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

CERN Storage Systems for Large-Scale Wireless

The project aims at evaluating the use of CERN computing infrastructure for next generation sensor networks data analysis. The proposed system allows the simulation of a large-scale sensor array for traffic analysis, streaming data to CERN storage systems in an efficient way. The data are made available for offline and quasi-online analysis, enabling both long term planning and fast reaction on the environment

Change Of Routines: A Multi-Level Analysis

This paper analyses how organizational routines change. It focuses on the level of learning groups within organizations. The paper starts with a summary of the 'activity theory' of knowledge used. Next, the notion of scripts is used, to analyse organizational groups as 'systems of distributed cognition', and to identify different levels of routines and their change. Finally, the paper looks at communication routines or rules needed for different levels of change, in the formation of new 'shared beliefs'.organizational change;organizational learning;evolution;routines;scripts

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

A Taxonomy for a Constructive Approach to Software Evolution

In many software design and evaluation techniques, either the software evolution problem is not systematically elaborated, or only the impact of evolution is considered. Thus, most of the time software is changed by editing the components of the software system, i.e. breaking down the software system. The software engineering discipline provides many mechanisms that allow evolution without breaking down the system; however, the contexts where these mechanisms are applicable are not taken into account. Furthermore, the software design and evaluation techniques do not support identifying these contexts. In this paper, we provide a taxonomy of software evolution that can be used to identify the context of the evolution problem. The identified contexts are used to retrieve, from the software engineering discipline, the mechanisms, which can evolve the software software without breaking it down. To build such a taxonomy, we build a model for software evolution and use this model to identify the factors that effect the selection of software evolution\ud mechanisms. Our approach is based on solution sets, however; the contents of these sets may vary at different stages of the software life-cycle. To address this problem, we introduce perspectives; that are filters to select relevant elements from a solution set. We apply our taxonomy to a parser tool to show how it coped with problematic evolution problems

The Effect of Adaptive Learning Style Scenarios on Learning Achievements

Bozhilov, D., Stefanov, K., & Stoyanov, S. (2009). Effect of adaptive learning style scenarios on learning achievements [Special issue]. International Journal of Continuing Engineering Education and Lifelong Learning (IJCEELL), 19(4/5/6), 381-398.The study compares three adaptive learning style scenarios, namely matching, compensating and monitoring. Matching and compensating scenarios operate on a design-time mode, while monitoring applies a run-time adaptation mode. In addition, the study investigates the role of pre-assessment and embedded adaptation controls. To measure the effectiveness of different adaptive scenarios, a software application serving as a test-bed. was developed. An experimental study indicated that the monitoring adaptation led to higher learning achievements when compared to matching and compensating adaptation, although no significant effect was found

Effect of adaptive learning style scenarios on learning achievements

Bozhilov, D., Stefanov, K., & Stoyanov, S. (2009). Effect of adaptive learning style scenarios on learning achievements [Special issue]. International Journal of Continuing Engineering Education and Lifelong Learning (IJCEELL), 19(4/5/6), 381-398.The study compares three adaptive learning style scenarios, namely matching, compensating and monitoring. Matching and compensating scenarios operate on a design-time mode, while monitoring applies a run-time adaptation mode. In addition, the study investigates the role of pre-assessment and embedded adaptation controls. To measure the effectiveness of different adaptive scenarios, a software application serving as a test-bed. was developed. An experimental study indicated that the monitoring adaptation led to higher learning achievements when compared to matching and compensating adaptation, although no significant effect was found