Using object-orientation to conceptualize an adaptive learning content management system modeling

The main research object of the paper is the analysis of learning content and software customization problems. Learning content is usually described by metadata and stored in Learning Object / Content Repositories (LORs) or Learning (Content) Management Systems (LMSs). Therefore the paper aims at the analysis of the learning content / objects (LOs) metadata as well as LORs and LMSs features / evaluation criteria dealing with learning customization possibilities. The LORs and LMSs quality evaluation criteria are further investigated as the possible optimization parameters, and one of the multiple criteria evaluation and optimization methods is explored to be applied to optimize LORs and LMSs according to the particular learners needs.

E-Learning is a fast, just-in-time, learning process, which is becoming popular in distributed and dynamic environments such as the World Wide Web. The core of E-Learning is Learning Content Management System (LCMS). Traditional LCMS supports search of learning objects based on keywords. The conventional approach provides relevant as well as non-relevant results as they do not consider user's context. Also, precise search of non-textual resources such as audio-visual or multimedia based resources is not possible as these are not simple document files where keywords could be searched. The learner has to spend large amount of time in screening the learning resource to determine the best suiting his/her current learning requirements. In this paper an ontology based LCMS has been discussed. Ontology is a formal description of concepts in a real-world domain and helps to store the semantic information of that domain. It is more expressive than any other standard database model and facilitates knowledge sharing and reuse, i.e. a common understanding of various contents that reaches across people and applications. Developed LCMS incorporates semantic capabilities with the help of ontologies to enable semantic based contribution and retrieval of learning objects. Two very useful ontologies are created to describe the learner profile and the learning content. These ontologies have been used to classify the learning material, organize topics of a course and store the information pertaining to learner's profile. The proposed OLearner LCMS facilitates a resource contributor to contribute the resource to the OLearner portal with the metadata corresponding to the learning content and learner profile ontologies. These ontologies are further useful for a learner in searching a more precise and appropriate learning object.

Learning management systems are characterized by the fact that the scenarios of their operation are related to various aspects of educational activity, which in turn requires costs for the creation of professional training programs, electronic educational resources and their exploitation. These problems confirm the relevance of research aimed at developing models of life cycles for electronic educational resources, programs and qualification levels of specialists, as well as at creating effective tools for automated management of the learning process. The purpose of this study is to optimize the life cycles of electronic educational resources, training programs, employer requirements and increase efficiency in managing the continuous process of training specialists. Analytical studies of life cycle models of electronic educational content, learning and educational activity management systems, content management systems were carried out in the work in order to select and develop tools for automated design and filling of educational resources, as well as synthesis of the life cycle management system of its main components. Models of the life cycles of the main components of the educational environment have been developed, which allow for their coordination and optimization for the system of automated management of training processes and professional development. Distinctive features of the models are the comprehensive synchronization of the life cycles of all components with the assessment of the effectiveness of this process, as well as adjustment to the changing requirements of the external environment. A model for synchronizing the life cycles of the components of the educational environment, including electronic educational content and a system for managing learning and professional development processes, has been developed. A feature of the model is a comprehensive assessment of the effectiveness of the synchronization process at all its stages for the automated selection and/or synthesis of personalized training programs and electronic resources in accordance with the changing requirements of standards and employers.

The education sector is experiencing an unprecedented shift in how students learn and progress through their education. With the large amounts of digital learning content available, teachers are increasingly turning to sources such as online tutorials and eBooks for their teaching needs. Schools often make use of a Learning Content Management System (LCMS) to store learning material, which is catalogued based on the curriculum followed in the school. However, the amount of content indexed in the LCMS is often limited by the ability to manually label and catalogue content. In this paper, we describe our LCMS called the Learning Content Hub (LCH), which not only offers the features of a traditional LCMS including document search and retrieval, document security, user role management, etc., but also provides the ability to automatically analyze and label documents. LCH provides a framework for easily extending the analytics support and exposes application programming interfaces that can be used to build custom education applications for which the content needs can be met using the LCH. We discuss experimental results of the analyzers in our system as well as our experience of deploying this system in a U.S. school district.

