A Classification Framework for Practice Exercises in Adaptive Learning Systems

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.

A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

Modern trends in educational software development are associated with adaptive learning systems, i.e. systems that can personalize the user’s interaction with the system. One of the key quality indicators for educational software, which is characterized by intensive user interaction with the system, is engagement. The paper discusses applicability of the known methods of assessing engagement to adapt learning process in learning systems, as well as the metrics used by these methods and ways of interpreting them in terms of engagement. A method of combined engagement assessment is presented, which makes it possible to take into account various aspects of possible manifestation of user engagement while interacting with the learning system. Metrics, indicators and rules for their convolution are proposed that are applicable to the online assessment of engagement in adaptive learning systems. Ways of using the developed method to adaptation of the learning process in learning systems are consider.

The use of digital tools and adaptive learning systems has changed the modern art education greatly by making it flexible, personalized, and interactive. Conventional in person training is usually constrained in terms of accessibility, personal feedback and resources. Adaptive digital learning systems solve these issues by means of applying artificial intelligence and data analytics and interactive multimedia tools to customize learning material and content based on artistic talents, learning speeds, and creativity that the students have. This paper discusses the theoretical and technological underpinnings of adaptive online platforms in art education as well as its educational implications. It investigates the use of virtual studios, digital drawing instruments, augmented reality (AR), virtual reality (VR), and collaborative multimedia space in improving artistic experimentation and creativity. The study also examines the algorithms of personalization, AI-informed feedback, and adaptive evaluation models that facilitate the process of unceasing enhancement of artistic learning outcomes. Findings suggest that adaptive online platforms can equally contribute to creativity and engagement in addition to increasing accessibility to a wide range of learners through flexible learning opportunities and inclusive learning materials.

We introduce a novel and unifying framework for the calculation of gradients of both quaternion holomorphic functions and nonholomorphic real functions of quaternion variables. This is achieved by considering the isomorphism between the quaternion domain H and the bivariate complex domain C×C, and by exploiting complex calculus to simplify the quaternion gradient calculation. The validation of the proposed HC calculus is performed against the existing HR calculus, and its convenience is illustrated in the context of gradient-based quaternion optimisation as well as in adaptive learning systems. Quaternion adaptive filtering algorithms and a dynamical perceptron update are next derived based on the bivariate complex representation of quaternions and the HC calculus. Simulations on both synthetic and real-world multidimensional signals support the analysis.

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.

This study explores the use of artificial intelligence technology to build an adaptive learning system to promote the innovation of college English teaching. By integrating natural language processing, deep learning, and recommendation algorithms, a multidimensional adaptive learning framework for college English is constructed that provides personalized support in various skills. By designing comparative teaching experiments and collecting learning behavior data, the effectiveness of the model is systematically evaluated. Experiments show that the system can significantly improve the utilization rate of teaching resources, learning efficiency, and interactive experience. Students who use the system have made remarkable progress in vocabulary mastery, writing ability, and oral expression, and their satisfaction is high. Students with different learning styles and levels can get adaptive learning paths and real-time feedback. The adaptive learning system driven by artificial intelligence shows good applicability and popularization potential in college English teaching.

Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligent adaptive learning and collaborative systems that are brought by the advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems. Given the variety of learning needs as well as the existence of different technological solutions, the book exemplifies the methodologies and best practices through several case studies and adaptive real-world collaborative learning scenarios, which show the advancement in the field of analysis, design and implementation of intelligent adaptive and personalized systems.

The International Maritime Organization's 2023 Greenhouse Gas Strategy necessitates rapid workforce transformation to train 1.2 million seafarers in decarbonization competencies by 2050. This qualitative research investigates adaptive digital learning management systems for maritime decarbonization training through analysis of perspectives from five maritime education professionals with specialized expertise in green shipping technologies and IMO-based learning frameworks. The study employs descriptive analysis to examine participant insights regarding intelligent tutoring systems, predictive learning models, and digital assessment frameworks. Results demonstrate strong support for adaptive learning implementation, with an overall effectiveness score of 4.2 out of 5.0 and projected competency development improvements of 63.0% across domains. Thematic analysis reveals five critical dimensions: personalization imperative, real-time assessment integration, scalability solutions, implementation complexity, and regulatory alignment confidence. Adaptive learning systems can potentially train 240,000 seafarers annually compared to 2,400 through traditional methods while maintaining regulatory compliance. Findings contribute to adaptive learning theory and provide implications for maritime institutions.

This study explores the effectiveness of AI-powered adaptive learning systems in enhancing student performance, engagement, and the overall learning experience. Through an experimental approach, the research evaluates the academic performance, engagement levels, and perceptions of both students and teachers in control and adaptive learning groups. The post-test score data showed an 18% improvement by students in the adaptive group while the control group only improved by 3% which indicates the adaptive learning system brought major academic benefits. Students in adaptive learning classes spent 45 minutes per session while engaging with system content four times per week which was greater than the control group students who dedicated 30 minutes per session with two sessions per week. The instructional staff from the adaptive learning group rated the system higher than those from the control group while system comments mentioned both its effectiveness and usefulness. Student participation increased primarily through the combination of personalized content feedback and relevant learning materials according to research findings. The cost-benefit evaluation demonstrated that performance enhancements of the adaptive learning system proved its value despite expensive initial installation costs and ongoing maintenance expenses. Empowered by artificial intelligence adaptive learning demonstrates potential to change educational practices through customized learning approaches which lead to improved academic outcomes combined with student engagement and generate valuable insights to support system advancement and scaling across diverse educational settings.

An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners’ skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners’ performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners’ progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan learning system. This system is formed by a web application and a game to teach computational logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning system by improving activity classification and assignment. This first step lays solid foundations for automation, making the PLMan learning system fully adaptive.

From a system’s theoretical point of view, adaptive learning systems (ALS) for education and training contain in their core – in a simplified form – closed feedback control loops in which the control is determined by the measured users’ performance. Improving this performance can increase the learning outcome, especially for critical disciplines such as education or training for disaster risk management. However, for this special form of intelligent (e-learning) assistance systems, learning theories and behavioral models have to be considered, e.g., game flow theory, cognition models, or learning models. The research question is how adaptive interactive learning environments (ILE) such as serious games and computer simulations can be characterized and analyzed to determine optimal adaptation strategies. Adaptive learning environments should adapt to the context-related needs of the user in order to ensure and optimize learning success, especially for disaster management training. This contribution presents a concept for an interoperable, adaptive ILE framework which follows control theory and its models, contributing to the state of the art for adaptive games or simulations in disaster risk management.

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits. Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model

<span lang="EN-US">A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. </span>

Artificial Intelligence is the technical foundation of the Fourth Industrial Revolution, leading innovation and change in various areas of society as a whole. Even in the education area, AI-based learning systems have the potential to be used in adaptive classes and to take into consideration the students’ learning situations. This study focused on the use of ALEKS as an AI-based system on an adapted learning system in a general chemistry class for the second semester of 2020 and the first semester of 2021, and suggested factors to consider when operating AI-applied classes. According to our study, AI-based adaptive learning systems have contributed to the improvement of conceptual learning through individual learning support and learning interest and through class operations measuring the students’ learning progress. However, in order for these exploratory studies to produce real and meaningful effects, flexible access is requires in the use of course content, learning data and systems. In addition, it seems that creative class composition and curriculum development are needed, with the application and expansion of concepts taking place in various interactions.</br>Finally, our exploratory research will be the basis for the introduction and application of a basic science class involving AI based adaptive learning systems.