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Journal articles on the topic 'Adaptive educational hypermedia'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Santoianni, Flavia, and Alessandro Ciasullo. "Adaptive Design for Educational Hypermedia Environments and Bio-Educational Adaptive Design for 3D Virtual Learning Environments." Research on Education and Media 10, no. 1 (June 1, 2018): 30–41. http://dx.doi.org/10.1515/rem-2018-0005.

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Abstract Adaptive learning environments design has been originally influenced by the adaptive learning environments model, which has been recently re-shaped by the bio-educational adaptive approach. Adaptive and bio-educational models share the common main idea that education should be adaptive. Since the 90’s the adoption of an adaptive educational point of view have been at the base of adaptive educational hypermedia systems, which design joins interest towards learners’ individual differences with adaptive learning environments research. Educational hypermedia systems have been overcome by the technology of 3D Virtual Learning Environments. Some emerging questions are related to the design criteria of adaptive learning environments. Which lessons learned from adaptive hypermedia systems design could be now applied to VLEs’ design? How a virtual learning environment should be designed to be adaptive? This research tries to answer to these questions by describing Federico 3DSU, an educational University 3D Virtual Learning Environment which has been designed with adaptive criteria, according to bio-educational model

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De Bra, Paul. "Adaptive educational hypermedia on the web." Communications of the ACM 45, no. 5 (May 2002): 60–61. http://dx.doi.org/10.1145/506218.506247.

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Henze, Nicola, and Wolfgang Nejdl. "A logical characterization of adaptive educational hypermedia." New Review of Hypermedia and Multimedia 10, no. 1 (June 2004): 77–113. http://dx.doi.org/10.1080/13614560410001728128.

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Kahraman, H. Tolga, Seref Sagiroglu, and İlhami Colak. "A novel model for web-based adaptive educational hypermedia systems: SAHM (supervised adaptive hypermedia model)." Computer Applications in Engineering Education 21, no. 1 (July 1, 2010): 60–74. http://dx.doi.org/10.1002/cae.20451.

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Drissi, Samia, and Abdelkrim Amirat. "An Adaptive E-Learning System based on Student's Learning Styles." International Journal of Distance Education Technologies 14, no. 3 (July 2016): 34–51. http://dx.doi.org/10.4018/ijdet.2016070103.

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Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's learning achievement. The findings support the use of learning styles as guideline for adaptation into the adaptive e-learning hypermedia systems.

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Hendrix, Maurice, Alexandra Cristea, and Wolfgang Nejdl. "Authoring adaptive educational hypermedia on the semantic desktop." International Journal of Learning Technology 3, no. 3 (2007): 230. http://dx.doi.org/10.1504/ijlt.2007.015443.

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Spallek, H. "Adaptive Hypermedia: A New Paradigm for Educational Software." Advances in Dental Research 17, no. 1 (December 2003): 38–42. http://dx.doi.org/10.1177/154407370301700110.

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Traditional online dental education courses follow the broadcast paradigm which centers on the teacher, not the student. This one-size-fits-all approach resembles a mass-production idea which cannot take individual learner characteristics into account. Most online course designs do not address the issue that users with different goals and knowledge may be interested in different pieces of information about a topic. Adaptive hypermedia (AH) is an emerging field in education research which investigates how computer systems can overcome this problem. AH can be applied to any course content. This learner-centered approach first considers the learning goal(s), then evaluates the user’s abilities and determines the individual learning style, to structure and tailor the curriculum most efficiently. The presented AH environment exploits various concepts of AH. The system collects data to create a model of the individual user, which is continuously refined based on test results throughout the course. The system then adapts the learning material dynamically, using active and passive curriculum sequencing and adaptive presentation.

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Chittaro, Luca, and Roberto Ranon. "Adaptive Hypermedia Techniques for 3D Educational Virtual Environments." IEEE Intelligent Systems 22, no. 4 (July 2007): 31–37. http://dx.doi.org/10.1109/mis.2007.63.

