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Journal articles on the topic 'Adaptive e-learning environment'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hajdari, Besnik. "The impact of information technology for online learning with the implementation and involvement of students in the creation of conditions and environment." Contemporary Research in Education and English Language Teaching 6, no. 1 (2024): 33–49. http://dx.doi.org/10.55214/26410230.v6i1.1131.

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Adaptive e-learning is considered a stimulus to assist learning and increase student engagement. Hence, building suitable adaptive e-learning environments helps to individualize education to reinforce learning goals. This work aims to create an adaptable e-learning environment based on students' learning styles and examine the effect of the adaptive e-learning environment on student engagement. This study also contrasts the suggested adaptive e-learning environment with the standard e-learning strategy. The suggested adaptive e-learning strategy and findings may assist e-learning institutions

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Fatahi, Somayeh. "An experimental study on an adaptive e-learning environment based on learner's personality and emotion." Education and Information Technologies 24, no. 4 (2019): 2225–41. https://doi.org/10.1007/s10639-019-09868-5.

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E-learning enables learners to learn everywhere and at any time but this kind of learning lacks the necessary attractiveness.&lrm; Therefore, adaptation is becoming increasingly important and the recent research interest in the adaptive e-learning system.&lrm; Since emotions and personality are important parts of human characteristics, and they play a significant role in parts of adaptive e-learning systems, it is essential to consider them in designing these systems.&lrm; This paper presents an empirical study on the impact of using an adaptive e-learning environment based on learner’s

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Abdulrahman Alzahrani, Abdulrahman Alzahrani, Muhammad Adnan Abdulrahman Alzahrani, Maha Aljohani Muhammad Adnan, Ala Abdulsalam Alarood Maha Aljohani, and M. Irfan Uddin Ala Abdulsalam Alarood. "Memory Load and Performance-based Adaptive Smartphone E-learning Framework for E-commerce Applications in Online Learning." 網際網路技術學刊 23, no. 6 (2022): 1353–65. http://dx.doi.org/10.53106/160792642022112306018.

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<p>The term e-commerce is not confined to the purchase and sale of goods only. There are several occasions where students are not able to comprehend any idea on their own. With the availability of online learning platforms such as Massive Open Online Courses (MOOCs) and Virtual Learning Environments (VLEs), students may visit online Internet sites, where various articles are made available with a single click. With mobile devices in their hands, they can also listen to the presentations of different academics from anywhere and at any time. In an online learning environment, it is challen

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Ewais, Ahmed, and Olga De Troyer. "Authoring Adaptive 3D Virtual Learning Environments." International Journal of Virtual and Personal Learning Environments 5, no. 1 (2014): 1–19. http://dx.doi.org/10.4018/ijvple.2014010101.

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The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the activities that he performs in the environment. In this paper, the authors discuss adaptive 3D virtual leaning environments and explain how a course author can specify such an environment (i.e., authoring). The approach and tool that the authors present allow

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Attia, M. R. ". Learning analytics technology in adaptive electronic learning environments." Pedagogicheskiy Zhurnal Bashkortostana 92, no. 2 (2021): 144–53. http://dx.doi.org/10.21510/1817-3292-2021-92-2-144-153.

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Adaptive e-learning environments are based on diversifying the presentation of content according to the learning styles of each learner, where the content is presented as if it is directed to each student separately, and activities and tests are presented so that they are sensitive to the different styles of learners and suitable for their mental abilities. These environments depend in their design on intelligence, therefore, these environments can analyze the characteristics and capabilities of learners, each separately, and this is done through learning analytics technology that helps in the

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Retalis, Symeon. "Creating Adaptive e-Learning Board Games for School Settings Using the ELG Environment." JUCS - Journal of Universal Computer Science 14, no. (17) (2008): 2897–908. https://doi.org/10.3217/jucs-014-17-2897.

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The use of digital games in education is well documented in the literature. They have been used in preschool, K-12, the university. A specific type of digital games is board games. Adding board games to the educational process can lead to an interactive stimulating learning experience. With a board game, players often learn from one another while at the same time having fun in a competitive environment. In this paper we propose the "ELG" game, an e-learning board game that adopts the basic elements of a racing board game but fosters students' creativity, problem-solving skills, and imagination

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Alzahrani, Nouf, Paul Newbury, and Phil L. Watten. "Self-Summarized Videos in Adaptive Collaborative E-Learning Environment." Journal of Information Technology and Application in Education 4 (2015): 1. http://dx.doi.org/10.14355/jitae.2015.04.001.

