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|Title: ||Alfanet Deliverable D32 – Standards Contribution Report|
|Authors: ||Van Es, René|
Van der Baaren, John
Carion, Maria Jose
Learning technology specification
|Issue Date: ||26-Apr-2005|
|Abstract: ||Learning technology standards are widely promoted by various organisations internationally because they potentially have several advantages. Such advantages are for example reusability of learning content, or the reuse of pedagogical scenarios. The current situation is that many learning technology standards have been created and evaluated in limited settings. The aim of the Alfanet project was to use a suite of learning technology standards rather than a single standard to realise adaptive features for learners that otherwise could not have been realised. This has increased the initial investment in development but has allowed us to build an open architecture composed by re-usable components. aLFanet applies important standards in e-learning. The central standard is IMS LD. It enables the design of a variety of pedagogical models and separates the design of the pedagogical model from the content. Thus, it allows to dynamically select from the available learning objects the content to be provided depending on the associated metadata. To complement this standard, IMS Metadata (IEEE LOM) to deal with the knowledge on the contents, IMS LIP for a representation of the user and the IMS QTI to compute the formal progress are used. On top of them, everything is delivered in IMS CP. In addition Alfanet makes use of a set of technical standards in particular SOAP and FIPA.
This report describes the standards used, the Alfanet adaptation scenarios and the role of the standards in these scenarios. The report contains two parts.
In the first part of this report the standards used are introduced and briefly explained and a study on IMS LD, the central standard in the project, is described. Learning Design has been developed to support any kind of education and some prove has been provided by use-cases as presented in the Best practice guide. However, at the time Alfanet selected IMS LD there was no information on how suitable LD is to describe education as it is delivered currently by various educational institutes. Therefore, an investigation was set up to find out what problems would occur if we would try to describe a random sample of lesson plans with LD. Here the findings are reported and special cases that needed more attention are elaborated in more detail.
The second part of the report (chapter 4 and onwards) starts with an overview of the main adaptation scenarios supported in Alfanet. They include adaptation to pre-knowledge, adaptation to learning characteristics, adaptation on assessments and adaptation based on similarities with peers. Subsequently each of these scenarios is explained in a separate chapter and the way the standards are applied to achieve the desired adaptation is clarified. The adaptive features of the Alfanet system are triggered by the data that is collected on individual learners which serves as a base to alter a course for an individual or these data are used to reason upon collective learner behaviour and make alterations in courses for groups of learners. The learner data is stored in a portfolio.
This report describes how determining a learners’ learning style, cognitive modality and his/her present knowledge on a course specific domain creates the initial learner profile. It is explained how, based on the learners’ profile, changes are made to the outline of the course material and what standards are used to realise these adaptations
To determine if a learner makes progress in mastering the learning material, the learner has to take formal assessments during the course, but a learner can also periodically test his/her learning progress. In the Alfanet system tests are used, and two adaptation mechanisms were created that make use of the outcome of these tests. It is explained how question items are created and how adaptive tests can be created out of these individual test items with additional meta-data and specifically created tool. Moreover a description is given how the test results are handled by the Alfanet system to feed them back into the Learning Design of a course.
Besides designed course adaptation the Alfanet system is also equipped with a module that monitors user behaviour and searches in the course material to recommend learner and situation specific interventions that should keep learners motivated and enhance learner performances. It is explained what kind of measures have to be taken at design time to realise this functionality at run-time.
Finally, when courses are designed, the authors have certain expectations with regard to how learners will react to that course. For example, learners are expected to complete a course within a certain amount of time, or authors design a route and learners are expected to follow this route, but are they? To answer questions like these the Alfanet system is equipped with audit functionality that monitors learner behaviour and compares this behaviour with predefined expectations by authors. The results are reported back to the authors with the expectation that when a bottleneck is found in a course it can be put right.|
|Appears in Collections:||5. LN: Deliverables, Reports, Work Documents|
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