Relevant bibliographies by topics / Personalized Mobile Learning

Contents

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

Academic literature on the topic 'Personalized Mobile Learning'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Personalized Mobile Learning"

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Harchay, Ahlem, Lilia Cheniti-Belcadhi, and Rafik Braham. "A Context-aware Approach for Personalized Mobile Self-Assessment." JUCS - Journal of Universal Computer Science 21, no. (8) (2015): 1061–85. https://doi.org/10.3217/jucs-021-08-1061.

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With the increasing development of mobile technologies, the learning environment is currently undergoing a major shift. Access to contextual information in a mobile learning environment aims to meet the needs of learning and assessment personalization according to various learners' profiles and a range of learning contexts. Semantic Web technologies have been applied in recent years with different purposes in education. But, their applications for generating useful personalized mobile assessment resources have not been researched enough so far. In this paper, an approach making use of semantic
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Radhakrishnan, Madhubala. "Personalized Mobile Learning and Course Recommendation System." International Journal of Mobile and Blended Learning 13, no. 1 (2021): 38–48. http://dx.doi.org/10.4018/ijmbl.2021010103.

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Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required content or materials in the desired format to the learner is essential to the learning management system. Also, it is very important to guide the learner based on their interest in learning. With this outset, the proposed mobile learning system helps the
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Cho, Sung-Jin, and Hwan-Mook Chung. "Intelligent Mobile Agents in Personalized u-learning." International Journal of Fuzzy Logic and Intelligent Systems 10, no. 1 (2010): 49–53. http://dx.doi.org/10.5391/ijfis.2010.10.1.049.

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Al-Razgan, Muna, and Hind Alotaibi. "Personalized Mobile Learning System to Enhance Language Learning Outcomes." Indian Journal of Science and Technology 12, no. 01 (2019): 1–9. http://dx.doi.org/10.17485/ijst/2019/v12i1/139871.

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Wang, Jianjiang, and Lingling Sun. "Impact of Personalized Learning Paths Supported by Mobile Technology on Student Academic Achievement." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 08 (2025): 174–87. https://doi.org/10.3991/ijim.v19i08.55339.

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With the rapid advancement of mobile technology, mobile learning has become an integral component of modern education, particularly in the design and implementation of personalized learning paths. These paths enable tailored learning content and strategies based on students’ interests, abilities, and progress, thereby enhancing knowledge acquisition and improving academic achievement. Recent studies on personalized learning paths have primarily focused on content recommendation and learning outcome assessment, whereas limited attention has been given to student interaction relationships. In mo
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Liu, Lichao. "The Impact of Mobile Applications on Personalized Learning Paths in Dance Education." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 05 (2025): 128–43. https://doi.org/10.3991/ijim.v19i05.54525.

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With the rapid development of smart mobile applications, mobile devices have become an essential learning tool in dance education. Traditional dance teaching methods often fail to effectively meet the individualized learning needs of students, especially in the dynamic learning and feedback of skills. As a result, the design and implementation of personalized learning paths have become a key issue in current dance education. Although existing research has preliminarily explored dance teaching on mobile platforms, problems still exist, such as inaccurate student dance posture assessments and in
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Zhao, Wen. "Intelligent Education Based on Mobile Learning: Transitioning from Traditional Classrooms to Adaptive Learning Environments." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 06 (2025): 33–47. https://doi.org/10.3991/ijim.v19i06.54707.

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With the rapid development of information technology, mobile learning has become a key means to enhance educational quality and facilitate personalized learning. Traditional classroom teaching models exhibit limitations in terms of personalization, adaptability, and flexibility. Mobile learning, on the other hand, offers the opportunity for learning anytime and anywhere, addressing the individualized needs of students. However, effectively integrating mobile learning with intelligent education technologies to create learning environments that cater to diverse student needs remains a significan
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Baba, Khalid, Nour-Eddine El Faddouli, and Nicolas Cheimanoff. "Mobile-Optimized AI-Driven Personalized Learning: A Case Study at Mohammed VI Polytechnic University." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 04 (2024): 81–96. http://dx.doi.org/10.3991/ijim.v18i04.46547.