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KOBSA, ALFRED, JÜRGEN KOENEMANN, and WOLFGANG POHL. "Personalised hypermedia presentation techniques for improving online customer relationships." Knowledge Engineering Review 16, no. 2 (March 2001): 111–55. http://dx.doi.org/10.1017/s0269888901000108.

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This article gives a comprehensive overview of techniques for personalised hypermedia presentation. It describes the data about the computer user, the computer usage and the physical environment that can be taken into account when adapting hypermedia pages to the needs of the current user. Methods for acquiring these data, for representing them as models in formal systems and for making generalisations and predictions about the user based thereon are discussed. Different types of hypermedia adaptation to the individual user's needs are distinguished and recommendations for further research and applications given. While the focus of the article is on hypermedia adaptation for improving customer relationship management utilising the World Wide Web, many of the techniques and distinctions also apply to other types of personalised hypermedia applications within and outside the World Wide Web, like adaptive educational systems.

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Qodad, Aymane, Abdelilah Benyoussef, Abdallah El Kenz, and Mourad Elyadari. "Toward an Adaptive Educational Hypermedia System (AEHS-JS) based on the Overlay Modeling and Felder and Silverman’s Learning Styles Model for Job Seekers." International Journal of Emerging Technologies in Learning (iJET) 15, no. 08 (April 24, 2020): 235. http://dx.doi.org/10.3991/ijet.v15i08.10574.

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In this paper we introduce a new design of an adaptive educational hypermedia system for job seekers, this proposal is based, for the part of learning objectives, on a job model which allows adapting the content and the path of education to the intended jobs, and, for the learner model construction, on a specific use of the learning styles of Felder and Silverman. First, we present existing literature to give a general review on adaptive edu-cational hypermedia systems, in that way; we have reported the related items to different notions in the adaptive educational Systems area as the differentiated pedagogy, the learning objects, and the learner profile. Then we argued our choice of the components of our model and we detailed the new ones. As designed, the model can produce a suitable learning path for the user to match the job characteristics and the learning style of the person in order to help the user owning the job sought. With the possibility of linking the required com-petencies to the education skills, we aim to map business tasks to learning activi-ties. Based on this approach, we designed an Adaptive Educational Hypermedia System named AEHS-JS that will help to improve the efficiency and pragmatism of job search activities. In plus of the social impact of this work as it help job seekers to complete their profiles and get the career they are looking for, this work will allow companies to find the candidates that match the job criteria sought.

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Özaydın Aydoğdu, Yıldız, and Nursel Yalçın. "A Web Based System Design for Creating Content in Adaptive Educational Hypermedia and Its Usability." Malaysian Online Journal of Educational Technology 8, no. 3 (July 1, 2020): 1–24. http://dx.doi.org/10.17220/mojet.2020.03.001.

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HOLZ, HILARY J., KATJA HOFMANN, and CATHERINE REED. "UNOBTRUSIVE USER MODELING FOR ADAPTIVE HYPERMEDIA." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 02 (March 2007): 225–44. http://dx.doi.org/10.1142/s0218001407005430.

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We propose a technique for user modeling in Adaptive Hypermedia (AH) that is unobtrusive at both the level of observable behavior and that of cognition. Unobtrusive user modeling is complementary to transparent user modeling. Unobtrusive user modeling induces user models appropriate for Educational AH (EAH) based on metaphors characterized by a shared locus of control over learning, such as small group learning. Transparent user modeling, on the other hand, induces user models appropriate for EAH based on metaphors characterized by an independent locus of control, such as social navigation. We extend an existing decomposition model of adaptation in AH to incorporate summative evaluation of unobtrusive user modeling. Summative evaluation examines the effects or outcomes of a system, while formative evaluation is used to improve a system under development. We also introduce a separate, two-stage model for formative evaluation of unobtrusive user modeling. We then present results from a field study for the first of the two developmental stages and describe the current field study of the second developmental stage. Finally, we compare unobtrusive and transparent user modeling, and explain the role of each.

