Academic journal article Journal of Interactive Learning Research

Formalization to Improve Lifelong Learning

Academic journal article Journal of Interactive Learning Research

Formalization to Improve Lifelong Learning

Article excerpt

SYLVIE RANWEZ [1]

TORSTEN LEIDIG [2]

MICHEL CRAMPES [1]

Utilizing the World Wide Web (Web) for delivering lifelong training services to individuals or communities raises great interest in intelligent methods for adaptive course curriculum building. People notice that it is insufficient to make training materials accessible by way of the browser. To satisfy learner requirements, the learning service has to give a highly customized and up-to-date view. Moreover, training is an interactive and collaborative process. The flow of the curriculum has to follow some didactic principles. Learners need to be guided through the training material according to these principles and their own preferences. A conventional hypertext system cannot provide this, mainly because the pedagogic semantics of the material are not "known" by the system.

This article presents a new approach for adaptive, computer-supported learning services within our Lifelong Learning project. This approach is based on a formalization of pedagogical knowledge, about learning material and learning processes, as well as the domain knowledge and a learner model. The goal of our research is to integrate efficient and reusable pedagogical knowledge representations into an adaptive learning system (and authoring tools). The article concentrates on the Didactic Planning obtained by various Pedagogical Strategies. Therefore, we focus on the strategy formalization using an expressive knowledge representation based on a dedicated learning ontology using conceptual graphs. The power of our approach for designing a new kind of adaptive learning system is demonstrated through examples of pedagogical strategies applied to course material.

The Internet creates a great interest in education. It brings out many exciting features to support trainees and trainers such as distant learning, course delivery, learner-teacher communication, and communication between learners in distributed rooms. Learning as a lifelong and expansive process reinforces this interest, particularly in industries where large amounts of money are invested in employee training.

Hypermedia systems have been widely used to store and explore large amounts of related information. Hypermedia systems are also often used for learning. However, the hypermedia model is very focused on linking of information and has little support to express the semantics of an application domain. Therefore hypermedia systems are not very well suited for semantic-driven applications and adaptive learning style modelling.

One of the fundamental problems is how to traverse the structures with a good strategy to find the right information. Pure, navigational-based systems have been severely criticized in the domain of education because the learner is often left free to choose whatever link they feel is attractive (Lindard & Zeilinger, 1995). Scalability is not well supported, thus authoring, navigation, and maintenance in the hypermedia net is too complicated if the number of nodes and links grow too large.

The Web has become a very popular Hypermedia system. For several reasons, the Web has become a de-facto standard, and it is often used as an infrastructure for Intranet and Internet applications. However, we observed that little research has been done in making web-based learning more interactive in the sense of applying pedagogical or didactic guidance to the learner and allowing those systems to reuse pedagogical materials. Several required characteristics in the context of hypermedia and learning need a closer look:

Adaptation. An adaptive authoring tool allows teachers to apply several pedagogical strategies and to design their courses.

Navigational support. A support for learner navigation through the course material is necessary. The learner has to be supported by an adaptive interface that takes individual performance into account (what that learner already has done, preferences, results of some tests, etc. …

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