Lasten ja nuorten hyvinvoinnin tietopankki Itlasto – tiedolla johtamisen tueksi

Itlastoon (www.itlasto.fi) kootaan kansallista, pitkittäistä seurantatietoa lasten ja nuorten ­hyvinvoinnista. Tieto esitetään visuaalisesti, käyttäjäystävällisessä muodossa. Itlastosta löytyy vastauksia kysymyksiin, miten lapsemme voivat ja miten hyvinvoinnin tilan ­indikaattoreiden tasot ovat kehittyneet ajassa. Indikaattoreiden tulkinta esimerkiksi päätöksenteossa vaatii taustalla olevien ilmiöiden tuntemista. Seurantatieto tarjoaa mahdollisuuden ilmiöiden pohdinnalle: mitkä mekanismit tai toimet ovat ­voineet vaikuttaa indikaattorin tason kehitykseen? Kansallisen tiedon rinnalla halutaan tulevaisuudessa esittää alueellista tietoa lasten ja nuorten ­hyvinvoinnista

Delivering courses modelled using IMS Learning Design

Tattersall, C., Vogten, H., Brouns, F., Koper, R., Van Rosmalen, P, Sloep, P. Van Bruggen, J. (submitted). Delivering courses modelled using IMS Learning Design.E-learning promises efficiency of education. This promise is based on the economics of multiple delivery, whereby initial production costs for an e-learning course are recouped by delivering the course to different groups of learners at different times. This can only be realised by distinguishing between abstract representations of courses and instances of these representations involving specific sets of learners. This article provides an analysis of the requirements for multiple deliveries of courses. It describes the design of an approach which meets these requirements in the domain of integrated e-learning systems, together with experiences resulting from implementation of the design. The article concludes with a discussion of the approach

Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning

When referring to this paper please use the original source: British Journal of Educational Technology, 2004, 35 nr 6, pp. 729 - 738As we move towards distributed, self-organized learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials and curricula. Considering the nature of the network envisaged, maintaining data on these characteristics and ensuring their integrity are difficult tasks. In this article we review the usability of Latent Semantic Analysis (LSA) to generate a common semantic framework for characteristics of the learner, learning materials and curricula. Although LSA is a promising technique, we identify several research topics that must be addressed before it can be used for learner positioning

IMS Learning Design Frequently Asked Questions

This list of frequently asked questions was composed on the basis of questions asked of the Educational Technology Expertise Centrum. The questions addessed are: Where can I find the IMS Learning Design Specification? What is meant by the phrase “Learning Design”? What is the IMS LD Specification about, in a nutshell? Why is the IMS LD Specification important? What problems is IMS LD designed to alleviate? What about IMS LD and pedagogical neutrality? What are the different levels of IMS LD and why are they distinguished? How does IMS LD relate to the other IMS specifications? IMS LD and IMS Simple Sequencing seem to be similar. Which one should I use? Will IMS LD be incorporated into a future version of SCORM? How was IMS LD developed? What’s the difference between IMS LD and EML? What kind of process is the creation of a learning design? What kind of support is available today for learning designers? What kind of support is envisaged for learning designers? How are learning designs transformed to ‘something that runs’? What happens at run-time? Is there an IMS LD developer’s community? The Best Practice Guide examples have incorrect schema locations. What’s wrong? I’m still getting schema errors with the examples. What’s wrong

Integrating IMS Learning Design and IMS Question and Test Interoperability using CopperCore Service Integration

Please, cite this publication as: Vogten, H., Martens, H., Nadolski, R., Tattersall, C., van Rosmalen, P., & Koper, R. (2006). Integrating IMS Learning Design and IMS Question and Test Interoperability using CopperCore Service Integration. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.orgThis article describes a framework for the integration of e-learning services. There is a need for this type of integration in general, but the presented solution was a direct result of work done on the IMS Learning Design specification (LD). This specification relies heavily on other specifications and ser-vices. The presented architecture is described using the example of two of such services: CopperCore, an LD service and APIS, an IMS Question and Test Interoperability service. One of the design goals of the architecture was to minimize the intrusion for both the services as well as any legacy client that already uses these services.This work has been sponsored by the EU project TENCompetenc

Designing a Learning Design Engine as a Collection of Finite State Machines

Please cite as: Vogten, H., Tattersall, C., Koper, R., van Rosmalen, P., Brouns, F., Sloep, P., van Bruggen, J. & Martens, H. (2006). Designing a Learning Design Engine as a Collection of Finite State Machines. International Journal on E-Learning. 5 (4), pp. 641-661Specifications and standards for e-learning are becoming increasingly sophisticated and complex as they deal with the core of the learning process. Simple transformations are not adequate anymore to successfully implement these latest specifications and standards for e-learning. IMS Learning Design (LD) (IMS, 2003b) is a representative of such a new specification in the field of e-learning. Its declarative nature, expressiveness and scope increase the complexity for any implementation. This probably is the largest hurdle that stands in the way of successful general deployment of this type of specifications. This article describes how an engine for interpreting LD can be designed as a collection of finite state machines (FSMs). A finite state machine is a computational model where a system is described through a finite number of states and their transition functions that map the change from one state to another. In the case of LD each state can be seen as constructed from a set of properties which can either be declared explicitly in LD or implicitly by the engine. State transitions are implemented through a mechanism of events and event handlers, completing the finite state machine. By re-using certain type of properties across FSMs it is possible to create an automatic propagation mechanism taking care of group dynamics without the need for any additional efforts. With the FSMs in place, personalization, one of the key features of LD, becomes a simple task. By combining the principles presented in the article, it becomes clear that an elegant design becomes feasible. This is demonstrated in the first actual implementation called CopperCore (Martens, Vogten, Rosmalen, & Koper, 2004).Alfanet (IST-2001-33288