Proposal of a mobile learning preferences model

A model consisting of five dimensions of mobile learning preferences – location, level of distractions, time of day, level of motivation and available time – is proposed in this paper. The aim of the model is to potentially increase the learning effectiveness of individuals or groups by appropriately matching and allocating mobile learning materials/applications according to each learner’s type. Examples are given. Our current research investigations relating to this model are described

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Integrating Human Factors and Semantic Mark-ups in Adaptive Interactive Systems

This paper focuses on incorporating individual differences in cognitive processing and semantic mark-ups in the context of adaptive interactive systems. In particular, a semantic Web-based adaptation framework is proposed that enables Web content providers to enrich content and functionality of Web environments with semantic mark-ups. The Web content is created using a Web authoring tool and is further processed and reconstructed by an adaptation mechanism based on cognitive factors of users. Main aim of this work is to investigate the added value of personalising content and functionality of Web environments based on the unique cognitive characteristics of users. Accordingly, a user study has been conducted that entailed a psychometric-based survey for extracting the users' cognitive characteristics, combined with a real usage scenario of an existing commercial Web environment that was enriched with semantic mark-ups and personalised based on different adaptation effects. The paper provides interesting insights in the design and development of adaptive interactive systems based on cognitive factors and semantic mark-ups

Ontology-based personalisation of e-learning resources for disabled students

Students with disabilities are often expected to use e-learning systems to access learning materials but most systems do not provide appropriate adaptation or personalisation to meet their needs.The difficulties related to inadaptability of current learning environments can now be resolved using semantic web technologies such as web ontologies which have been successfully used to drive e-learning personalisation. Nevertheless, e-learning personalisation for students with disabilities has mainly targeted those with single disabilities such as dyslexia or visual impairment, often neglecting those with multiple disabilities due to the difficulty of designing for a combination of disabilities.This thesis argues that it is possible to personalise learning materials for learners with disabilities, including those with multiple disabilities. This is achieved by developing a model that allows the learning environment to present the student with learning materials in suitable formats while considering their disability and learning needs through an ontology-driven and disability-aware personalised e-learning system model (ONTODAPS). A disability ontology known as the Abilities and Disabilities Ontology for Online LEarning and Services (ADOOLES) is developed and used to drive this model. To test the above hypothesis, some case studies are employed to show how the model functions for various individuals with and without disabilities and then the implemented visual interface is experimentally evaluated by eighteen students with disabilities and heuristically by ten lecturers. The results are collected and statistically analysed.The results obtained confirm the above hypothesis and suggest that ONTODAPS can be effectively employed to personalise learning and to manage learning resources. The student participants found that ONTODAPS could aid their learning experience and all agreed that they would like to use this functionality in an existing learning environment. The results also suggest that ONTODAPS provides a platform where students with disabilities can have equivalent learning experience with their peers without disabilities. For the results to be generalised, this study could be extended through further experiments with more diverse groups of students with disabilities and across multiple educational institutions

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area

HOW DO UNIVERSITY STUDENTS SELECT AND USE THEIR LEARNING TOOLS? A MIXED-METHOD STUDY ON PERSONALISED LEARNING (21)

Universities often blend traditional learning and e-learning by providing software licenses, electronic learning materials, and access to Learning Management Systems. Following the idea of personalised learning in higher education, students are free to choose between a wide range of learning tools constructing their Personalised Learning Environment. However, the characteristics of the chosen tools need to match the characteristics of the learning tasks to support students adequately. In the present paper, a mixed-method approach is used to analyse which types of tools are used in practice and which types of learning tasks are performed using these learning tools. Furthermore, important factors influencing the decision to select learning tools are identified. This study shows that a wide array of learning tools is used in practice. Although students consider individual factors (such as perceived ease of use and task-technology fit) to be most important when selecting their tools, several exogenous factors such as the lecturers’ targeted pedagogy, social norm and the occurrence of higher order thinking skills limit the range of adequate learning tools

