In this paper we address the issue of monitoring students’ and groups’ activity in online collaborative learning environments. This issue is especially important in the collaborative e-learning context, since an efficient monitoring process can provide valuable information to online instructors who may guide and support the development of collaborative learning projects. We have developed and tested an information system model which facilitates the automatic generation of weekly monitoring reports derived from data contained in server log files. These reports provide online instructors with visual information regarding students’ and groups’ activity, thus allowing for a quick and easy classification of students and groups according to their activity level. Therefore, entities with a low activity level are identified as soon as possible and just-in-time assistance can be established for them. Furthermore, instructors can use these monitoring reports to forecast potential problems –such as students’ dropouts or possible conflicts inside the groups due to unbalanced distribution of tasks– and take operational and tactical decisions oriented to avoid them

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