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With the rise of mobile learning platforms, it has become increasingly evident that individuals require personalized experiences that are tailored to the strengths and limitations of mobile devices. The present study explores the significant impact that personalized mobile learning environments, powered by artificial intelligence (AI), could have. This study specifically evaluates the impact of an AI-driven personalized educational platform, designed for mobile devices, on the academic achievement and educational progress of students at Mohammed VI Polytechnic University. The platform, designe
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Yang, Ning. "Optimization of Personalized English Learning Paths through Mobile Interaction Technology." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 05 (2025): 195–209. https://doi.org/10.3991/ijim.v19i05.54527.

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With the development of information technology (IT), particularly the widespread application of mobile internet and smart devices, traditional methods of English language learning can no longer meet the personalized needs of modern learners. The design and recommendation of personalized learning paths have become key issues in enhancing learning outcomes. Current study primarily focuses on personalized recommendation systems based on big data and artificial intelligence (AI) algorithms. While these systems have achieved a certain degree of accuracy in recommending learners’ interests and learn
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PETERSEN, SOBAH ABBAS, JAN-KRISTIAN MARKIEWICZ, and SONDRE SKAUG BJØRNEBEKK. "PERSONALIZED AND CONTEXTUALIZED LANGUAGE LEARNING: CHOOSE WHEN, WHERE AND WHAT." Research and Practice in Technology Enhanced Learning 4, no. 1 (2009): 33–60. https://doi.org/10.58459/rptel.2009.433-60.

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Mobile and ubiquitous learning facilitates language learners to continue their learning process outside the formal classroom, when and where they desire. While more and more learning resources are accessible via a mobile device, there is a challenge in providing access to appropriate personalized learning resources. This paper describes the PALLAS system which enables real life language learning scenarios by providing personalized and contextualized access to learning resources via a mobile device. The support provided to the learner includes tasks that are personalized and contextualized and
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Dissertations / Theses on the topic "Personalized Mobile Learning"

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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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Youssef, Éliane. "A design model to personalize learning using mobile technology and devices." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAG018.

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Cette recherche définit la personnalisation comme une approche centrée sur l'apprenant où la personne est prise dans son ensemble (émotions, cognition et socialisation). L’apprenant est actif, prend des initiatives et collabore avec les autres. Elle détaille les attributs de l'apprentissage personnalisé, ses perspectives et ses théories et propose un modèle personnalisé testé via le développement d’une application mobile pour les cours de base en informatique pour les étudiants en première année universitaire. Les résultats portent principalement sur l'impact de la personnalisation sur la perf
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Gómez, Ardila Sergio Eduardo. "Learning design implementation in context-aware and adaptive mobile learning." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/116492.

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Mobile learning (m-learning) is still in its infancy, and great efforts should be made so as to investigate the potentials of an educational paradigm shift from the traditional one-size-fits-all teaching approaches to an adaptive learning that can be delivered via mobile devices. Thus, the next challenge has been identified from this implication: How learning design can be implemented so as to benefit from the m-learning characteristics and achieve adaptation and personalization of the individual learning process in different contexts? An important factor for achieving personalized and adaptiv
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Jotham, Victoria Anne. "iSpace? : identity & space : a visual ethnography with young people and mobile phone technologies." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/ispace-identity-and-space--a-visual-ethnography-with-young-people-and-mobile-phone-technologies(ab192610-3778-487f-bd7f-9a741dbddf56).html.

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Mobile phone technologies are transforming how young people think, work, play and relate to each other. However, a central concern for the thesis is that education policy and practice far too often resembles an industrial model that is standardised, mechanistic and linear and that rarely reflects the informational, dynamic and creative lives of young people. In particular, the educational project fails to connect with the way young people use their mobile phone technologies to multi-task, connect, and create content at a precipitous rate. This thesis focuses on the ways in which mobile phone t
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Hu, Xinyu. "Personalized Policy Learning with Longitudinal mHealth Data." Thesis, 2019. https://doi.org/10.7916/d8-94k8-1490.

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Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it
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Chang, Chun, and 張鈞. "Personalized Activity Generator in Mobile Context-awareness Role Playing Game for Museum Learning." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/84658156248690868390.

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碩士<br>中原大學<br>資訊工程研究所<br>97<br>Most of the classroom learning is teacher lead which students cannot observe and touch learning objects in real environments. Informal learning is different from traditional learning. Students can select learning objects by themselves in informal learning. Museum learning is informal learning. The knowledge of museums is so abundant that that are chosen to be the learning environment by this study. This study tries to achieve three goals: (1) Mobility: Students in an informal learning environment like a museum always keep moving around and learn relevant knowledg
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Liu, Yao-Chang, and 劉曜彰. "Automated Negotiation in Multiple e-Marketplaces by Using Learning Personalized Mobile Trading Agents." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/b56gby.