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Younes, Sayed S. "Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19." Journal of Healthcare Engineering 2021 (September 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/3928326.

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This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 leve

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M. El-Bakry, Hazem, and Ahmed A. Saleh. "Adaptive E-Learning Based on Learner's Styles." Bulletin of Electrical Engineering and Informatics 2, no. 4 (2013): 240–51. http://dx.doi.org/10.11591/eei.v2i4.189.

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In this paper, a new model for adaptive e-learning based on learner's styles is presented. In the previous work, the dimensions of learner's styles given by Felder-Silverman did not consider some important issues of the learner himself. Here, new learner's parameters such as his social environment, health conditions, psychological and economical states are taken into account. Such parameters greatly affect the ability of student to learn and understand. Therefore, in order to perfectly recognize the ability of the student to be interactive in the leaning environment and accept information, new

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Perera, M. P. L. "A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 37–42. http://dx.doi.org/10.35940/10.35940/ijeat.d2297.0410421.

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Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptiv

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Perera*, M. P. L. "A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 37–42. http://dx.doi.org/10.35940/ijeat.d2297.0410421.

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Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptiv

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M.P.L., Perera. "A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 4 (2021): 37–42. https://doi.org/10.35940/ijeat.D2297.0410421.

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Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptiv

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Бериев, И. Р., and Э. Д. Алисултанова. "ADAPTIVE LEARNING SYSTEM IN ELECTRONIC ENVIRONMENT." Вестник ГГНТУ. Технические науки, no. 4(26) (December 28, 2021): 5–12. http://dx.doi.org/10.34708/gstou.2021.13.11.001.

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Данная работа посвящена созданию адаптивной системы обучения в электронной среде. В ходе исследования сформулированы принципы, заложенные в концепцию проектирования такой системы. Представлена структура системы, которая включает модель предметной области (образовательного контента), модель пользователя, модель адаптации и модель оценки результатов обучения. В рамках предложенной системы разработан адаптивный электронный обучающий курс математической дисциплины. Обосновано, что успешное освоение такого курса обучающимися способствует формированию у них математической компетентности. The work is

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Muralidharan, S., and Latha Parthiban. "Adaptive E-Learning Using Soft Computing Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 5 (2020): 2057–59. http://dx.doi.org/10.1166/jctn.2020.8849.

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E-Learning is gaining more importance in the present education system and methodology of learning are moving from instructor-orientation to learner-orientation thereby providing learner with flexible, efficient and personalized learning environment. In this paper, adaptive e-learning using various soft computing techniques needed for achieving adaptation in learning path in e-learning is discussed. Adaptive e-learning becomes necessary for slow learners and challenged people who take their own time for learning due to their impairment.

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KumarAgarwal, Santosh, and Madhavi Sinha. "Student Modeling in Distributed Adaptive Knowledge based E-Learning Environment." International Journal of Computer Applications 76, no. 14 (2013): 30–36. http://dx.doi.org/10.5120/13317-0935.

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Ahmed, Moizuddin, Nazir Ahmed Sangi, and Amjad Mahmood. "A Model of Adaptive E-Learning in an ODL Environment." Mehran University Research Journal of Engineering and Technology 37, no. 2 (2018): 367–82. http://dx.doi.org/10.22581/muet1982.1802.13.

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Karthikeyan, D., and S. Ganeshmoorthy. "An adaptive and intelligent e-learning environment using fuzzy logic." ASIAN JOURNAL OF MULTIDIMENSIONAL RESEARCH 10, no. 4 (2021): 738–49. http://dx.doi.org/10.5958/2278-4853.2021.00359.1.

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Mahajan, Renuka, J. S. Sodhi, Vishal Mahajan, and Richa Misra. "Comparative study of mining algorithms for adaptive e-learning environment." International Journal of Logistics Economics and Globalisation 6, no. 2 (2014): 102. http://dx.doi.org/10.1504/ijleg.2014.068271.

