Starting from a general framework for web-based e-learning systems that is based on an abstraction layer model, this paper presents a conceptual modelling approach, which captures the modelling of learners, the modelling of courses, the personalisation of courses, and the management of data in e-learning systems. Courses are modelled by outline graphs, which are further refined by some form of process algebra. The linguistic analysis of word fields referring to an application domain helps to set up these course outlines. Learners are modelled by classifying value combinations for their characteristic properties. Each learner type gives rise to intentions as well as rights and obligations in using a learning system. Intentions can be formalised as postconditions, while rights and obligations lead to deontic constraints. The intentions can be used for the personalisation of the learning system to a learner type. Finally, the management of data in an e-learning system is approached on two different levels dealing with the content of individual learning units and the integrated content of the whole system, respectively. This leads to supporting databases and views defined on them.

Abstract:The increasing attention paid to the importance of context-awareness of content has contributed to more useable e-Iearning websites. While existing approaches mostly cover static learner characteristics, to date it has been difficult to cover the dynamics of context. E-Iearning content must be delivered in the right doses, at the right moment, and in an appropriate form.Our work presents a storyboarding-based approach for specifying contentintensive websites. E-Iearning sites can be validated with regard to learner context. The practicability of our method is shown upon the e-Iearning project DaMiT. The SiteLang methodology helps overcome the limitations of existing approaches based on pre-dermed, structuring of content. It allows a flexible content generation upon learner context, depending on the dynamics of the learning process.Keywords: Database, Learner Centred Learning, Learning Systems, Modelling INTRODUCTIONMany scientific papers and working group specifications have identified the fundamental insight that a high level of granularity of learning units leads to better user-adaptive systems. One guideline for implementing this is to deal with many different types of units from the same topic. Learners just want the most appropriate content delivered just in time and to the right place and device. They should not be confronted with unnecessary content. Therefore, some services are needed to fulfil the learner's needs. Information logistics can provide acceptable strategies that satisfy the requirements for the delivery of the correct unit of information. The goals of information logistics are readily adapted to the e-cha1lenges of e-Learning. Information logisticsInformation logistics aims to provide optimised information-to serve textually correct and needed information at the right time and place-to users (Lienemann, 2001). The information should always be adapted to the user's preferences and communication facilities. Lienemann explains some principles in information logistics, which are relevant to e-Learning.Several information sources: The user looks for additional or extended information in distributed, correlated content bases. At the moment we restrict this to one database of one domain. Information on the tick: Information at the right time depends on the value of the information and the user's context. The value depends on the deliverable content in the knowledge bases. High value demands a detailed description of the content. Users of an e-Learning system may interact with the system in different roles with special characteristics. This is the primary context for the user. The system has to choose, on demand, what the preferred content for the user at any given moment. Consideration of user preferences:Applications of information logistics must be able to satisfy the individual needs of the users. User needs must be specified for optimal operation and implemented in an explicit and an implicit way. Explicit data, like the preferred presentation style or difficulty, must be treated as g...

e-learning systems will provide new pathways to knowledge and skills acquisition. To increase their quality using a parametric learner model is proposed. Tailoring the learning material presented to the respective parameter values may fit this material to the actual learner's needs.We use a stereotype user model. Our learner characteristics complement the IMS LIP. We first represent learners by points in a learner space. Then we propose increasingly generic ways of grouping together such points to form learner types. The less generic one models learner types using convex regions in the learner space. The more generic one models learner types using characteristic functions. While the latter approach allows for more flexible learner type combination the former approach allows for convincing definition of initial learner types. Combining both approaches thus may result in a reasonable number of useful learner types.

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