Background/Objectives: This study discusses quality control measures for use in distance education institutions that are part of the academic credit bank system of lifelong education in South Korea. Methods/Statistical Analysis: This study proposes a standard model, which measures quality control from two perspectives, namely, learning content and Learning Management Systems (LMS). Findings: This model will operate as a guideline for the development of a quality control system in distance education institutions. As self-directed learning programs are important in measuring quality control from the content viewpoint, this paper suggests pre-quiz narratives, thinking, formative assessment, regular tests, assignments, and discussion methods. Application/Improvements: The study aims to contribute to the field of quality education by ensuring that the distance learning education system in South Korea meets a specific level of education quality in the future.

The scholarly investigation of discourse in teaching and learning is multi-disciplinary, theoretically rich, and highly technical. Researchers with backgrounds in education, cognitive psychology, computer science, and the social sciences apply a diverse set of techniques to understand how student discussions affect learning. Aided by big data coming from learning content management and massive open online course systems, these researchers have an unparalleled opportunity for insight into the teaching and learning process. In this paper we summarize some of the challenges and opportunities arising out of three workshops on Educational Discourse. These workshops convened both expert and emerging scholars to discuss the (i) ethical, (ii) technical, and (iii) infrastructure barriers to building a research community focused on computer-mediated educational discourse. Of particular note is that while computational infrastructure exists for storing and manipulating educational discourse, there is a need for a sociotechnical infrastructure upon which community members can come together to engage in joint work.

Learning Content Management System ATutor, according to data found in this study, has multitudes of faulty User Interface and Interactive controls that should be resolved appropriately as per learners' needs. In relation to this, Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) act as efficacious facilitators in educational environment. This study has evaluated the quality of LMSs, LCMSs and ATutor as case in Human Computer Interaction checklist to increase the fluency of these systems. This research has been assessed ATutor an Open Source Learning Content Management System based on Human Computer Interaction criteria in terms of Conceptual Graphic Design and Interaction Design that had been conducted on a General Survey Model based on descriptive analysis data. Appropriate suggestions to faulty controls including solutions for elimination of redundancy and errors in some controls, substitution of proper technologies and expansion on system capabilities in other controls has been provided based on this assessment. Keywords: ATutor, Conceptual Graphical Models, Interaction Design, Learning Content Management Systems, Learning Management Systems

As a Learning Management System (LMS) and Learning Content Management System (LCMS), netUniversite provides teachers with course models based on IMS Learning Design. This paper presents the psycho-pedagogical method for the generation of online course of netUniversite. An interactive user questionnaire, integrating the results of lecture and quantitative research, is presented as a part of user model, in form of ontology to mitigate the impact of combinatorial explosion.

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.

Learning Management Systems (LMS) in e-learning have functionality for monitoring learning activities. The statistics generated from learning performances can be processed for tutors and a group of learners to improve their quality of teaching and learning. However the current LMS are not designed to facilitate personalised learning support for an individual learner, e.g. adaptive delivery, constructive feedback on assessment and real-time learning activities monitoring. This paper presents a learner-oriented tracking approach for one-to-one support. Technical components of this approach can be embedded in the LMS to effectively facilitate learning experience of the individual learner and quality of learning content design. A learner-oriented tracking model relies on the information from a Learner’s Profile and educational requirements. This determines initially suitable learning content (learning objects) and delivery methods and also provides monitoring information about learners’ experiences.