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Siddique, Ansar, Qaiser S. Durrani, and Husnain A. Naqvi. "Developing Adaptive E-Learning Environment Using Cognitive and Noncognitive Parameters." Journal of Educational Computing Research 57, no. 4 (2018): 811–45. http://dx.doi.org/10.1177/0735633118769433.

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Romi, Ismail M. "Adaptive E-Learning Systems Success Model." International Journal of Emerging Technologies in Learning (iJET) 18, no. 18 (2023): 177–91. http://dx.doi.org/10.3991/ijet.v18i18.42929.

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E-learning implementation is growing alongside the e-learning market’s growth. As a result, competing models and approaches were proposed to improve e-learning and learner performance; one of these models is the e-learning systems success model. In order to expand this model, an analysis of the published e-learning models and frameworks in the period 2017–2022 was conducted using the aggregative review method. 37 studies that match the current study interests have been selected and analyzed. The main results support the e-learning system success model. In addition to further constructs other t

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Zargane, Kawtar, Mohamed Erradi, and Mohamed Khaldi. "E-learning adaptive and the tools of screenwriting: a case of collaboration." Global Journal of Engineering and Technology Advances 7, no. 3 (2021): 203–12. https://doi.org/10.5281/zenodo.5074421.

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The purpose of this article is to contribute to enriching the models are intended to be promoted teachers and trainers reuse of pedagogical scenarios. We offer some of the steps of the scenarios of learning at the level of education and e learning. We also study tools lesson plan in teaching online, which can be provided to practitioner’s new ways. In addition, as a computing environment in terms of collaboration, we are particularly interested in the phase teaching situation collaborative, the purpose of which is to describe the use of simulations free in a learning

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Joseph, Lumy, and Sajimon Abraham. "An adaptive learning frame work for slow learners in an e-learning environment." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 407–12. http://dx.doi.org/10.26438/ijcse/v6i7.407412.

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Joseph, Lumy, and Sajimon Abraham. "Identifying the Learning Path of Online Learners in an Adaptive E-Learning Environment." International Journal of Computer Sciences and Engineering 06, no. 06 (2018): 67–73. http://dx.doi.org/10.26438/ijcse/v6si6.6773.

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Premlatha, K. R., and T. V. Geetha. "Learning content design and learner adaptation for adaptive e-learning environment: a survey." Artificial Intelligence Review 44, no. 4 (2015): 443–65. http://dx.doi.org/10.1007/s10462-015-9432-z.

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Seki, Kazuya, Tatsunori Matsui, and Toshio Okamoto. "An adaptive sequencing method of the learning objects for the e-learning environment." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 88, no. 3 (2004): 54–71. http://dx.doi.org/10.1002/ecjc.20163.

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Ibam, Emmanuel Onwuka, Olumide Sunday Adewale, Oluwatoyin Catherine Agbonifo, and Ibrahim Makinde Akindeji. "MODELING OF AN ADAPTIVE E-LEARNING SYSTEM FOR IMPROVED LEARNING PERFORMANCE." IJISCS (International Journal of Information System and Computer Science) 7, no. 1 (2023): 8. http://dx.doi.org/10.56327/ijiscs.v7i1.1422.

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Majority of the online learning systems in use today, lack proper integration of adaptive, collaborative, personalized and ubiquitous concepts in their design and implementation. Integration of these basic concepts in online learning systems will enable adaptation, individualization, and collaboration of learning resources to learners’ preferences, with an added advantage of accessibility to online resources anywhere and anytime. Hence, the research proposes an Adaptive E-Learning System (AES) model that incorporates activities sequencing in a personalised, adaptive, collaborative and ubiquito

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Madhu Sudhana, Kalla. "Contextual Diversity and Rule-based Adaptive E-learning System Scheme." International Journal of Emerging Technologies in Learning (iJET) 10, no. 5 (2015): 72. http://dx.doi.org/10.3991/ijet.v10i5.4799.

