Relevant bibliographies by topics / And Personalized Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'And Personalized Learning'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "And Personalized Learning"

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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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Dissertations / Theses on the topic "And Personalized Learning"

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Ghali, Fawaz. "Social personalized e-learning framework." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35247/.

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This thesis discusses the topic of how to improve adaptive and personalized e-learning in order to provide novel learning experiences. A recent literature review revealed that adaptive and personalized e-learning systems are not widely used. There is a lack of interoperability between adaptive systems and learning management systems, in addition to limited collaborative and social features. First of all, this thesis investigates the interoperability issue via two case studies. The first case study focuses on how to achieve interoperability between adaptive systems and learning management syste
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Galozy, Alexander. "Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health." Licentiate thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44091.

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Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care.
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Feffer, Michael A. (Michael Anthony). "Personalized machine learning for facial expression analysis." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119763.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 35-36).<br>For this MEng Thesis Project, I investigated the personalization of deep convolutional networks for facial expression analysis. While prior work focused on population-based ("one-size-fits-all") models for predict
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Sheth, Beerud Dilip. "A learning approach to personalized information filtering." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37998.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (leaves 96-100).<br>by Beerud Dilip Sheth.<br>M.S.
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Dinh, The Canh. "Distributed Algorithms for Fast and Personalized Federated Learning." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30019.

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The significant increase in the number of cutting-edge user equipment (UE) results in the phenomenal growth of the data volume generated at the edge. This shift fuels the booming trend of an emerging technique named Federated Learning. In contrast to traditional methods in which data is collected and processed centrally, FL builds a global model from contributions of UE's model without sending private data then effectively ensures data privacy. However, FL faces challenges in non-identically distributed (non-IID) data, communication cost, and convergence rate. Firstly, we propose first-order
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Guelman, Leo. "Optimal personalized treatment learning models with insurance applications." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/289500.

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In many important settings, subjects can show significant heterogeneity in response to a stimulus or “treatment". For instance, a treatment that works for the overall population might be highly inefiective, or even harmful, for a subgroup of subjects with specific characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would benefit from it. The notion that “one size may not fit all" is becoming increasingly recognized in a wide variety of fields, ranging from economics to medicine. This
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Ozpolat, Ebru. "A Framework For A Personalized E-learning System." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610612/index.pdf.

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This thesis focuses on three of the main components of an e-learning system: Infrastructure model, data integration and personalization. For the infrastructure model, our aim is to get best use of heterogeneously structured, geographically distributed data resources. Therefore, a detailed analysis of the available infrastructure models is carried out and an open source reference implementation based on grid technology is implemented. Furthermore, a simple data integration mechanism is proposed for the suggested reference implementation. For personalization, a statistical algorithm is propose
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Nori, Nozomi. "Machine Learning Approaches for Personalized Clinical Risk Modeling." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225729.

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Metcalf, Shelia Elaine. "Redesigning High School through a Personalized Learning Model." Thesis, William Woods University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10742972.

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<p> Personalized learning is an evolving method for tailoring the goals of K-12 education to meet the needs of each student. This study supports education leaders in learning how to tailor education plans that maximize success for each student. This study extends learning about a better course of action for schools that might prevent students from being in credit lag and at risk of dropping out. This qualitative research study aims to outline themes about time, place and pace in earning high school credits, as well as combat the reasons why students drop out. Through a qualitative content anal
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Shen, Yan. "Personalized ontology learning for enhancing text mining effectiveness." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63699/1/Yan_Shen_Thesis.pdf.

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Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their nee
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Books on the topic "And Personalized Learning"

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Cheung, Simon, Fu Wang, Lam Kwok, and Petra Poulová. Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952.

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Kondrat'ev, Sergey. Theory and practice of personalized learning. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1098272.

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The monograph presents the psychological theory and organization of personalized learning in general education schools. The concept of integrative subjectivity of a person as a form of reflexive and areflexive self-existence is considered as the psychological basis of personalized learning. The author characterizes the personality and the social individual in the light of the humanitarian Christian paradigm: reveals the phenomenology of integrative subjectivity, its structural organization, levels and forms of development of the individual and the social individual. From the standpoint of the
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Cheung, Simon K. S., Lap-Kei Lee, Ivana Simonova, Tomas Kozel, and Lam-For Kwok, eds. Blended Learning: Educational Innovation for Personalized Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21562-0.

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Kinshuk. Designing Adaptive and Personalized Learning Environments. Routledge, 2016. http://dx.doi.org/10.4324/9781315795492.

