Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Providing Service-based Personalization in an Adaptive Hypermedia System

Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large.In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content.To empirically prove the viability of our approach, we developed PERSEUS - server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS

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 Review of Adaptive Educational Hypermedia System Based on Learning Style

Accommodating learning style in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning processes as well as teacher and learner satisfaction. The premise is that a fact that learning in classroom is less efficient, when teachers will not be able to get insight of each of the student’s learning style hence, they wont be able to adapt their teaching strategies to match with the student’s learning style. In order to get insight of the student’s learning style in AEHS, the system must be able to recognize the learning style of the students. Current methods for recognizing learning styles are less efficient, where questionnaires or surveys were used to the students, which lead to tedium and disturbance at learning processes. By using proposed approaches which are multilayer feed forward artificial neural network (MLFF), fragment sorting, and adaptive annotation technique, this study will design and develop an AEHS

A prototype tool for the automatic generation of adaptive websites

This paper presents AWAC, a prototype CAWE tool for the automatic generation of adaptive Web applications based on the A-OOH methodology. A-OOH (Adaptive OO-H) is an extension of the OO-H approach to support the modeling of personalized Websites. A-OOH allows modeling the content, structure, presentation and personalization of a Web Application. The AWAC tool takes the A-OOH design models of the adaptive Website to generate as an input. Once generated, the adaptive Website also contains two modules for managing the personalization which, at runtime, analyze the user browsing events and adapt the Website according to the personalization rule(s) triggered. These personalization rules are specified in an independent file so they can be updated without modifying the rest of the application logic

WiBAF into a CMS: Personalization in learning environments made easy

Adaptivity has proven successful in reducing navigation and comprehension problems in hypermedia documents. Authoring of adaptive hypermedia documents and especially of the adaptivity in these documents has been problematic or at least labour intensive throughout AH history. This paper shows how the integration of a CMS with an adaptive framework greatly simplifies the inclusion of personalization in existing educational applications. It does this within the context of European project Autism&Uni that uses adaptive hypermedia to offer information for students transitioning from high school to university, especially to cater for students on the autism spectrum as well as for non-autistic students. The use of our Within Browser adaptation framework (WiBAF) reduces privacy concerns because the user model is stored on the end-user's machine, and eliminates performance issues that currently prevent the adoption of adaptivity in MOOC platforms by having the adaptation performed on the end-user's machine as well (within the browser). Authoring of adaptive applications within the educational domain with the system proposed was tried out with first year students from the Design-Based Learning Hypermedia course at the Eindhoven University of Technology (TU/e) to gather feedback on the problems they faced with the platform