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Bachov, I. "THE USE OF ADAPTIVE AND INTELLECTUAL EDUCATIONAL TECHNOLOGIES IN PREPARING OF MODERN SPECIALISTS." Visnyk Taras Shevchenko National University of Kyiv. Military-Special Sciences, no. 1 (2019): 15–18. http://dx.doi.org/10.17721/1728-2217.2019.41.15-18.

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In the development of educational software, there are ongoing dynamic changes that affect the market of educational services on the Internet. The current state of the distance education system requires the latest educational technology to effectively meet the needs of students who study virtually.The article defines how intelligent systems are used in the training of modern specialists through the system of distance learning.Key words: educational systems, intellectual systems, student, internet, hypermedia.

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Papadimitriou, Alexandros, Maria Grigoriadou, and Georgios Gyftodimos. "Interactive Problem Solving Support in the Adaptive Educational Hypermedia System MATHEMA." IEEE Transactions on Learning Technologies 2, no. 2 (April 2009): 93–106. http://dx.doi.org/10.1109/tlt.2009.19.

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Bouchboua, Ahmed, Rabah Ouremchi, and Mohammed El ghazi. "Educational Content Development Process in "CleverUniversity": Our Dynamic Adaptive Hypermedia Environment." International Journal of Emerging Technologies in Learning (iJET) 11, no. 12 (December 8, 2016): 14. http://dx.doi.org/10.3991/ijet.v11i12.5868.

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The implementation of a simple and flexible dynamic adaptive hypermedia environment meets several needs of teachers. Using a fitting system, teachers will be able to share their knowledge in chunks of content in order to create quality teaching resources. In this paper, we propose a development approach through our simple system dedicated to teachers and learners alike taking into account the different work done on the subject. Moreover, our approach is based on the various elements of our system such as the learner’s model, the domain model, the pedagogical model, the courses generator and the multimedia database. Our focus in this paper will be on the domain model given its importance in the development and adaptation of educational content as needed, and which consists of finding educational content related to a given field of knowledge to be adapted for a particular learner.

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Abraham-Curto, Javier. "Contributions to Improve Online Learning Through Adaptive Hypermedia Techniques." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 21, Special-Issue (June 1, 2013): 1–6. http://dx.doi.org/10.2478/rput-2013-0001.

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Abstract The personalization of learning through the automatic adaptation of the educational process, especially for the contents, is regarded as an important and necessary step for the improvement of education in the future. It is believed that through individualization get improved efficiency, increased speed and quality of learning for students and by automating the process, improved efficiency, better resource utilization, scalability of the process learning. To achieve this goal we have developed different strategies. One of them are looking for Adaptive Hypermedia Systems adjust all contents in a atuomática to the characteristics of each user.

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Azevedo, Roger, Jennifer G. Cromley, Fielding I. Winters, Daniel C. Moos, and Jeffrey A. Greene. "Adaptive Human Scaffolding Facilitates Adolescents’ Self-regulated Learning with Hypermedia." Instructional Science 33, no. 5-6 (November 2005): 381–412. http://dx.doi.org/10.1007/s11251-005-1273-8.

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Levacher, Killian, Seamus Lawless, and Vincent Wade. "Slicepedia: Content-agnostic slicing resource production for adaptive hypermedia." Computer Science and Information Systems 11, no. 1 (2014): 393–417. http://dx.doi.org/10.2298/csis121223014l.

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The production of resources supporting the needs of Adaptive Hypermedia Systems (AHSs) is labor-intensive. As a result, content production is focused upon meeting the needs of resources with higher demand, which limits the extent to which numerous and diverse content requirements of AHSs can be met. Open Corpus Slicing attempts to convert the wealth of information available on the web, into customisable information objects. This approach could provide the basis of an open corpus supply service meeting more diverse and unpredictable content requirements of AHSs. This paper takes a case study approach, focusing on an educational sector of adaptive hypermedia, to test out the effect of using Slicepedia, a service which enables the reuse and customisation of open corpus resources. An architecture and implementation of the system is presented along with two user-trial evaluations, involving 91 participants, which suggest that slicing techniques represent a valid content production supply for AHSs.

