SAHAM: Shared Adaptive Hypermedia Application Model

Traditional online learning environments generally use a one-size-fits-all approach, neglecting individual learner needs, goals, and cognitive abilities. Adaptive Educational Hypermedia (AEH) systems emerged to address this by personalizing learning content based on user profiles. The Adaptive Learning System is a computerized system developed to enhance digital interactive educational content teaching and learning. The system’s Adaptive algorithm automatically detects learners’ strengths and weaknesses through the adaptation process and provides personalized learning materials, feedback, and guidance during their learning. Adaptive content allows learners to learn the material at their own pace while providing additional support, evaluation, and resources tailored to each learner. In traditional educational systems, every student is given access to the same learning content regardless of their individual learning needs. Higher education institutions face various challenges when testing and implementing adaptive learning systems for their domain of knowledge. Issues related to technical challenges include processing real-time data, difficulties consolidating learning concepts and solutions into existing learning management systems, and the complexity, ease of use, and usability of adaptive learning systems. However, despite the advancement and technical sophistication in adaptive algorithms, most AEH systems still face significant usability challenges. Learners often struggle with complex interfaces, limited system responsiveness, and poor adaptation to diverse demographics and subject backgrounds. Consequently, the current adaptive systems are underutilized in higher education, with limited empirical evidence on their effectiveness in enhancing student engagement, engagement, quality of experience (QoE), and academic performance. While foundational research work (Brusilovsky, 2001; De Bra et al., 2013) and recent studies (Liu & Lim, 2020; colMOOC project) have shown the importance of adaptive educational hypermedia, yet ongoing issues persist regarding usability, learner control (Paramythis & Loidl-Reisinger, 2004), and empirical validation across varied higher education settings (Latham et al., 2004; Educause, 2018). Systematic investigations into user-controllable personalization and its effects on inclusivity and academic achievement remain limited (Xie et al., 2019; Lundqvist; Warburton, 2019). Furthermore, Li et al. (2021) highlighted that existing systems lack comprehensive cognitive modelling and fail to adequately adapt to individual learner dispositions, which restricts their intelligence and adaptability. This complexity in adaptive systems continues to pose challenges for education overall, as emphasized by Fengying Li, Yifeng He, and Qingshui Xue (2021). Addressing these issues, Dubiel et al. (2022) propose a framework for adaptive user interfaces that integrates the interaction environment to enhance system responsiveness. Therefore, there remains a significant gap in the literature regarding the lack of empirical evaluation of user-controlled personalization, particularly in real-world, multi-disciplinary higher education contexts. Existing research has focused mainly on adaptive algorithmic and technical development, but has not given sufficient attention to practical usability (the lack of effective, usable AEH systems, learner diversity, and actual learner-centered outcomes). Moreover, there is a lack of comprehensive frameworks for evaluating the usability of adaptive educational hypermedia (AEH), and there is limited research on how these adaptive systems can foster inclusivity and fairness among diverse learner populations. Therefore, there is an urgent need to address the usability and complexity problems in AEH systems and to suggest the creation of usable, effective, personalised, learner-centric, and user-controllable AEH systems for higher education environments. This research study addresses these gaps by introducing a personalized, user-controllable Adaptive Educational Hypermedia (AEH) framework specially designed for a Higher Education setting. It evaluates multiple versions of adaptive learning systems across multiple disciplines (English, Malay, Computer Science) revealing significant enhancements in both student achievement and satisfaction. The study not only evaluates multiple versions of adaptive systems but also offers practical recommendations for software developers and educators seeking to implement effective, more intuitive, and learner-centric adaptive systems within real higher educational environments. By exploring the relationships between learner demographics, usability, and academic outcomes, the study promotes inclusivity and equity in digital education. More specifically, it introduces a novel user-controllable personalization feature that empowers learners to customize the learning content to improve their adaptive learning experience, addressing a major limitation found in existing systems. The findings demonstrate significant improvements in student performance and satisfaction with the system’s interface, thereby enhancing subject retention, confirming that adaptive learning systems can effectively improve the academic experience in higher education environments.