An ontology has been proposed in this paper, which arranges metadata, and defines the relationships of metadata on learning objects belong to academic courses and user profiles. This ontology has been incorporated with proposed architecture, as a critical part of this architecture what consists of a set of components which synergies to achieve a higher usability to the learner. The ontology is utilized for effective retrieval of learning content, customizing learning management systems (LMS), and an integrating learning content to reuse them. It has been specified in ontology inference language (OIL) and in resource description framework (RDF) for exchange of ontology. The ontology and user profile management components have been introduced into a typical open sourced LMS. Proposed ontology maps user preferences with learning content in searching learning object and amalgamates them to build new course objects. Hence it increases the learner- orientation and usability of e-learning systems.

Emerging telemedicine frameworks with dynamically adaptive content management systems deliver the proper information at the time and place, when and where it is needed. Aggregated telemedicine sensory data is customized and dynamically presented to the suitable recipient. Such systems and methods have already been in some way available for scientific and industrial application. This presented paper extends the underlying adaptive eHealth content management methodology to the coherent structural implementation of weighted majority voting algorithm. This cognitive human-machine interaction drives the targeted dynamically adaptive eHealth content management system in runtime mode. Telemedicine embraces information technology and telecommunication solutions for the sake of serving healthcare services at remote locations outside of the premises of classical medical institutions. These systems produce and process exponentially growing amount of sensory data. Moreover, they support real-time processing which requires a dynamically scalable and fault tolerant architecture. Utilizing cognitive services, the suitable methods determine the adequate system landscape delivering the suitable medical information from the collected body-sensory data. The underlying methodology utilizes correlation-data between different factors, thus invoking eHealth customizable cognitive content management system. The paper also demonstrates the corresponding sociotechnical environment, the applied cognitive infocommunicational methodologies and the related adaptive technological elements. Finally, the future improvement potential and also the challenges of the presented dynamically adaptive eHealth content management system are analyzed in the paper thoroughly.

The increasing demand for World Wide Web (Web) based courses has imposed some challenges in Information and Communications Technology (ICT) field, in particular when considering the education supported by the Web. One of the emerging needs of education supported by the Web is related to the Learning Objects (LO) usage inside Learning Management Systems (LMS). Besides production of "good quality" LO, it must be provided efficient methods concerning their storage and dissemination. However, some difficulties have been reported by teachers of Web based courses: "expensive costs" for LO authoring; lack of tools for sharing these materials; and negative practical experiences for reusing them in an LMS. Some proposals to mitigate these problems have been the development of Learning Object Repository (LOR) tools, mainly in order to facilitate the dissemination of LO. Despite the progress already achieved, there are still several barriers for their adoption by teachers such as lack of easy integration between LMS environment and LOR. In general, current integrations are not simple for typical users. In this context, the target of this project is to presents an innovative LOR, the Interactive Repository of Digital Contents -iRepository. Its model provides seamless integration with a particular LMS and offers several resources to help teaching and learning processes, such as registry of LO educational effectiveness. As a practical contribution, the fundamentals of iRepository model were implemented for an specific LMS, the Moodle system.

Currently, a learning management system (LMS) plays a central role in any e-learning environment. These environments include systems to handle the pedagogic aspects of the teaching–learning process (e.g. specialized tutors, simulation games) and the academic aspects (e.g. academic management systems). Thus, the potential for interoperability is an important, although over looked, aspect of an LMS. In this paper, we make a comparative study of the interoperability level of the most relevant LMS. We start by defining an application and a specification model. For the application model, we create a basic application that acts as a tool provider for LMS integration. The specification model acts as the API that the LMS should implement to communicate with the tool provider. Based on researches, we select the Learning Tools Interoperability (LTI) from IMS. Finally, we compare the LMS interoperability level defined as the effort made to integrate the application on the study LMS.

Common security problems when creating a new system range from basic browser authentication to physical access to servers. The security perspective in e-learning systems is often ignored, and this why this paper tends to emphasize common security issues when developing new Information Systems. We introduce a three layered security measure, such as: the physical control layer, the software control layer and the social engineering training. These three layers tend in various methods to secure users credentials and other data in the newly created Learning Content Management System (LMS).