Framework for improving usability, reliability, and learning outcomes in adaptive educational hypermedia systems in higher education

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

Higher education, as a leading channel for the transmission of scientific knowledge, is today under strong pressure from the society, which is characterized by demands for its improvement, quality improvement, approximation to the real practice of social life, etc. This is related, in particular, to the formation of open societies of knowledge, post-classical science and open education. The imperatives of education development today are interdisciplinarity and transdisciplinarity, integrity, dialogization, anthropocentricity, individual freedom, orientation to the needs of society and production, attention to complex systems, accessibility of education for broad sections of the population, continuity of education, etc. These guidelines are reflected in the implementation of student-centered and competency-based approaches, individual educational trajectories, consideration of labour market requirements, etc. At the same time, they somehow conflict with the traditional educational system, built on the foundations of epistemological knowledge, and create new risks for it. The analysis of scientific studies has shown that the problem of implementing an adaptive system of professional training of future specialists as a complete pedagogical system, which includes not only the latest information and communication technologies, but also the conceptual foundations and organizational conditions of its functioning, remains outside the attention of researchers. The analysis of existing adaptive learning systems has proved that in most cases these developments are either distributed on a commercial basis, or have limited functionality, or do not have the means to integrate them into the educational process of specific educational institutions. Therefore, the relevance of the study is determined by the need to overcome the contradictions between: the high level of professional demand for qualified specialists and the unreadiness of the higher education system of Ukraine to meet these needs in full; – the urgent need of the economy and society for responsible, creative and critical-thinking specialists who are able to independently and quickly make professional decisions, and the predominance of traditional, knowledge-based forms and methods of professional training in higher education; – society's demand for the application of the individual approach to training and education of specialists and the unwillingness of scientific and pedagogical workers to implement it; – wide possibilities of information and communication technologies, in particular distance ones, and insufficient readiness of scientific and pedagogical workers for their use in the educational process. Learning management systems and their use in blended learning enable the implementation of the individual approach, but to an incomplete extent. That is why the current direction of research is the improvement of existing learning management systems and the creation of adaptive systems of professional training of future specialists based on them. The research project of the Department of Informatics and Cybernetics of Bogdan Khmelnitsky Melitopol state pedagogical university "Adaptive system for individualization and personalization of professional training of future specialists under conditions of blended learning" (state registration number: 0120U101970) is aimed at solving this task. Its results are presented in the monograph. Part of the monograph authored by K. P. Osadcha is funded by the Federal Ministry of Education and Research (BMBF) and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments.

This paper describes the interactive problem solving support offered by our adaptive educational hypermedia system called MATHEMA. The general goal of the MATHEMA is the support of senior high school students or the beginners of higher education, through an interactive and constructivist environment, in learning physics (electromagnetism) individually and/or collaboratively, and to overcome their possible misconceptions and learning difficulties. Initially, a review of related work about the implemented AEHS/ITS and the didactic design principles of the MATHEMA are presented. Through the interactive problem solving, the system supports the students in solving electromagnetism problems, individually and/or collaboratively, by following an activity that is based on the experimentation with simulations, explorations, guided discovery, and collaboration didactic approaches. An experimental study with senior high school students showed that they improve their performances when following this activity. A questionnaire that we gave to the students to express their opinion about our system helped us to improve the quality of the courses.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.