Personalised learning environments: Core development issues for construction

The growth of e-Learning has been continual and sustained. This has been fuelled by developments in Information and Communication Technologies (ICT) the nuances of which are starting to reap considerable benefits in the educational and business environments. Specific benefits have included e-interoperability, scalability, adaptability and the mass-customisation of learning packages to the distributed learner community. Notwithstanding the technology related issues, from a pedagogic perspective, learning styles and instructional strategies are now being intensively studied in the "traditional’ classroom setting to leverage advantage. However, there has been little research undertaken on the application of learning styles within the educational arena, perhaps because of limited authoring applications or explicit choice vis-à-vis the creation of instructional strategies for specific learning styles. In this context, some of the evidence identifies that the more thoroughly instructors understand the differences in learning styles, the better chance they have of meeting the diverse learning needs of learners. Therefore, the paradigm of "one size fits all", by default, can only address the generic learner issues (and not the specific ‘personalised’ learner requirements). This paper introduces the concepts and issues surrounding the development (and barriers) of personalised learning environments, which incorporates learning styles.The growth of e-Learning has been continual and sustained. This has been fuelled by developments in Information and Communication Technologies (ICT) the nuances of which are starting to reap considerable benefits in the educational and business environments. Specific benefits have included e-interoperability, scalability, adaptability and the mass-customisation of learning packages to the distributed learner community. Notwithstanding the technology related issues, from a pedagogic perspective, learning styles and instructional strategies are now being intensively studied in the "traditional’ classroom setting to leverage advantage. However, there has been little research undertaken on the application of learning styles within the educational arena, perhaps because of limited authoring applications or explicit choice vis-à-vis the creation of instructional strategies for specific learning styles. In this context, some of the evidence identifies that the more thoroughly instructors understand the differences in learning styles, the better chance they have of meeting the diverse learning needs of learners. Therefore, the paradigm of "one size fits all", by default, can only address the generic learner issues (and not the specific ‘personalised’ learner requirements). This paper introduces the concepts and issues surrounding the development (and barriers) of personalised learning environments, which incorporates learning styles

Personalised trails and learner profiling in an e-learning environment

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Personalised learning environments: Core development issues for construction

The growth of e-Learning has been continual and sustained. This has been fuelled by developments in Information and Communication Technologies (ICT) the nuances of which are starting to reap considerable benefits in the educational and business environments. Specific benefits have included e-interoperability, scalability, adaptability and the mass-customisation of learning packages to the distributed learner community. Notwithstanding the technology related issues, from a pedagogic perspective, learning styles and instructional strategies are now being intensively studied in the "traditional’ classroom setting to leverage advantage. However, there has been little research undertaken on the application of learning styles within the educational arena, perhaps because of limited authoring applications or explicit choice vis-à-vis the creation of instructional strategies for specific learning styles. In this context, some of the evidence identifies that the more thoroughly instructors understand the differences in learning styles, the better chance they have of meeting the diverse learning needs of learners. Therefore, the paradigm of "one size fits all", by default, can only address the generic learner issues (and not the specific ‘personalised’ learner requirements). This paper introduces the concepts and issues surrounding the development (and barriers) of personalised learning environments, which incorporates learning styles.The growth of e-Learning has been continual and sustained. This has been fuelled by developments in Information and Communication Technologies (ICT) the nuances of which are starting to reap considerable benefits in the educational and business environments. Specific benefits have included e-interoperability, scalability, adaptability and the mass-customisation of learning packages to the distributed learner community. Notwithstanding the technology related issues, from a pedagogic perspective, learning styles and instructional strategies are now being intensively studied in the "traditional’ classroom setting to leverage advantage. However, there has been little research undertaken on the application of learning styles within the educational arena, perhaps because of limited authoring applications or explicit choice vis-à-vis the creation of instructional strategies for specific learning styles. In this context, some of the evidence identifies that the more thoroughly instructors understand the differences in learning styles, the better chance they have of meeting the diverse learning needs of learners. Therefore, the paradigm of "one size fits all", by default, can only address the generic learner issues (and not the specific ‘personalised’ learner requirements). This paper introduces the concepts and issues surrounding the development (and barriers) of personalised learning environments, which incorporates learning styles