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碩士<br>朝陽科技大學<br>資訊管理系碩士班<br>92<br>A new kind of trading platform, e-Marketplace, was therefore born for the sake of great advance on Internet and Information Technology that dramatically and vastly changed the way traditional trading was conducted. As a result, B2C-based and C2C-based websites became popular recently. The same merchandise is sold by different sellers and the similar need is proposed by different buyers in different e-Marketplaces. The trading platforms nowadays have resulted in information explosion that makes users hardly to retrieve and analyze overall merchandise informatio
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Zare, Saeed [Verfasser]. "Intelligent mobile learning interaction system (IMLIS) : a personalized learning system for people with mental disabilities / Saeed Zare." 2010. http://d-nb.info/1008809136/34.

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Lam, Pham Xuan, and 范藍. "PACARD: A PERSONALIZED ADAPTIVE CARD-BASED INTERFACE FOR MOBILE LEARNING APPLICATIONS DISTRIBUTED ON APP STORES." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ezwfya.

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博士<br>國立中央大學<br>資訊工程學系<br>106<br>This research proposes and evaluates PACARD (Personalize Adaptive CARD-based interface) that provides learning materials in an adaptive way. Basically, PACARD suggested and changed related cards over time in response to learners, thereby personalizing each user’s experiments. It combines card-based design and spaced repetition learning technique to output a personalized adaptive mechanism that enables learners to study, and review learning content efficiently. PACARD address the problems of mobile learning app distributed on the app stores, including the issues
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Wu, Ming-Yao, and 吳明耀. "The Design and Implementation of a Personalized and Adaptive Mobile Learning Mechanism – The Case of Learning Natural Science Experiment for Junior High School." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/42533062522847470709.

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碩士<br>國立屏東科技大學<br>資訊管理系<br>93<br>This research intends to design and to construct a workable learning management system. The system must support mobile learning, and have the mechanism of personalized and adaptive learning. We hope the system is able to establish a e-learning platform for high schools and primary schools in our own country, and provide the different learning way to make the adaptive learning come true. For above purposes, we present a mechanism of adaptive learning is consist of five functional modules as below: 1. A learning material structure consists of multiple directories
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Books on the topic "Personalized Mobile Learning"

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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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Virvou, Maria, and Efthimios Alepis. Object-Oriented User Interfaces for Personalized Mobile Learning. Springer, 2016.

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Virvou, Maria, and Efthimios Alepis. Object-Oriented User Interfaces for Personalized Mobile Learning. Springer, 2014.

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Springer. Object-Oriented User Interfaces for Personalized Mobile Learning. Springer London, Limited, 2014.

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Object Oriented User Interfaces For Personalized Mobile Learning. Springer-Verlag Berlin and Heidelberg GmbH &, 2014.

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Darcy, Alison, and Shiri Sadeh-Sharvit. Mobile Device Applications for the Assessment and Treatment of Eating Disorders. Edited by W. Stewart Agras and Athena Robinson. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190620998.013.27.

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Mobile devices and applications (apps) are increasingly used in clinical practice, offering reconceptualization of and novel avenues to tracking symptoms and delivery of more personalized interventions. This chapter reviews the burgeoning approaches to the integration of mobile in screening and treating individuals with eating disorders. Promising methods of data collection such as ecological momentary assessments enhance the capabilities of detecting symptoms and recognizing patterns—both are fundamental to the screening, evaluation, and monitoring of eating disorders and lay the foundations
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McCartney, Kieran. Mobile Education : Personalised Learning and Assessment in Remote Education: A Guide for Educators and Learners. NBN International, 2021.

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McCartney, Kieran. Mobile Education : Personalised Learning and Assessment in Remote Education: A Guide for Educators and Learners. NBN International, 2021.

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McCartney, Kieran. Mobile Education : Personalised Learning and Assessment in Remote Education: A Guide for Educators and Learners. NBN International, 2021.

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McCartney, Kieran. Mobile Education : Personalised Learning and Assessment in Remote Education: A Guide for Educators and Learners. Lang AG International Academic Publishers, Peter, 2021.

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Book chapters on the topic "Personalized Mobile Learning"

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Madhubala, Radhakrishnan, and Akila. "Context-Aware Personalized Mobile Learning." In Intelligent Computing and Communication. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_45.

