An approach to tracking and analysing interactions in CSCL environments

In this paper an approach is presented that supports tracking and analysis of interactions within an online course with the twofold aim of evaluating the overall quality of the learning process and monitoring students' performance. Focus of the paper is on what data can be collected and how they can be gathered and analysed to provide course designers, tutors and researchers with all the information needed to inform monitoring at run-time and validation at the end of the course. The approach proposes to distinguish four kinds of data: raw, additional, subjective and aggregated data. Raw data consist of quantitative data about message exchanges and are usually stored automatically by the communication system used. Additional data have to do with course and target population features and are normally available in course documentation. Subjective data are quantitative information obtained by processing subjective aspects such as opinions of participants or content analysis of messages. Finally, aggregated data include any elaboration or the above, including graphical representations and statistics. Finally the paper provides examples of how and by whom these data can be used to inform monitoring and evaluation of an online course, pointing out some of the questions that can be addressed through the analysis of these information.