The purpose of adaptive educational hypermedia systems (AEHS) is to provide each learner with learning experiences that have been specially tailored to their specific learning requirements. While the concept of AEHS appears promising, AEHS are very complex systems to design and develop. This chapter reviews a few of the challenges encountered in the design and development of these complex systems and some of the challenges encountered by educators who propose to use AEHS with their students. A number of the skills required by educators to develop positive learning experiences are discussed. In order to successfully use AEHS, educators must decide on what student characteristics to base the adaptive elements of the course. Educators may feel challenged to show the impact that AEHS can have on the learning experience. Educators may have a dilemma in deciding to allow or not to allow (1) student access to their user model and (2) students to edit their user model. Further research is required to explore why AEHS have not yet impacted education as initially expected.

The support of diversity is a growing concern in public secondary schools in Spain. Adaptive Educational Hypermedia (AEH) technology provides tools well fitted for this task. Through three different experiences, this paper shows how AEH helps to improve student performance. However, the complexity of the design process for adaptive hypermedia is hindering a wider application of this technology. One possible solution is to apply the benefits of adaptive hypermedia to provide support to adaptive-course authors. In this way, teachers with no knowledge of adaptive systems, user models or formal languages would be able to produce rather complex adaptive courses, starting from course templates automatically selected according to their specific needs. In this paper it is also shown how an adaptive course on adaptive hypermedia design was used to help secondary school teachers to select and instantiate these templates.

E-learning systems have rapidly evolved from generation to another throughout the recent years. Adaptive Educational Hypermedia System (AEHS) provides capabilities to personalize the web-based system according to student's model such as user's interests, needs, preferences, knowledge, and background, which are personal characteristics gathered explicitly or implicitly. The goal of developing AEHS is to achieve an effective and affective learning performance, as well as to get positive impacts on the student through gaining skills and learning experiences. Most AEHS are technologically-driven to transfer knowledge rather than pedagogically-driven focusing on gaining cognitive skills and other experiences. To overcome this gap many researchers considered Cognitive style (CS) as the most important factor for reasoning and predicting individual traits, learning strategies and other preferences. Furthermore many researchers proposed new cognitive factors- rational, intuitive, and interpersonal as the most innate, brain-based, fixed stable preferences to build a robust adaptive student model dynamically and to get positive influence on the affective and effective AEHS. Based on the literature review, we have identified a number of theoretical and technical issues related to the AEHS architecture in relation to the whole brain cognitive style model to achieve an effective learning outcomes. The findings will be used for designing a user model for AEHS.

In the last years the development of learning environments, and particularly of Educational Adaptive Hypermedia (EAH) systems has increased significantly. However, it is important to complement this development with evaluation methods in order to improve EAH system performance. In this context, we propose to analyze the data from student interaction with EAH systems utilizing the GeSES method. This method has been specifically designed to work with student logs and is based on C4.5 rules. In particular, the work described in this paper aims to achieve the following two objectives: testing the method with different types of data in order to find out its reliability, and detecting symptoms of low performance in a specific adaptive learning environment, called CoMoLE.

Adaptive content selection is recognized as a challenging research issue in adaptive educational hypermedia systems (AEHS). Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design by providing AEHS designers with either guidance for the direct definition of adaptation rules, or semi-automated mechanisms which generate the AM via the implicit definition of such rules. The goal of the semi-automated, decision-based approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the insufficiency and/or inconsistency problems of the defined adaptation rule sets. Although such approaches bare the potential to provide efficient AMs, they still miss a commonly accepted framework for evaluating their performance. In this paper, we propose an evaluation framework suitable for validating the performance decision-based approaches in adaptive learning objects selection in AEHS and demonstrate the use of this framework in the case of our proposed decision-based approach for estimating the desired AEHS response.

