This paper describes a collaborative educational data mining tool based on association rule mining for the ongoing improvement of e-learning courses and allowing teachers with similar course profiles to share and score the discovered information. The mining tool is oriented to be used by non-expert instructors in data mining so its internal operation has to be transparent to the user and the instructor can focus on the analysis of the results and make decisions about how to improve the e-learning course. In this paper, a data mining tool is described in a tutorial way and some examples of rules discovered in an adaptive web-based course are shown and explained. (Contains 10 figures and 4 tables.)