Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective

Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective

Third international workshop on Authoring of adaptive and adaptable educational hypermedia (A3EH), Amsterdam, 18-22 July, 2005

The A3EH follows a successful series of workshops on Adaptive and Adaptable Educational Hypermedia. This workshop focuses on models, design and authoring of AEH, on assessment of AEH, conversion between AEH and evaluation of AEH. The workshop has paper presentations, poster session and panel discussions

Supporting authoring of adaptive hypermedia

It is well-known that students benefit from personalised attention. However, frequently teachers are unable to provide this, most often due to time constraints. An Adaptive Hypermedia (AH) system can offer a richer learning experience, by giving personalised attention to students. The authoring process, however, is time consuming and cumbersome. Our research explores the two main aspects to authoring of AH: authoring of content and adaptive behaviour. The research proposes possible solutions, to overcome the hurdles towards acceptance of AH in education. Automation methods can help authors, for example, teachers could create linear lessons and our prototype can add content alternatives for adaptation. Creating adaptive behaviour is more complex. Rule-based systems, XML-based conditional inclusion, Semantic Web reasoning and reusable, portable scripting in a programming language have been proposed. These methods all require specialised knowledge. Hence authoring of adaptive behaviour is difficult and teachers cannot be expected to create such strategies. We investigate three ways to address this issue. 1. Reusability: We investigate limitations regarding adaptation engines, which influence the authoring and reuse of adaptation strategies. We propose a metalanguage, as a supplement to the existing LAG adaptation language, showing how it can overcome such limitations. 2. Standardisation: There are no widely accepted standards for AH. The IMSLearning Design (IMS-LD) specification has similar goals to Adaptive Educational Hypermedia (AEH). Investigation shows that IMS-LD is more limited in terms of adaptive behaviour, but the authoring process focuses more on learning sequences and outcomes. 3. Visualisation: Another way is to simplify the authoring process of strategies using a visual tool. We define a reference model and a tool, the Conceptual Adaptation Model (CAM) and GRAPPLE Authoring Tool (GAT), which allow specification of an adaptive course in a graphical way. A key feature is the separation between content, strategy and adaptive course, which increases reusability compared to approaches that combine all factors in one model

The use of learning styles in adaptive hypermedia

Computer-based learning has become a common phenomenon in the modern age. Many distance-learning systems distribute educational resources on the Internet and indeed entire study programmes are now widely available online. Such a large amount of content and information can be intimidating to learners, who may exhibit different individual characteristics, such as variation in goals, interests, motivation and/or learning preferences. This suggests that a uniform approach taken by learning environments to deliver materials and resources to students is not appropriate and that personalisation of such materials/resources should address users' differences to provide a customised learning experience, thus enhancing its effectiveness, lowering drop-out rates and maintaining high student motivation. This thesis addresses the latter issue of learning preferences, specifically investigating learning styles as an adaptation mechanism for personalised computer-based learning. A number of previous studies indicated the positive effect that this kind of adaptation provides, but under closer examination these were not conducted in a scientifically rigorous manner and thus their findings are somewhat limited. This research utilises a quantitative and highly objective approach to investigate visual/verbal and sequential/global learning styles in different user groups. Three user trials were carried out to discover whether there were any benefits to using these learning styles for studying in an adapted environment. Overall, no statistically significant benefits were found and these findings now shed doubt as to whether learning styles are indeed an effective mechanism for personalised learning

The promises and pitfalls of personalised eLearning

Personalised learning has been around for decades, and never really seems to diminish in its appeal. To educators, its attraction is obvious and logical: we all know that our students learn differently – at different times, different speeds, and respond differently to teaching materials and course resources. To learners, especially where they are part of a larger cohort of students and may become ‘lost’ in the crowd, having a personalised learning experience that is tailored to their individual needs and should help them to reach their specific goals, is very desirable. However, the field of personalised learning is not without its challenges. In this talk, we examine what is meant by personalised eLearning, how it is enacted but also the problems inherent to this work. We consider pedagogical, technical and economic issues as well as considering the wider impact, not just on teachers and students but also at an institutional and even governmental level