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In an adaptive e-learning environment, the system must respond harmoniously to the changes of learner needs. The traditional e-learning systems provide adaptation based on user preferences only. To improve performance, it is required to incorporate learning context information such as the device and network context to determine the appropriate presentation method along with the user preferences. Mainly this paper discussed about the basic categories of context in e-learning environment and proposed an architectural overview of ontology driven rule-based adaptive system for obtaining effective

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Katsaris, Iraklis, and Nikolas Vidakis. "Adaptive e-learning systems through learning styles: A review of the literature." Advances in Mobile Learning Educational Research 1, no. 2 (2021): 124–45. http://dx.doi.org/10.25082/amler.2021.02.007.

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The domain of education has taken great leaps by capitalizing on technology and the utilization of modern devices. Nowadays, the established term "one size fits all" has begun to fade. The research focuses on personalized solutions to provide a specially designed environment on the needs and requirements of the learner. The adaptive platforms usually use Learning Styles to offer a more effective learning experience. This review analyzes the learner model, adaptation module, and domain module, originating from the study of 42 papers published from 2015 to 2020. As more modern techniques for ada

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Vainshtein, Yulia V., Roman V. Esin, and Victoria A. Shershneva. "Individualization of teaching mathematical logic in the electronic environment." Perspectives of Science and Education 47, no. 5 (2020): 147–59. http://dx.doi.org/10.32744/pse.2020.5.10.

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The rapid development of digital technologies and the direction of education modernization, which takes into account the individual characteristics of students, allows us to talk about the need to ensure the individualization of the educational process in the electronic information and educational environment. Adaptive e-learning courses are a promising means of individualizing the educational process, providing the construction of individual learning trajectory based on the level of academic performance, perceptual modality and the level of student activity in the electronic environment. The

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Praveenkumar, Babu, M.Thangamani, and Chandra Sekar P. Dr. "An Adaptive Learning Path Optimization Algorithm for Personalized E-Learning." Recent Trends in Information Technology and its Application 8, no. 1 (2024): 1–13. https://doi.org/10.5281/zenodo.13756290.

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<em>In the rapidly evolving landscape of digital education, there is a growing need for personalized e-learning experiences that cater to individual learners' unique needs and preferences. Traditional e-learning systems often fail to adapt to the dynamic requirements of learners, resulting in decreased engagement and suboptimal learning outcomes. To address this challenge, we propose an Adaptive Learning Path Optimization Algorithm (ALPOA) designed to create personalized learning paths that dynamically adjust based on real-time learner data. The methodology involves integrating machine learnin

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Sharma, Richa, Punam Bedi, and Hema Banati. "Stigmergic agent-based adaptive content sequencing in an e-learning environment." International Journal of Advanced Intelligence Paradigms 5, no. 1/2 (2013): 59. http://dx.doi.org/10.1504/ijaip.2013.054673.

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Megahed, Mohammed, and Ammar Mohammed. "Modeling adaptive E-Learning environment using facial expressions and fuzzy logic." Expert Systems with Applications 157 (November 2020): 113460. http://dx.doi.org/10.1016/j.eswa.2020.113460.

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Villesseche, Julie, Olivier Le Bohec, Christophe Quaireau, et al. "Enhancing reading skills through adaptive e-learning." Interactive Technology and Smart Education 16, no. 1 (2019): 2–17. http://dx.doi.org/10.1108/itse-07-2018-0047.

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Purpose E-learning is part of instructional design and has opened a whole world of new possibilities in terms of learning and teaching. The purpose of this paper is to develop an adaptive e-learning platform that enhances skills from primary school to university learners. Two purposes converge here: a pedagogical one – offering new possibilities, especially in terms of teaching scenarios (blended learning); and a research one – confirming the effectiveness of an adaptive e-learning tool in the case of individualized cross-disciplinary competences, such as comprehension of implicit information

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Bimba, Andrew Thomas, Norisma Idris, Ahmed Al-Hunaiyyan, Rohana Binti Mahmud, and Nor Liyana Bt Mohd Shuib. "Adaptive feedback in computer-based learning environments: a review." Adaptive Behavior 25, no. 5 (2017): 217–34. http://dx.doi.org/10.1177/1059712317727590.