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Venohr, Yannik. Deep Learning in Personalized Music Emotion Recognition. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46997-9.

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Alepis, Efthimios, and Maria Virvou. Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3.

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1964-, Ullman Diane, and Curtis Greg 1950-, eds. Learning personalized: The evolution of the contemporary classroom. Jossey-Bass Bass, a Wiley brand, 2015.

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Joseph, DiMartino, Wolk Denise 1959-, and Curtis Paul teacher, eds. The personalized high school: Making learning count for adolescents. Jossey-Bass, 2010.

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Metcalf, Linda. Solution-focused RTI: A positive and personalized approach to response to intervention. Jossey-Bass, 2010.

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Fisher, William P., and Paula J. Massengill, eds. Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3747-7.

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Book chapters on the topic "And Personalized Learning"

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Zhang, Xiaohong, Kenichi Kubota, Mayumi Kubota, and Kedong Li. "Designing Blended Learning Environments with Thinking Tool Strategies: Examining a Chinese Teacher's Decision-making and Beliefs." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-9.

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Li, Kam Cheong. "The Prospects of Personalized Learning." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-12.

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Nie, Yanjiao, Heng Luo, and Di Sun. "Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-10.

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Li, Kam Cheong, and Billy Tak-Ming Wong. "Features and Trends of Personalised Learning: A Review of Journal Publications from 2001 to 2018." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-2.

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Lertnattee, Verayuth, and Bunyapa Wangwattana. "Personalized Learning in Microscopic Method for Identification of Herbal Medicines." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-3.

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Zou, Di, Minhong Wang, Haoran Xie, Gary Cheng, Fu Lee Wang, and Lap-Kei Lee. "A Comparative Study on Linguistic Theories for Modelling EFL Learners: Facilitating Personalized Vocabulary Learning via Task Recommendations." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-7.

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Baranyi, Máté, and Roland Molontay. "Comparing the Effectiveness of Two Remedial Mathematics Courses Using Modern Regression Discontinuity Techniques." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-6.

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Shi, Yinghui, Jingman Zhang, Huiyun Yang, and Harrison Hao Yang. "Effects of Interactive Whiteboard-based Classroom Instruction on Students' Cognitive Learning Outcomes: A Meta-Analysis." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-8.

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Hui, Yan Keung, Lam For Kwok, and Horace H. S. Ip. "CRESDA: Towards a Personalized Student Advisory for Professional Development." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-11.

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Bureš, Vladimír, Pavel Čech, Martina Husáková, et al. "Use of Virtual Medical Cases as a Learning Tool in Medicine." In Personalized Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003448952-5.

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Conference papers on the topic "And Personalized Learning"

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Hajjir-Mesihovic, Nahla, and Belma Ramic-Brkic. "PERSONALIZED LEARNING APPROACH TO EDUCATION." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.0299.

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Sikarwar, Shailendra Singh, Sushma Shyam Bihari, Imtiyaz Ali, Divyansh Khandelwal, Sandeep Kapoor, and Shailendra Singh. "Machine Learning Techniques for Personalized E-Learning Systems." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895052.

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Wang, Zizhuo. "Research Advanced in Personalized Federated Learning." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012958400004508.

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Khan, Azal Ahmad, Ahmad Faraz Khan, Haidar Ali, and Ali Anwar. "Personalized Federated Learning Techniques: Empirical Analysis." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825575.

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Vijayasekaran, G., Jenifer J. Bibiana, Derangula Sankeerthana, Kuruva Soumya, D. Hema, and B. Kusuma. "Personalized Learning Platform using Artificial Intelligence." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859816.

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Samy, Khaled, Abdelrahman Osheba, Hala Shams El-Din, Reem M. Sallam, and Mohamed Abd Elaziz. "Personalized Nutrition Recommendations Using Machine Learning." In 2024 International Conference on Smart-Digital-Green Technologies and Artificial Intelligence Sciences (CSDGAIS). IEEE, 2024. https://doi.org/10.1109/csdgais64098.2024.11064829.

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Belkhouche, Boumediene, and Heba Ismail. "Personalized learning." In 2016 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2016. http://dx.doi.org/10.1109/educon.2016.7474651.

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Montebello, Matthew. "Personalized Learning Environments." In 2021 International Symposium on Educational Technology (ISET). IEEE, 2021. http://dx.doi.org/10.1109/iset52350.2021.00036.

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Mirzaei, Maryam, and Kourosh Meshgi. "The use of machine learning in developing learner-adaptive tools for second language acquisition." In EuroCALL 2023: CALL for all Languages. Editorial Universitat Politécnica de Valéncia, 2023. http://dx.doi.org/10.4995/eurocall2023.2023.16996.