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Medina-Medina, Nuria, and Lina García-Cabrera. "A taxonomy for user models in adaptive systems: special considerations for learning environments." Knowledge Engineering Review 31, no. 2 (March 2016): 124–41. http://dx.doi.org/10.1017/s0269888916000035.

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AbstractThis paper outlines the educational advantages provided by adaptive systems and stresses the importance of the user model in the task of adaptation. The paper also establishes a general taxonomy of user models thereby enabling classification of any model according to various features related to its structure and management. This taxonomy allows an exhaustive description of all user models, facilitating analysis and design of the corresponding adaptive systems. Subsequently, the taxonomy is used to describe the user modeling process in a concrete adaptive hypermedia system and to analyze the model of a student appropriate for use in a general educational environment.

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Ahmadaliev, Doniyorbek Kambaraliyevich, Asilbek Abduvaliyevich Medatov, Mansurbek Mirkomilovich Jo'rayev, and Nodirbek Toxirjonovich O'rinov. "ADAPTIVE EDUCATIONAL HYPERMEDIA SYSTEMS: AN OVERVIEW OF CURRENT TREND OF ADAPTIVE CONTENT REPRESENTATION AND SEQUENCING." Theoretical & Applied Science 71, no. 03 (March 30, 2019): 58–61. http://dx.doi.org/10.15863/tas.2019.03.71.7.

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21

Tsortanidou, Xanthippi, Charalampos Karagiannidis, and Adamantios Koumpis. "Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules." International Journal of Emerging Technologies in Learning (iJET) 12, no. 05 (May 31, 2017): 150. http://dx.doi.org/10.3991/ijet.v12i05.6967.

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This paper investigates the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles in order to accommodate user's learning style preferences and needs. Therefore, AES adapt the learning content, its presentation and navigation to the user's learning style preferences. We collect thirty three (33) Adaptive and Intelligent Web-based Educational Systems (AIWBES) that incorporate learning styles and discuss twenty of them, namely the AEHS, as the remaining are Intelligent Tutorng Systems. The main achievement of this work is the investigation of AEHS' pedagogical basis in terms of adaptation rules. We conclude that these systems follow similar patterns in their adaptation logic.

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BOUSSAKUK, Mohammed. "A Fully individualized Adaptive and Intelligent Educational Hypermedia System: Details of CleverUniversity." International Journal of Emerging Trends in Engineering Research 8, no. 5 (May 25, 2020): 1497–502. http://dx.doi.org/10.30534/ijeter/2020/04852020.

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23

A., Nafisa, and Noureldien A. "A Multi-Trait Dynamic User Model for Adaptive Educational Hypermedia Systems (AEHS)." International Journal of Computer Applications 179, no. 38 (April 28, 2018): 1–6. http://dx.doi.org/10.5120/ijca2018916716.

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Engelhardt, Michael, Arne Hildebrand, Dagmar Lange, and Thomas C. Schmidt. "Semantic overlays in educational content networks – the hylOs approach." Campus-Wide Information Systems 23, no. 4 (August 1, 2006): 254–67. http://dx.doi.org/10.1108/10650740610704126.