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Adaptive support within a learning environment is useful because most learners have different personal characteristics such as prior knowledge, learning progress, and learning preferences. This study reviews various implementation of adaptive feedback, based on the four adaptation characteristics: means, target, goal, and strategy. This review focuses on 20 different implementations of feedback in a computer-based learning environment, ranging from multimedia web-based intelligent tutoring systems, dialog-based intelligent tutoring systems, web-based intelligent e-learning systems, adaptive hy

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Colchester, Khalid, Hani Hagras, Daniyal Alghazzawi, and Ghadah Aldabbagh. "A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms." Journal of Artificial Intelligence and Soft Computing Research 7, no. 1 (2017): 47–64. http://dx.doi.org/10.1515/jaiscr-2017-0004.

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Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based up

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Bershadskiy, A. M., A. S. Bozhday, A. A. Gudkov, and Yu I. Evseeva. "Self-adaptation of e-learning software based on observing the information environment." Open Education 23, no. 3 (2019): 33–41. http://dx.doi.org/10.21686/1818-4243-2019-3-33-41.

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Purpose of the research. The purpose of the study is to develop a new universal method of self-adaptation of applied software systems used in the field of e-learning (E-Learning). Self-adaptation refers to the ability of a software application to change its own structure and behavior depending on external circumstances, which include, for example, the trainee’s personal characteristics, which is especially important for systems used in education. Such self-adaptive behavior should be sufficiently flexible and not be reduced to the choice of one of the many behavioral options predetermined by t

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Bielskis, Antanas Andrius, and Olegas Ramašauskas. "Applied modeling of the objects in the e-laboratory environment." Lietuvos matematikos rinkinys 43 (December 22, 2003): 185–89. http://dx.doi.org/10.15388/lmr.2003.32380.

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An attempt to implement adaptive learning and modeling possibilities in a distance learning environment is shown in this article. Flexible learning environment means the system able not only to present and manage learning material, collect information about the users, give statistics on them and learning material, but even able to adapt its behavior to the specific learner needs. Usually standard learning environments have nothing to offer in such a case. The possibilities to use such an object in distance education and to add this experience to the knowledge base of e-learning adviser by stud

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Oancea, Romana, Vlad-Andrei Barsan, and Iulian Bouleanu. "Adaptivity in E-Learning Systems." International conference KNOWLEDGE-BASED ORGANIZATION 24, no. 3 (2018): 66–69. http://dx.doi.org/10.1515/kbo-2018-0138.

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Abstract The differentiated designing of lessons from the virtual environment can have beneficial effects on the student. In order to achieve the objectives proposed in the course, the content should not only meet the student’s expectations but must be designed adaptively according to the defining elements of each learner. Adaptability in content is an essential factor in maintaining motivation, involving the student, and engaging him/her in the study. The paper analyses approaches for the adaptive building of content by identifying the elements that can influence the achievement of the object

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Puma, Elena Guillermina Martinez, Elmer Benito Rivera Mansilla, José Luis Arias Gonzáles, et al. "How universities have responded to E-learning as a result of Covid-19 challenges." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 3 (2022): 40–47. https://doi.org/10.21533/pen.v10.i3.641.

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E-learning environments designed with adaptive technology in mind can help students learn and retain information more effectively by enhancing their learning experience and increasing their level of engagement. Here, students' learning styles are considered in creating an adaptable online environment, and the effects on student engagement are examined. For the sake of this study, we've also attempted to describe and compare the suggested adaptive learning environment to an existing e-learning technique. With Covid-19 in the classroom, technology advancements have grown exponentially, and this

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Shaiful, Bakhtiar bin Rodzman, Abu Bakar Nordin, Choo Yun-Huoy, et al. "I-OnAR: A rule-based machine learning approach for intelligent assessment in an online learning environment." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 1021–28. https://doi.org/10.11591/ijeecs.v17.i2.pp1021-1028.

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Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what

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Gevorgyan, Suren. "The Use of Adaptive Learning Technologies in e-Learning for Inclusive Education: A Systematic Review." E-Learning Innovations Journal 2, no. 1 (2024): 90–107. http://dx.doi.org/10.57125/elij.2024.03.25.05.

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The ongoing transformation of the education system involves the introduction of adaptive e-learning technologies at all levels, including inclusive education. The purpose of this study is to conduct a systematic review of scientific papers on e-learning, inclusion, and adaptive technologies. For this purpose, the PRISMA research approach was used to search for relevant sources and to initially process them. Main scientific bases: Scopus and Google Scholar. Date range: 2014–2023. The search for materials was carried out in stages: first, all duplicates were eliminated, a screening was performed

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Zine, Othmane, Aziz Derouich, and Abdennebi Talbi. "IMS Compliant Ontological Learner Model for Adaptive E-Learning Environments." International Journal of Emerging Technologies in Learning (iJET) 14, no. 16 (2019): 97. http://dx.doi.org/10.3991/ijet.v14i16.10682.

