Franka Grünewald

Uniting the Social Web and Topic Maps with Tele-Teaching to Provide User-Friendly Interaction Possibilities with E-Lectures

Lecture recordings, so called e-lectures, are widely spread across universities. But, concerning those e-lectures, there exist two problems. On the one hand, it is very easy for users just to lean back and not become active. From the perspective of learning theory, this is no desirable state. On the other hand, it is a challenge to find relevant content from the mass of lecture recordings when at the same time there is not a sufficient amount of metadata available. Due to these two issues, this dissertation deals with the utilization of Web 2.0 and Semantic Web technologies to provide interaction possibilities with e-lectures. The aim of the tools developed in this work with the help of Web 2.0 and Semantic Web technologies is to activate users and the support the search process for learning content. The students watching the e-lectures should be able to actively work with learning material, collaboratively acquire new learning content and independently identify new topic areas.

Initially, many user functions like tagging, rating and playlists were implemented in order to activate the participants. However, user activity was still low. Reasons identified were e.g., little incentive for participation and little added value of the function beyond the activity. A collaborative annotation environment with participative elements was implemented and evaluated as one solution for the participation problem. Furthermore, the possibility to generate added value for students was researched. Since finding the relevant content is the another major issue, the added value was to tackle this problem. A semantic topic map was implemented as an extension to the lecture video annotation function. With the help of methods from the Semantic Web, keywords within the user-generated annotation can be linked to topic maps. These topic maps visualize the context of the keywords and indicate appropriate learning videos.

The evaluation of the annotation functions and the topic map was conducted by expert review and user tests.