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PurposeThe paper, aims to introduce an educational content management system Hypermedia Learning Objects System (hylOs), which is fully compliant to the IEEE LOM eLearning object metadata standard. Enabled through an advanced authoring toolset, hylOs allows the definition of instructional overlays of a given eLearning object mesh.Design/methodology/approachIn educational content management, simple file distribution is considered insufficient. Instead, IEEE LOM standardised eLearning objects have been well established as the basic building blocks for educational online content. They are nicely suited for self‐explorative learning approaches within adaptive hypermedia applications. Even though eLearning objects typically reside within content repositories, they may propagate metadata relations beyond repository limits. Given the explicit meaning of these interobject references, a semantic net of content strings can be knotted, overlaying the repository infrastructure.FindingsBased on a newly introduced ontological evaluation layer, meaningful overlay relations between knowledge objects are shown to derive autonomously. A technology framework to extend the resulting semantic nets beyond repository limits is also presented.Research limitations/implicationsThis paper provides proof of concept for the derivation and use of semantic content networks in educational hypermedia. It thereby opens up new directions for future eLearning technologies and pedagogical adoption.Practical implicationsThe paper illustrates capabilities of the hylOs eLearning content management. The hylOs is built upon the more general Media Information Repository (MIR) and the MIR Adaptive Context Linking Environment (MIRaCLE): its linking extension. MIR is an open system supporting the standard XML, CORBA and JNDI. hylOs benefits from manageable information structures, sophisticated access logic and high‐level authoring tools like the eLO editor responsible for the semi‐manual creation of meta data and WYSIWYG like XML–content editing, allowing for rapid distributed content development.Originality/valueOver the last few years, networking technologies and distributed information systems have moved up the OSI layer and are established well within application‐centric middleware. Most recently, content overlay networks have matured, incorporating the semantics of data files into their self‐organisational structure with the aim of optimising data‐centric distributed indexing and retrieval. This paper elaborates a corresponding concept of semantic structuring for educational content objects. It introduces and analyses the autonomous generation and educational exploitation of semantic content nets, providing proof of concept by a full‐featured implementation within the hylOs educational content management system.

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Queirós, Ricardo, Paulo Leal, and José Campos. "Sequencing educational resources with Seqins." Computer Science and Information Systems 11, no. 4 (2014): 1479–97. http://dx.doi.org/10.2298/csis131005074q.

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Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

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Meccawy, Maram, Craig D. Stewart, and Helen Ashman. "Adaptive educational hypermedia interoperability and content creation with a web service-based architecture." International Journal of Learning Technology 3, no. 3 (2007): 269. http://dx.doi.org/10.1504/ijlt.2007.015445.

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Botsios, Sotirios, and Dimitrios A. Georgiou. "Recent Contributions to a Generic Architecture Design that Supports Learning Objects Interoperability." International Journal of Distance Education Technologies 7, no. 4 (October 2009): 17–37. http://dx.doi.org/10.4018/jats.2009071002.

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Adaptation and personalization services in e-learning environments are considered the turning point of recent research efforts, as the “one-size-fits-all” approach has some important drawbacks, from the educational point of view. Adaptive Educational Hypermedia Systems in World Wide Web became a very active research field and the need of standardization arose, as the continually augmenting research efforts lacked interoperability capabilities. This article concentrates and classifies recent research work and notices important points that can lead to an open, modular and generic architecture of a Learning Management System based on widely accepted standards.

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Tadlaoui, Mouenis Anouar, Rommel N. Carvalho, and Mohamed Khaldi. "The Development of a Learner Model in Adaptive Hypermedia Educational Systems: A Comparative Study." International Journal for Infonomics 11, no. 2 (June 30, 2018): 1763–71. http://dx.doi.org/10.20533/iji.1742.4712.2018.0178.

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Al-Azawei, Ahmed, and Atta Badii. "State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)." International Journal of Computer Science and Information Technology 6, no. 3 (June 30, 2014): 01–19. http://dx.doi.org/10.5121/ijcsit.2014.6301.

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Colace, Francesco, Massimo De Santo, and Luca Greco. "E-Learning and Personalized Learning Path: A Proposal Based on the Adaptive Educational Hypermedia System." International Journal of Emerging Technologies in Learning (iJET) 9, no. 2 (March 18, 2014): 9. http://dx.doi.org/10.3991/ijet.v9i2.3211.

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Rodríguez, Arnoldo. "Teacher Model to Personalize Support for the Implementer Teacher in Authoring an Educational Adaptive Hypermedia." International Journal for Cross-Disciplinary Subjects in Education 2, no. 3 (September 1, 2011): 463–71. http://dx.doi.org/10.20533/ijcdse.2042.6364.2011.0064.

