Adaptive hypermedia system interoperability : a 'real world' evaluation

Adaptive Hypermedia (AH) authoring is widely acknowledged to be complex and time consuming, yet this vital process is rarely evaluated. Recent research has approached the authoring problem by ensuring that previously created materials can be converted from one system to another. This paper evaluates the results of this research, specifically the creation of adaptive materials in MOT and their conversion and subsequent delivery in WHURLE. A group of technically experienced IT users who are novice AH authors were exposed to MOT and WHURLE during an introductory week long course. This paper interprets the results of these authors using a "write once, deliver many" paradigm of adaptive hypermedia creation

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

A problem-oriented method for supporting AEH authors through data mining

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Proceeding of International Workshop on Applying Data Mining in e-Learning ADML'07. Sissi, Lassithi - Crete Greece, 18 September, 2007.One of the main problems with Adaptive Educational Hypermedia Systems (AEHS) is that is very difficult to test whether adaptation decisions are beneficial for all the students or some of them would benefit from a different adaptation. Data mining techniques can provide support to overcome, to a certain extent, this problem. This paper proposes the use of these techniques for detecting potential problems of adaptation in AEH systems. The proposed method searches for symptoms of these problems (called anomalies) through log analysis and tries to interpret the findings. Currently, a decision tree technique is being used for the task.This work has been partially funded by the Spanish Ministry of Science and Education through project TIN2004-03140 and TSI2006-12085. The author C. Vialardi is also funded by Fundacion Carolina

The hybrid model, and adaptive educational hypermedia frameworks

The amount of information on the web is characterised by being enormous, as is the number of users with different goals and interests. User models have been utilized by adaptive hypermedia systems generally and adaptive educational hypermedia systems (AEHS) particularly to personalize the amount of information they have with respect to each individual's knowledge, background and goals. As a result of the research described herein, a user model called the Hybrid Model has been developed. This model is both generic and abstract, and it extends other models used by AEHS by measuring users' knowledge levels with respect to different knowledge domains simultaneously by utilising well known techniques in the world of user modelling, specifically the Overlay model (which has been modified) and the Stereotype model. Therefore, using the Hybrid Model, AEHS will not be restricted to a single knowledge domain at anyone time. Thus, by implementing the Hybrid model, those systems can manage users' knowledge globally with respect to the deployed knowledge domains. The model has been implemented experimentally in an educational hypermedia system called WHURLE (Web-based Hierarchal Universal Reactive Learning Environment) to verify its aim - managing users' knowledge globally. Moreover, this implementation has been tested successfully through a user trial as an adaptive revision guide for a Biological Anthropology Course. Furthermore, the infrastructure of the WHURLE system has been modified to embrace the objective of the Hybrid Model. This has led to a novel design that provides the system with the capability of utilising different user models easily without affecting any of its component modules

Improving AEH courses through log analysis

Authoring in adaptive educational hypermedia environment is complex activity. In order to promote a wider application of this technology, the teachers and course designers need specific methods and tools for supporting their work. In that sense, data mining is a promising technology. In fact, data mining techniques have already been used in E-learning systems, but most of the times their application is oriented to provide better support to students; little work has been done for assisting adaptive hypermedia authors through data mining. In this paper we present a proposal for using data mining for improving an adaptive hypermedia system. A tool implementing the proposed approach is also presented, along with examples of how data mining technology can assist teachers.This work has been partially funded by the Spanish Ministry of Science and Education through project HADA (TIN2007-64716). The first author is also funded by Fundación Carolina

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

Supporting the development of mobile adaptive learning environments: A case study

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. E. Martín and R. M. Carro, "Supporting the development of mobile adaptive learning environments: A case study" IEEE Transactions on learning technologies, vol. 2, no. 1, pp. 23-36, january-march 2009In this paper, we describe a system to support the generation of adaptive mobile learning environments. In these environments, students and teachers can accomplish different types of individual and collaborative activities in different contexts. Activities are dynamically recommended to users depending on different criteria (user features, context, etc.), and workspaces to support the corresponding activity accomplishment are dynamically generated. In this paper, we present the main characteristics of the mechanism that suggests the most suitable activities at each situation, the system in which this mechanism has been implemented, the authoring tool to facilitate the specification of context-based adaptive m-learning environments, and two environments generated following this approach will be presented. The outcomes of two case studies carried out with students of the first and second courses of “Computer Engineering” at the “Universidad Auto´noma de Madrid” are also presented.This work has been supported by the Spanish Ministry of Science and Education, project number TIN2007-64718

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