Educational Data Mining (EDM) is a growing field of use of data analysis techniques. Speci fically, we consider partial implications. The main problems are, fi rst, that a support threshold is absolutely necessary but setting it "right" is extremely di fficult; and, second, that, very often, large amounts of partial implications are found, beyond what an EDM user would be able to manually inspect. Our program yacaree, recently developed, is an associator that tackles both problems. In an EDM context, our program has demonstrated to be competitive with respect to the amount of partial implications output. But "fi nding few rules" is not the same as "fi nding the right rules". We extend the evaluation with a deeper quantitative analysis and a subjective evaluation on EDM datasets, eliciting the opinion of the instructors of the courses under analysis to assess the pertinence of the rules found by diff erent association miners.