Why not empower knowledge workers and lifelong learners to develop their own environments?

In industrial and educational practice, learning environments are designed and implemented by experts from many different fields, reaching from traditional software development and product management to pedagogy and didactics. Workplace and lifelong learning, however, implicate that learners are more self-motivated, capable, and self-confident in achieving their goals and, consequently, tempt to consider that certain development tasks can be shifted to end-users in order to facilitate a more flexible, open, and responsive learning environment. With respect to streams like end-user development and opportunistic design, this paper elaborates a methodology for user-driven environment design for action-based activities. Based on a former research approach named 'Mash-Up Personal Learning Environments'(MUPPLE) we demonstrate how workplace and lifelong learners can be empowered to develop their own environment for collaborating in learner networks and which prerequisites and support facilities are necessary for this methodology

Language design for a personal learning environment design language

Approaching technology-enhanced learning from the perspective of a learner, we foster the idea of learning environment design, learner interactions, and tool interoperability. In this paper, we shortly summarize the motivation for our personal learning environment approach and describe the development of a domain-specific language for this purpose as well as its realization in practice. Consequently, we examine our learning environment design language according to its lexis and syntax, the semantics behind it, and pragmatical aspects within a first prototypic implementation. Finally, we discuss strengths, problematic aspects, and open issues of our approach

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

E-Learning and microformats: a learning object harvesting model and a sample application

In order to support interoperability of learning tools and reusability of resources, this paper introduces a framework for harvesting learning objects from web-based content. Therefore, commonly-known web technologies are examined with respect to their suitability for harvesting embedded meta-data. Then, a lightweight application profile and a microformat for learning objects are proposed based on well-known learning object metadata standards. Additionally, we describe a web service which utilizes XSL transformation (GRDDL) to extract learning objects from different web pages, and provide a SQI target as a retrieval facility using a more complex query language called SPARQL. Finally, we outline the applicability of our framework on the basis of a search client employing the new SQI service for searching and retrieving learning objects

May I Suggest? Comparing Three PLE Recommender Strategies

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general

May I Suggest? Comparing Three PLE Recommender Strategies

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general

Collaborative Development of a PLE for Language Learning

This paper provides a report on the experimental collaborative and distributed development of a prototypic Widget-based PLE. The development process is described and detailed taking into account the requirements of a language learning scenario. First results are presented, and developer experiences are discussed critically with a focus on the development process as well as problems with current Widget technologies and interoperabilit

Sharing good practice through mash-up personal learning environments

Personal learning environments (PLEs) require new ways to motivate and scaffold learners. In particular, practice sharing is of importance for learnercentric approaches in the scope of (technology-enhanced) lifelong learning, as it is an enabler for community building and sustaining. In this paper we elaborate prerequisites for 'good practice sharing' and explain how we realized these aspects in our PLE solution named Mash-Up Personal Learning Environments (MUPPLE.org). Finally, we argue for the utility of our MUPPLE approach by highlighting two different strategies of good practice sharing and their benefits for learning and community building