DCG + GTE: Dynamic Courseware Generation with teaching expertise

This paper discusses the place of GTE as an approach for bridging the gap between CAL and ITS systems. DCG is our architecture for dynamic courseware generation, which allows dynamic planning of the contents of an instructional course with a given goal. Finally, a further development of DCG by combining it with GTE is described which includes explicit representation of generic teaching knowledge. This allows dynamic planning of how a selected contents will be presented to the student.     

Dynamic Courseware Generation on the WWW

We have developed a tool for the authoring of adaptive CAL courses, called "Dynamic Courseware Generator" (DCG). It generates an individual course according to the learner's goals and previous knowledge, and dynamically adapts the course according to the learner's success in acquiring knowledge. The DCG runs on a WWW server. The learner receives from this server an individualised course targeted to a specified goal. Afterwards, s/he is adaptively guided by the course through a space of teaching materials on the WWW. Unlike other CAL courses on the WWW, a course produced by the DCG is interactive, it tests the learner's knowledge and dynamically adapts to the student's progress. The authoring tool can be used also for collaborative authoring and learning.     

SocConnect: A personalized social network aggregator and recommender

Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends’ social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends’ updates and other activities). To address these two problems, we propose a user-centric system called “SocConnect” (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends’ activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends’ activities and help users deal with cognitive overload.