The use of learning styles in adaptive hypermedia

Computer-based learning has become a common phenomenon in the modern age. Many distance-learning systems distribute educational resources on the Internet and indeed entire study programmes are now widely available online. Such a large amount of content and information can be intimidating to learners, who may exhibit different individual characteristics, such as variation in goals, interests, motivation and/or learning preferences. This suggests that a uniform approach taken by learning environments to deliver materials and resources to students is not appropriate and that personalisation of such materials/resources should address users' differences to provide a customised learning experience, thus enhancing its effectiveness, lowering drop-out rates and maintaining high student motivation. This thesis addresses the latter issue of learning preferences, specifically investigating learning styles as an adaptation mechanism for personalised computer-based learning. A number of previous studies indicated the positive effect that this kind of adaptation provides, but under closer examination these were not conducted in a scientifically rigorous manner and thus their findings are somewhat limited. This research utilises a quantitative and highly objective approach to investigate visual/verbal and sequential/global learning styles in different user groups. Three user trials were carried out to discover whether there were any benefits to using these learning styles for studying in an adapted environment. Overall, no statistically significant benefits were found and these findings now shed doubt as to whether learning styles are indeed an effective mechanism for personalised learning

Construction of an adaptive e-learning environment to address learning styles and an investigation of the effect of media choice

This study attempted to combine the benefits of multimedia learning, adaptive interfaces, and learning style theory by constructing a novel e-learning environment. The environment was designed to accommodate individual learning styles while students progressed through a computer programming course. Despite the benefits of individualised instruction and a growing worldwide e-learning market, there is a paucity of guidance on how to effectively accommodate learning styles in an online environment. Several existing learning-style adaptive environments base their behaviour on an initial assessment of the learner's profile, which is then assumed to remain stable. Consequently, these environments rarely offer the learner choices between different versions of content. However, these choices could cater for flexible learning styles, promote cognitive flexibility, and increase learner control. The first research question underlying the project asked how learning styles could be accommodated in an adaptive e-learning environment. The second question asked whether a dynamically adaptive environment that provides the learner with a choice of media experiences is more beneficial than a statically adapted environment. To answer these questions, an adaptive e-learning environment named iWeaver was created and experimentally evaluated. iWeaver was based on an introductory course in Java programming and offered learning content as style-specific media experiences, assisted by additional learning tools. These experiences and tools were based on the perceptual and information processing dimension of an adapted version of the Dunn and Dunn learning styles model. An experimental evaluation of iWeaver was conducted with 63 multimedia students. The analysis investigated the effect of having a choice of multiple media experiences (compared to having just one static media experience) on learning gain, enjoyment, perceived progress, and motivation. In addition to these quantitative measurements, learners provided qualitative feedback at the end of each lesson. Data from 27 participants were sufficiently complete to be analysed. For the data analysis, participants were divided into two groups of high and low interest in programming and Java, then into two groups of high and low experience with computers and the Internet. Both group comparisons revealed statistically significant differences for the effect of choice. Having a choice of media experiences proved beneficial for learners with low experience but detrimental for learners with high experience or interest. These findings suggest that the effect of choice appears to be strongly influenced by the learner's background. It is hypothesised that encouraging a more active learner role in educational systems would expand the positive influence of choice to a wider range of learners. The study has contributed some weight to the argument that for certain groups of learners, it is more beneficial to view learning style as a flexible, rather than a stable construct. As a practical implication, it seems advisable to collect data on prior experience, interest, and the initial learning style distribution of the target audience before developing environments comparable to iWeaver

The impact of learning styles on student grouping for collaborative learning: a case study

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-006-9012-7Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.This project has been funded by the Spanish Ministry of Science and Education, TIN2004-03140