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It has been proven that adopting the “one size fits one” approach has better learning outcomes than the “one size fits all” one. A customized learning experience is attainable with the use of learner models, the main source of variability, in adaptive educational hypermedia systems or any intelligent learning environment. While such a model includes a large number of characteristics which can be difficult to incorporate and use, several standards that were developed to overcome these complexities.  In this paper, the proposed work intents to improve learner’s model representation to meet

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SowmyashreeS, Shet N. Manas, DM Prashanth, P. A. Pramanth, and MS Raghunandana. "The Role of Adaptive AI in Skill Development:A Comprehensive Survey." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2024): 134–38. https://doi.org/10.5281/zenodo.14471771.

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E-learning systems facilitate accessible, flexible learning but often lack adaptive features tailored to individual student needs, leading to reduced effectiveness. Adaptive Intelligent Tutoring Systems (AITS) and Adaptive Hypermedia (AH) offer personalized support by monitoring student progress and adapting content and instruction. However, AITS excels in customized guidance, while AH generally provides technical resources without the same level of adaptation. The AI-Tutor system proposes integrating AITS and AH into an Adaptive Intelligent Tutoring System (AITS) to create a more effective e-

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van den Bergh, Mattis, Verena D. Schmittmann, Abe D. Hofman, and Han L. J. van der Maas. "Tracing the Development of Typewriting Skills in an Adaptive E-Learning Environment." Perceptual and Motor Skills 121, no. 3 (2015): 727–45. http://dx.doi.org/10.2466/23.25.pms.121c26x6.

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Premlatha, K. R., B. Dharani, and T. V. Geetha. "Dynamic learner profiling and automatic learner classification for adaptive e-learning environment." Interactive Learning Environments 24, no. 6 (2014): 1054–75. http://dx.doi.org/10.1080/10494820.2014.948459.

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Ambarka, Ali Elghali, and Hanan Ettaher Dagez. "Adaptive Learning and Thinking Style to Improve E-Learning Environment Using Neural Network (ALTENN) Model." International Journal of Applied Mathematics, Electronics and Computers 3, no. 4 (2015): 249. http://dx.doi.org/10.18100/ijamec.12490.

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Alshmrany, Sami. "Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model." Cluster Computing 25, no. 1 (2021): 523–36. http://dx.doi.org/10.1007/s10586-021-03403-3.

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Almohammadi, Khalid, Hani Hagras, Daniyal Alghazzawi, and Ghadah Aldabbagh. "Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms." Journal of Artificial Intelligence and Soft Computing Research 6, no. 2 (2016): 81–101. http://dx.doi.org/10.1515/jaiscr-2016-0008.

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Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learne

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Pu, Haitao, Jinjiao Lin, Yanwei Song, and Fasheng Liu. "Adaptive Device Context Based Mobile Learning Systems." International Journal of Distance Education Technologies 9, no. 1 (2011): 44–56. http://dx.doi.org/10.4018/jdet.2011010103.

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Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for mobile devices to adapt to e-learning is presented. To provide device-independence mobile learning services, a context-aware mobile learning approach is proposed. Firstly, the formal definitions of contexts and their influence on mobile learning services

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N.D, Oye, and A. Iahad N. . "E-Learning Barriers and Solutions to Knowledge Management and Transfer." Information Management and Business Review 3, no. 6 (2011): 366–72. http://dx.doi.org/10.22610/imbr.v3i6.953.

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This paper present a systematic overview of barriers and solutions of e learning in knowledge management (KM) and knowledge transfer (KT) with more focus on organizations. The review paper also discusses KT in organizational settings and KT in the field of e learning. Here, an e-learning initiative shows adaptive solutions to overcome knowledge transfer barriers. The paper concludes that the flow of knowledge depends on people and the social environment they operate in. Therefore E-Learning environments support knowledge transfer not only by helping learners to make sense of content, but also

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