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Tsianos, N., Z. Lekkas, P. Germanakos, C. Mourlas, and G. Samaras. "An Experimental Assessment of the Use of Cognitive and Affective Factors in Adaptive Educational Hypermedia." IEEE Transactions on Learning Technologies 2, no. 3 (July 2009): 249–58. http://dx.doi.org/10.1109/tlt.2009.29.

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Akbulut, Yavuz, and Cigdem Suzan Cardak. "Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011." Computers & Education 58, no. 2 (February 2012): 835–42. http://dx.doi.org/10.1016/j.compedu.2011.10.008.

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Khaldi Maha, Erradi Omar, Erradi Mohamed, and Khaldi Mohamed. "Design of educational scenarios of activities in a learning situation for online teaching." GSC Advanced Engineering and Technology 1, no. 1 (May 30, 2021): 049–64. http://dx.doi.org/10.30574/gscaet.2021.1.1.0028.

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Through this work, based on research works concerning e-learning in particular works dealing with e_learning project management systems, adaptive hypermedia systems and decision-making systems on the one hand. Work on the impact of project-based learning on self-regulation in teaching and the design of pedagogical scenarios for classical education and in particular for modular online education on the other hand. Our work concerns the design and scripting of a modular online education by proposing different possible scenarios according to the nature of the learning or assessment activities linked to a learning situation without taking into account the nature of the discipline and the nature of the concept to be addressed.

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BONTCHEVA, KALINA, and VANIA DIMITROVA. "EXAMINING THE USE OF CONCEPTUAL GRAPHS IN ADAPTIVE WEB-BASED SYSTEMS THAT AID TERMINOLOGY LEARNING." International Journal on Artificial Intelligence Tools 13, no. 02 (June 2004): 299–331. http://dx.doi.org/10.1142/s0218213004001569.

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This paper discussed the use of Conceptual Graphs (CGs) for implementing tasks employed in web-based educational systems that aid terminology learning. Specifically, we focus on two critical issues in intelligent tutoring - student diagnosis and generation of adaptive explanations. Both tasks are demonstrated in terminological domains where learners have to familiarize themselves with concepts in a specific subject area (e.g. computing, finance, chemistry). Based on CG reasoning, robust and computationally tractable algorithms for student modelling and adaptive explanation generation are defined. Two intelligent systems are presented — STyLE-OLM and HYLITE+. STyLE-OLM is an interactive learner modelling system that extracts extended models of the learners' cognition. HYLITE+ is a natural language generation system that generates adaptive Web pages based on a learner model(LM). The two systems are complementary and have been implemented separately. However, considered together they cover most of the key tasks in adaptive web-based educational hypermedia that aid learning technical terminology. Based on evaluative studies of STyLE-OLM and HYLITE+, the use of CGs for interactive open student modelling and adaptive concept explanations is examined. The applicability of CGs in adaptive web-based systems that aid learning technical terminology is discussed.

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Mwambe, Othmar Othmar, Phan Xuan Tan, and Eiji Kamioka. "Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization." Education Sciences 10, no. 2 (February 17, 2020): 42. http://dx.doi.org/10.3390/educsci10020042.

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Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

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Tsortanidou, Xanthippi, Charalampos Karagiannidis, and Adamantios Koumpis. "Role and Value of Learning Theories in the Area of Adaptive Educational Hypermedia Systems Incorporating Learning Styles." International Journal of Emerging Technologies in Learning (iJET) 13, no. 01 (January 22, 2018): 93. http://dx.doi.org/10.3991/ijet.v13i01.7614.

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The purpose of this study was to investigate the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles concerning learning paradigms and learning theories through a meticulous review of the relevant published work. We investigated twenty (20) AEHS and analyze them comparatively to a variety of adaptivity determinants. Two are the pivotal points that are crucial in exploration of pedagogical approaches of these systems: the locus of control and the provided learning material. We conclude that these systems are based mostly on the learning paradigm of Cognitivism and Constructivism. In addition, we assume that the concept of learning theory is not such a narrow term, given that networked world imposes the concept of personal learning.

