning Framework." Journal of Education and e-Learning Research 4, no. 1 (2017): 15–21. http://dx.doi.org/10.20448/journal.509.2017.41.15.21.

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Aleinikova, Kseniya A. "Personalized Learning in the USA." Science and School, no. 1 (2021): 59–64. http://dx.doi.org/10.31862/1819-463x-2021-1-59-64.

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Çullhaj, Salian. "Key Features of Personalized Learning." European Journal of Multidisciplinary Studies 6, no. 2 (2017): 130. http://dx.doi.org/10.26417/ejms.v6i2.p130-132.

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The term personalized learning, or personalization, refers to a diverse variety of educational programs, learning experiences, instructional approaches, and academic-support strategies that are intended to address the distinct learning needs, interests, aspirations, or cultural backgrounds of individual students.

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Savina, N. V. "Methodological foundations of personalized learning." Science of the Person: Humanitarian Researches 14, no. 4 (2020): 82–90. http://dx.doi.org/10.17238/issn1998-5320.2020.14.4.10.

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Wenfang CHEN, Mingfeng Su, Xiaoguang He, and Yibo CHEN. "Personalized Learning Instant Support Service." Journal of Convergence Information Technology 8, no. 1 (2013): 578–87. http://dx.doi.org/10.4156/jcit.vol8.issue1.71.

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Kallio, Julie M., and Richard Halverson. "Distributed leadership for personalized learning." Journal of Research on Technology in Education 52, no. 3 (2020): 371–90. http://dx.doi.org/10.1080/15391523.2020.1734508.

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Feng, Zhou. "Personalized Learning Network Teaching Model." Physics Procedia 24 (2012): 2026–31. http://dx.doi.org/10.1016/j.phpro.2012.02.297.

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Chao, Qianwen, Jingjing Shen, and Xiaogang Jin. "Video-based personalized traffic learning." Graphical Models 75, no. 6 (2013): 305–17. http://dx.doi.org/10.1016/j.gmod.2013.07.003.

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S Subiksha Sri, Gna Harshini, R Kowsalya, Mrs. S. G. Janani Ratthna, and Dr. S. Kalaiselvi. "Personalized Learning Analytics Using AI." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1892–99. https://doi.org/10.47392/irjaeh.2025.0275.

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The increasing demand for personalized learning experiences in education has led to the development of a Personalized Learning Analytics system powered by AI and enhanced with Randomized Algorithm Design (RAD). The system forecasts results, evaluates student performance, and offers personalized learning trajectories. RAD maximizes prediction accuracy through random selection of past data, ensuring for the purpose of offering objective analysis. Students can track progress and receive personalized feedback, and instructors can track class performance through individual interfaces. Career guidan

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Sheng Cao, Sheng Cao, Songdeng Niu Sheng Cao, Guanghao Xiong Songdeng Niu, Xiaolin Qin Guanghao Xiong, and Pengfei Liu Xiaolin Qin. "Student Model and Clustering Research on Personalized E-learning." 網際網路技術學刊 22, no. 4 (2021): 935–47. http://dx.doi.org/10.53106/160792642021072204020.

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Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

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Raut, Anish, Aditya Pawar, and Khushal Sharma. "AI-Based Educational Avatars for Personalized Learning: A Review." International Journal of Research Publication and Reviews 6, no. 4 (2025): 17186–92. https://doi.org/10.55248/gengpi.6.0425.16139.

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Rishi Venkat, Darshana Suresh, Anu Rai, Shubham Metha, and Dyuti Dave. "Transforming learning through artificial intelligence: Evolution of guided learning systems." International Journal of Science and Research Archive 13, no. 2 (2024): 4334–40. https://doi.org/10.30574/ijsra.2024.13.2.2045.

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Artificial intelligence powered learning systems are transforming the learning landscape by using advanced algorithms and comprehensive data to deliver a highly personalized and adaptive learning experience. These systems analyze learner behavior, preferences, and outcomes at the macro and micro levels. This allows them to provide tailored content recommendations. adaptive assessment and tailored learning paths. Adaptive systems are those that are able to continuously learn from user interactions and feedback. An AI-powered recommendation system can dynamically adjust recommendations. This ens

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Zhang, Yan. "Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 16 (2024): 68–82. http://dx.doi.org/10.3991/ijim.v18i16.51009.

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With the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in

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