Constantly embedding information and knowledge about group activity into on-line collaborative learning is a challenging yet one of the latest and most attractive issues to influence learning experience in a positive manner. The possibility to enhance learning groupâ¿¿s participation by means of providing appropriate knowledge is rapidly gaining popularity due to its great impact on group performance and outcomes. Indeed, by storing parameters of interaction such as participation behaviour and giving constant awareness and feedback of these parameters to the group may influence groupâ¿¿s motivation and emotional state as well as enhance the learnersâ¿¿ and groupsâ¿¿ problem solving abilities. This implies a need to capture and structure the information generated by group activity and then to extract the relevant knowledge in order to provide learners and tutors with efficient awareness and feedback as regards group performance and collaboration. To that end, we first identify and define the main types of information generated in on-line group activity and then we propose a process for efficiently embedding this information and the knowledge extracted into collaborative learning applications. However, in order to provide learners with effective knowledge, it is necessary to process large and complex event log files from group activity in a constant manner, and thus it may require computational capacity beyond that of a single computer. To that end, in this chapter we show how a Grid approach can considerably decrease the time of processing group activity log files and thus allow group learners to receive selected knowledge even in real time.