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Advancements in artificial intelligence and machine learning present opportunities to revolutionize language learning tools with learner-adaptive capabilities. These technologies facilitate the creation of trainable systems that can interact with learners, offering personalized learning experiences tailored to individual needs, interests, proficiency levels, backgrounds, and native languages. This study explores the role of machine learning in developing personalized frameworks for second language learning, introducing the Partial and Synchronized Caption (PSC) tool as an example. PSC utilizes
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de la Chica, Sebastian, Faisal Ahmad, Qianyi Gu, et al. "A personalized learning environment." In the 2009 joint international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1555400.1555467.

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Reports on the topic "And Personalized Learning"

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Pinchuk, Olga. How artificial intelligence is revolutionizing learning. TVORY, 2023. https://doi.org/10.33407/lib.naes.id/eprint/738543.

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Abstracts of the report for the reporting conference. The field of education from the standpoint of applications of artificial intelligence systems. In particular: revolutionary changes in personalized learning, creation of automated training courses, improvement of support for students with special needs.
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Barnwal, Ashirwad. An R-based machine learning workflow with applications in personalized medicine. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-200.

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Oskolkov, Nikolay. Deep Learning for the Life Sciences. Instats Inc., 2024. https://doi.org/10.61700/zjxxse1x3u05y1846.

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This intensive workshop provides a comprehensive exploration of deep learning applications in life sciences, focusing on practical techniques for analyzing complex biological datasets. Participants will gain theoretical and hands-on experience with deep learning tools such as TensorFlow and Keras, learning to construct neural networks and apply them to areas like genomics and personalized medicine.
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Peters, Vanessa, Barbara Means, Maria Langworthy, et al. Enabling Analytics for Improvement: Lessons from Year 2 of Fresno’s Personalized Learning Initiative. Digital Promise, 2018. http://dx.doi.org/10.51388/20.500.12265/53.

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Now in its second year, the Fresno Unified School District’s Personalized Learning Initiative (PLI) continues to help teachers and students develop the skills, competencies and mindsets essential for “as yet imagined” futures. A unique aspect of Fresno’s PLI is its analytics partnership between Fresno Unified, Microsoft Education, Houghton Mifflin Harcourt and Digital Promise. This report describes the early success of the PLI on students’ learning outcomes, evidence on what elements of the implementation are working, and the process and principles of the analytics partnership. The report aims
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Cristia, Julian P., Santiago Cueto, Ofer Malamud, and Raphaëlle Aulagnon. Streaking to Success: The Effects of Highlighting Streaks on Student Effort and Achievement. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0012912.

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We examine whether highlighting streaks encourages 4th to 6th grade students in Peru to increase their use of an online math platform and improve learning. Sixty thousand students were randomly assigned to receive messages that i) highlighted streaks, ii) provided personalized reminders with positive reinforcement, or iii) provided generic reminders, while others were assigned to a control group. Highlighting streaks and providing personalized reminders significantly increased platform use compared to generic reminders and the control group, with streaks more effective on the intensive margin
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Shmulevich, Ilya, and Boris Aguilar. Prototyping a self-learning digital twin platform for personalized treatment in melanoma patients - Final Report. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1846670.

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Marienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4418.

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The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their ap
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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-dri
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Suarez Enciso, Sonia, Hyeri Mel Yang, and Gabriela Chacon Ugarte. Skills for Life Series: Self-awareness. Inter-American Development Bank, 2024. https://doi.org/10.18235/0013332.

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Self-awareness, the ability to monitor internal states and understand interactions with others, is a key socioemotional skill. It encompasses internal clarity and external understanding of how others perceive us. Linked to well-being, empathy, and self-regulation, self-awareness can be developed through reflective practices, feedback, and personalized learning. In this brief, we explore its significance, successful initiatives to cultivate self-awareness, and tools available to measure it.
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Brizard, Jean-Claude. Breaking With the Past: Embracing Digital Transformation in Education. Digital Promise, 2023. http://dx.doi.org/10.51388/20.500.12265/176.

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Today's businesses know that driving innovation is integral to succeeding not just in the present, but more importantly in the decades to come. Through the years, the innovation of digital technologies has transformed entire industries. Now it’s time to put those technologies to use and apply that same mentality to transform our schools. We need digital transformation of teaching and learning at scale across the United States. This report examines how the traditional one-size-fits-all approach to teaching and learning compares to more learner-centered, personalized frameworks; why we need to t
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