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The purpose of this paper is to discuss how learning styles and theories are currently used within personalised adaptable e‐learning adaptive systems. This paper then aims to describe the e‐learning platform iLearn and how this platform is designed to incorporate learning styles as part of the personalisation offered by the system.

The paper discusses how learning styles and theories are currently being used within the area of adaptive e‐learning and describes current research within this area. This paper then gives an overview of the iLearn project and describes how iLearn is using the VARK learning style to enhance the platform's personalisation and adaptability for the learner. This research also describes the system's design and how the learning style is incorporated into the system design and semantic framework within the learner's profile.

The findings describe how the final implemented iLearn platform intends to address the issues found with the limited personalisation within common learning management systems and intends to provide the learner with a personalised learning experience.

Adaptability and personalisation are large research areas, however, many limitations have been found during the current research. This research project, therefore adds value to this by proposing a system which will address the current personalisation limitations.

Institutional repositories (IR) are usually used to archive and manage digital collections including research results, educational material, etc. Learning management systems (LMS) form a popular basis for e-learning and blended learning. This paper aims to study how to integrate IR and LMS to support accessibility of disabled students and students with learning difficulties (dyslexic students) in higher education. Customised ontologies focusing on disabled students can be used to facilitate indexing, and access of items in the repository.

The authors propose a simple methodological approach to establish an integrating system for supporting accessibility. First, the authors review research works related to adaptive learning environments (ALEs) and blended learning, and discuss issues of the interoperability of IR and LMS. Then, based on the review, the authors discuss the use of an integrated ALE for supporting disabled students in the domain of higher technological education. The integrated system is based on IR, LMS and assistive and adaptive technology. The open source software platform DSpace is used to build up the repository applications Use of the web ontology language (OWL) ontologies is also proposed for indexing and accessing the various, heterogeneous items stored in the repository. Various open source LMS (e.g. openeclass) could be used to build up the integrated system. Finally, the authors describe experimentation with a prototype implemented to provide the mentioned capabilities.

The technology is mature enough for building up integrated systems, combining capabilities of IR and LMS, for supporting disabled students. The use of ontologies focused on disabled students could facilitate the use of such integrated systems. Customisation and operation of a platform, for the selection and use of portions of OWL ontologies, could be based on the open source software Protégé. Such a platform forms a basis to create an appropriate ontology suitable for specific domains, e.g. the domain of technological education. Finally, the authors argue that the combined use of the OWL platform and the DSpace repository with open source LMS platforms could support domain experts for creating customised ontologies and facilitating searching.

A new perception of the term integrated system for supporting disabled students in the higher education context is presented. This perception tries to combine the IR technology that supports the self-archiving approach of information, open LMS technology and the user-centred approach to support students and manage the “life of information”.

This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities.

The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia).

The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties.

The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results.

The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities.

The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

This paper aims to provide a methodological road for the next generation of e‐learning environments.

This paper considers a survey of recent publications (1995‐2002), which aim to provide practical and theoretical indications and advice, which are coupled with practical experimentations.

The paper provides road‐mapping elements, indicating the impact on services and systems to be expected by this design approach.

The survey is based on a selection of sources and it is not exhaustive. The methodology experiments that are used for argumentation are based on the authors’ platform.

The paper presents a useful source of knowledge for researchers and advanced students.

This paper identifies a road for advanced e‐learning systems, and can help researchers and those in industry who desire to introduce and understand the design methodological context of advanced e‐learning systems.

E-learning is part of instructional design and has opened a whole world of new possibilities in terms of learning and teaching. The purpose of this paper is to develop an adaptive e-learning platform that enhances skills from primary school to university learners. Two purposes converge here: a pedagogical one – offering new possibilities, especially in terms of teaching scenarios (blended learning); and a research one – confirming the effectiveness of an adaptive e-learning tool in the case of individualized cross-disciplinary competences, such as comprehension of implicit information in written texts (French).

The case study presented here concerns primary-school learners using the Implicit module of TACIT adaptive e-learning tool over the 2016-2017 academic year.

This paper gives a first positive answer to the effectiveness of such a tool in this specific context. This pedagogical effectiveness is more pronounced for low-level pupils, especially for girls and for older pupils (CM1/CM2, respectively, fourth/fifth grade).

In this case study, the module comes from an existing platform, created by the TACIT research group. The adaptive environment was created by using the Item Response Theory models and, more precisely, the Rasch model.

Through 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.

The 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.

An 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.

The 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.

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

The purpose of this paper is to explore the role of emotion in learning, specifically, e‐learning and its relationship to the phenomenon called energetic learning.

After first presenting operation definitions, the paper looks through the lens of new findings in neuroscience to build an understanding of the role of emotions in learning, then focuses specifically on how e‐learning systems contribute to energetic learning, providing examples of e‐learning platforms and software programs currently available that have specific attributes contributing to energetic learning.

With technology comes a natural excitement in terms of connectivity and its support of self‐driven, experiential learning which is part of the evolutionary heritage. As the understanding of the neuroscience and biology of human learning advances, the personal needs of individual learners are being begun to understand better. Bringing these needs together with e‐learning system capabilities will offer a significant jump in the learning rate and efficiency as we move into a future filled with change, uncertainty, complexity and anxiety.

The paper introduces the concept of energetic learning with specific focus on the contribution of e‐learning to energetic learning.

The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given learning management system based on learners’ learning styles.

This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle.

The system prototype supports real-time adaptivity in any given learning management system based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the learning management system. Moreover, it can support adaptivity at both course and learner levels.

To the best of the authors’ knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity.

This paper aims to present part of the work of an ongoing research project that is looking at socio‐ cultural and technological developments from a mobile technology convergence view; in order to show how culturally aware convergence developments in mobile technology can be adopted and employed for the betterment of society.

The paper presents a scenario for a mobile technology enabled learning environment in support of the conventional learning approach with a focus on enabling parental involvement and contribution to the daily learning objectives of their children and hence enhancing a quality learning experience. It further critically discusses issues of interface design – at both the device and application levels – that will have an impact on the quality of e‐learning, with a focus on mobile technology.

The paper shows how interface design can positively enhance the quality defining characteristics of learning in an e‐learning environment. Ways of achieving these characteristics of learning through effective e‐learning are reported. This is done by addressing requirements for quality‐learning through effective interface‐design considerations, towards meeting the overall quality requirements of learning that should be intrinsic to a holistic e‐learning environment. The value of human computer interaction and the critical factors of promoting productive interaction are addressed.

There are several factors affecting quality of e‐learning as a tool and approach to flexible and independent learning. The advent and use of mobile technology has been investigated in this work from a socio‐cultural and technological perspectives in two continents. The limitations lie in the depth of investigations and how far the findings can be applied to the diversity of learners.

As the effects of cultures and the rapid technological advancements take toll on teaching and learning the findings reported in this paper have far reaching implications for learners from different cultures and also for attempts at bridging existing digital divide.

The approach adopted in the research is unique by virtue of new findings and ideas presented. The paper highlights the opportunities for mobile devices and technology to play a role in the development of communities through technology aided learning (e‐learning), with a focus on e‐learning systems and technology requirements for delivering a quality learning experience.