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Alepis, Efthimios, and Maria Virvou. "Mobile Affective Education." In Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3_10.

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Alepis, Efthimios, and Maria Virvou. "Mobile Student Modeling." In Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3_3.

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Tsai, Yi-Ting, Shih-Jou Yu, Xin-Yen Chen, Oscal Tzyh-Chiang Chen, Jerry Chih-Yuan Sun, and Ching-Chun Huang. "Personalized Mobile Learning System via Smart Glasses." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00410-1_17.

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Klímová, Blanka, and Pavel Pražák. "Mobile Blended Learning and Evaluation of Its Effectiveness on Students’ Learning Achievement." In Blended Learning: Educational Innovation for Personalized Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21562-0_18.

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Alepis, Efthimios, and Maria Virvou. "Mobile Authoring in Educational Software." In Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3_4.

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Alepis, Efthimios, and Maria Virvou. "Mobile Versus Desktop Educational Applications." In Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3_6.

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Alepis, Efthimios, and Maria Virvou. "Multiple Modalities in Mobile Interfaces." In Object-Oriented User Interfaces for Personalized Mobile Learning. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53851-3_7.

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Ko, Jin-hee, Chihoon Hur, and Hanil Kim. "A Personalized Mobile Learning System Using Multi-agent." In Web Information Systems Engineering – WISE 2005 Workshops. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11581116_15.

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Sampson, Demetrios G., and Panagiotis Zervas. "Context-Aware Adaptive and Personalized Mobile Learning Systems." In Ubiquitous and Mobile Learning in the Digital Age. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3329-3_1.

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Conference papers on the topic "Personalized Mobile Learning"

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Tkach, Galina, Nurassyl Kerimbayev, Vladimir Jotsov, Ivan Garvanov, and Aliya Akramova. "Mobile Education Agents:A New Paradigm for Personalized Learning." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705188.

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Chen, Yu-Chun, Hui-Nien Hung, Shun-Ren Yang, Yu-Chen Chou, and Phone Lin. "On Defending against Label Flipping Poisoning Attack for Personalized Federated Learning." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059648.

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Yan, Kai, Yuanfang Chen, Xing Fang, Guangxu Bian, and Noel Crespi. "A Distributed Personalized Federated Learning Method based on Siamese Neural Networks." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059662.

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Abdelmageed, Loay Nabil, Bassel Elsayed Fathy, Raghda Essam Ali, and Mohamed Labib Borham. "Enhancing Dietary Guidance with Machine Learning: A Stacking Classifier Approach to Personalized Nutrition." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783581.

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Kiriella, Anjali, Samadhi Rathnayake, and Thusithanjana Thilakarthna. "A Novel Mobile Application for Personalized Hair Care Using Advanced Machine Learning Techniques." In 2024 6th International Conference on Advancements in Computing (ICAC). IEEE, 2024. https://doi.org/10.1109/icac64487.2024.10851166.

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Hany, Nancy, Ramez Sherif, Kirollos Emad, Amr Emad, Maha Elsayed, and Hossam Abdelrahman. "Nexia Tutor: An AI-Powered Language Personalized Learning System for Kids with Dyslexia and Reading Challenges." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783640.

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Tselepatiotis, Michail, and Efthimios Alepis. "Adaptive Learning in Mobile Serious Games: A Personalized Approach Using AI for General Knowledge Quizzes." In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2024. https://doi.org/10.1109/iisa62523.2024.10786681.

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Bhat, Devidas, Vipashi Kansal, B. N. Mohan Kumar, Harshal Patil, Daxa Vekariya, and Sathish Kannan. "Personalized Health Monitoring Platform with Privacy Preservation in IoT Cloud Networks using Deep Learning." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714711.

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Changala, Ravindra, Archana Borde, R. Subhashini, Punit Pathak, Vuda Sreenivasa Rao, and B. Kiran Bala. "Sentiment Analysis in Mobile Language Learning Apps Utilizing LSTM-GRU for Enhanced User Engagement and Personalized Feedback." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718406.

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Saardloun, Punpaporn, Napacha Mahadumrongkul, Kanis Surajarus, et al. "Designing a User-Centric AI-Driven Mobile App for Personalized Time Management: Integrating Machine Learning and Design Thinking." In 2024 23rd International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2024. https://doi.org/10.1109/iscit63075.2024.10793695.

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Reports on the topic "Personalized Mobile Learning"

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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concep
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