Adaptive hypermedia system stores information about particular user and according to this information, it adapts its output in accord to this user information. This paper focuses on general reusability of user data in adaptive hypermedia system which are used as an input of the adaptation process. This proposal is based on the mapping language, which maps user parameters (atomic parts of user information in adaptive hypermedia system) between the systems. The essential point of this proposal is, that the proposed integration is done by the external way (by definition of the mapping and external data pump), thus any changes in existing adaptive hypermedia systems are not necessary.

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.

This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described. The most important learning methods are introduced by examples, and their applicability in adaptive hypermedia is discussed. The chapter concludes with a comparison of all approaches one might consider when applying machine learning in adaptive hypermedia systems.

Computer-based educational systems are becoming increasingly important in all levels and types of education. Intelligent Educational Systems (IESs) are computer-based educational systems encompassing Artificial Intelligence schemes to increase the effectiveness of the learning process. In this paper, we survey patents regarding IESs. As IESs we consider either Intelligent Tutoring Systems (ITSs) or Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent techniques. We first present indicative functions implemented in the three basic components of an IES: the domain knowledge, the user (or student) modeling unit and the pedagogical module. Afterwards, we focus on representative patents concerning IESs and present the corresponding work. At the end, we outline current and future developments regarding IESs. Keywords: Intelligent tutoring systems, adaptive educational hypermedia systems, intelligent educational systems, artificial intelligence in education, intelligent E-learning systems, intelligent computer-based training systems, artificial intelligence in Elearning

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.

This paper concentrates on the retrieval aspect in adaptive hypermedia (AH). Traditionally, AH research concentrates on applications that are ‘closed’, in the sense that they assume fixed content elements. Certain applications ask for an extension of the contents considered, with data obtained through information retrieval (IR). This paper addresses this issue of ‘opening up’ AH applications, and gives insight into research that applies techniques from IR and from the Semantic Web (SW) for the embedding of IR in AH. We look at this issue in the context of an abstract reference model (AHAM) and a concrete implementation framework (AHA!). The goal of this research is to define a framework for AH with extended IR functionality. We address the relevant issues for this framework, characterized by the application of concepts from the SW paradigm leading to an enriched notion of concept relevancy.

This literature review focuses on the educational benefits afforded to learners by Technology Enhanced Learning Environments (TELE) which adapt and personalize the learning experience. More specifically it focuses on Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles. Adaptive approaches to learning offer alternatives to the traditional "One-size-fits-all" approach and have driven the development of dynamic educational environments. The use of such environments can deliver educational benefits as educational offerings are personalised based upon various characteristics of individual learners.

Accommodating learning styles in adaptive educational hypermedia systems (AEHS) improves students' learning performance in web-based learning. Hence, in implementing the systems, the students' learning style identification process is important. Most of today's AEHS rely on a traditional technique of identifying students' learning styles, which is using questionnaires. However, using questionnaires for this purpose is less efficient, cumbersome and may not be that feasible. This study proposes a real-time learning style identification technique by recording students' browsing behaviors and analyzing them by using multi-layer feed forward artificial neural network (MLFF). The result suggests that there is a relationship between the frequencies of students' click on learning components with their staying time on those components. It also indicates that the proposed identification technique performs well in identifying students' learning styles.

Adaptive hypermedia is an alternative to the traditional “one‐size‐fits‐all” approach in the development of hypermedia systems. Adaptive hypermedia (AH) systems build a model of the goals, preferences, and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. Adaptive navigation support is a specific group of adaptive hypermedia techniques that become especially popular in educational hypermedia systems. This paper provides a brief overview of main adaptive navigation support techniques and analyzes the results of most representative empirical studies of these techniques. It demonstrates an evidence that different known techniques work most efficiently in different context. In particular, the studies summarized in the paper have provided evidence that users with different knowledge level of the subject may appreciate different adaptive navigation support technologies. The paper argues that more empirical studies are required to help the developers of adaptive hypermedia systems in selecting most relevant adaptation technologies. It also attempts to build a case for meta‐adaptive hypermedia systems, ie, systems that are able to adapt the very adaptation technology to the given user and context.