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Papadimitriou, Alexandros, Maria Grigoriadou, and Georgios Gyftodimos. "A Web-based Learner-Controlled Adaptive Group Formation Technique." International Journal of e-Collaboration 10, no. 1 (January 2014): 14–34. http://dx.doi.org/10.4018/ijec.2014010102.

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This paper presents the learner-controlled adaptive group formation technique offered by the web-based adaptive educational hypermedia system MATHEMA. More specifically, this paper describes why we take into consideration the group effectiveness, concerning the concrete-abstract dimension of learners' learning style, in forming learner groups and how the adaptive group formation algorithm generates a priority list of possible matching candidate collaborators for a certain student, taking into account the abstract or concrete dimension of his/her learning style and his/her possible candidate collaborators' learning style and knowledge level on the current learning goal as well. Moreover, the paper describes how the algorithm supports the learners in selecting the most suitable collaborator, and how it automatically links them up via a chat tool with the aim of negotiating a collaboration agreement. An evaluation of the adaptive group formation of our system indicated that it is usable and useful enough tool for collaborative learning.

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Papadimitriou, Alexandros, and Georgios Gyftodimos. "The Role of Learner Characteristics in the Adaptive Educational Hypermedia Systems: The Case of the MATHEMA." International Journal of Modern Education and Computer Science 9, no. 10 (October 8, 2017): 55–68. http://dx.doi.org/10.5815/ijmecs.2017.10.07.

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Yoosooka, Burasakorn, and Vilas Wuwongse. "An Adaptive System for Retrieval and Composition of Learning Objects." International Journal of Systems and Service-Oriented Engineering 2, no. 4 (October 2011): 42–59. http://dx.doi.org/10.4018/jssoe.2011100103.

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This paper proposes a new approach to automatic retrieval and composition of Learning Objects (LOs) in an Adaptive Educational Hypermedia System (AEHS) using multidimensional learner characteristics to enhance learning effectiveness. The approach focuses on adaptive techniques in four components of AEHS: Learning Paths, LO Retrieval, LO Sequencing, and Examination Difficulty Levels. This approach has been designed to enable the adaptation of rules to become generic. Hence, the application to various domains is possible. The approach dynamically selects, sequences, and composes LOs into an individual learning package based on the use of domain ontology, learner profiles, and LO metadata. The Sharable Content Object Reference Model is employed to represent LO metadata and learning packages in order to support LO sharing. The IMS Learner Information Package Specification is used to represent learner profiles. A preliminary evaluation of the developed system indicates the system’s effectiveness in terms of learners’ satisfaction.

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Tadlaoui, Mouenis Anouar, Rommel Novaes Carvalho, and Mohamed Khaldi. "A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems." International Journal of Advanced Computer Research 8, no. 37 (July 31, 2018): 148–60. http://dx.doi.org/10.19101/ijacr.2018.836020.

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Özyurt, Özcan, Hacer Özyurt, Adnan Baki, Bülent Güven, and Hasan Karal. "Evaluation of an adaptive and intelligent educational hypermedia for enhanced individual learning of mathematics: A qualitative study." Expert Systems with Applications 39, no. 15 (November 2012): 12092–104. http://dx.doi.org/10.1016/j.eswa.2012.04.018.

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Mwambe, Othmar Othmar, Phan Xuan Tan, and Eiji Kamioka. "Endogenous Eye Blinking Rate to Support Human–Automation Interaction for E-Learning Multimedia Content Specification." Education Sciences 11, no. 2 (January 28, 2021): 49. http://dx.doi.org/10.3390/educsci11020049.

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As intelligent systems demand for human–automation interaction increases, the need for learners’ cognitive traits adaptation in adaptive educational hypermedia systems (AEHS) has dramatically increased. AEHS utilize learners’ cognitive processes to attain fair human–automation interaction for their adaptive processes. However, obtaining accurate cognitive trait for the AEHS adaptation process has been a challenge due to the fact that it is difficult to determine what extent such traits can comprehend system functionalities. Hence, this study has explored correlation among learners’ pupil size dilation, learners’ reading time and endogenous blinking rate when using AEHS so as to enable cognitive load estimation in support of AEHS adaptive process. An eye-tracking sensor was used and the study found correlation among learners’ pupil size dilation, reading time and learners’ endogenous blinking rate. Thus, the results show that endogenous blinking rate, pupil size and reading time are not only AEHS reliable parameters for cognitive load measurement but can also support human–automation interaction at large.

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Brusilovsky, Peter. "Adaptive navigation support in educational hypermedia: the role of student knowledge level and the case for meta-adaptation." British Journal of Educational Technology 34, no. 4 (August 1, 2003): 487–97. http://dx.doi.org/10.1111/1467-8535.00345.

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Ikram, Chelliq, Erradi Mohamed, Aammou Souhaib, and Khaldi Mohamed. "Integration of Pedagogical Videos As Learning Object In An Adaptive Educational Hypermedia Systems According To The Learner Profile." International Journal of Computer Trends and Technology 69, no. 6 (June 25, 2021): 1–6. http://dx.doi.org/10.14445/22312803/ijctt-v69i6p101.

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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 (August 29, 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 the requirements and needs of adaptation. We took IMS-LIP, IMS-ACCLIP and IMS-RDCEO standards into consideration and incorporated their characteristics to our proposed learner model so that it conforms to international standards. Moreover, the suggested learner model takes advantage of the semantic web technologies that offer a better data organization, indexing and management and ensures the reusability, the interoperability and the extensibility of this model. Furthermore, due to the use of ontologies, the metadata about a learner can be used by a wide range of personalization techniques to provide more accurate customization.

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Ozyurt, Hacer, Ozcan Ozyurt, Adnan Baki, and Bulent Guven. "An Application of Individualized Assessment in Educational Hypermedia: Design of Computerized Adaptive Testing System and its Integration Into UZWEBMAT." Procedia - Social and Behavioral Sciences 46 (2012): 3191–96. http://dx.doi.org/10.1016/j.sbspro.2012.06.035.

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Pan, Dong, Anwar Hussain, Shah Nazir, and Sulaiman Khan. "A Computationally Efficient User Model for Effective Content Adaptation Based on Domain-Wise Learning Style Preferences: A Web-Based Approach." Complexity 2021 (March 31, 2021): 1–15. http://dx.doi.org/10.1155/2021/6634328.

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In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective.

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Rego, H., T. Moreira, and F. J. García-Peñalvo. "AHKME eLearning Information System." International Journal of Knowledge Society Research 2, no. 2 (April 2011): 71–79. http://dx.doi.org/10.4018/jksr.2011040107.

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AHKME (Adaptive Hypermedia Knowledge Management E-Learning Platform) is an eLearning Information System that has evolved to fulfill the web-based learning requirements when they are compliant with a Web 3.0 philosophy. The ideas behind Web 3.0 are related to more semantic and intelligent systems. AHKME allows teachers to access standardized resources and evaluate integration and reuse possibilities in eLearning systems, not only content but also learning strategy. The educational resources adaptation in AHKME is supported by a set of collaborative tools, which also allow the users’ feedback provision that is stored in system database. The semantic component in AHKME is based on a set of tools for the instructional designer to create and customize specifications and ontologies to give structure and meaning to resources, manual and automatic search with recommendation of resources and instructional design based on the context, and recommendation of adaptations in learning resources. Finally, AHKME takes into account the mobile learning (mLearning) capabilities, allowing access by teachers and students to learning resources, regardless of time and space.

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Alsobhi, Aisha Yaquob, and Khaled Hamed Alyoubi. "Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type." Data Technologies and Applications 53, no. 2 (April 1, 2019): 189–200. http://dx.doi.org/10.1108/dta-10-2018-0092.

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PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

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