Association rules constitute a well-known and widely employed data mining technique. We study their applicability in Educational Data Mining. We develop a case study of datasets from that eld: logs of an e-learning platform. We demonstrate that it is convenient to analyze such datasets in terms of association rules that relate not only presence of items in each of the transactions, but also their absence. To cope with the algorithmic di culties and the large output, we apply a new heuristic regarding the support of negative attributes, complementing two previously studied contributions: a basis for closure-oriented notions of redundancy and a notion of novelty called the con dence boost. Our ndings have been validated through interactions with end-user experts, namely, the instructors in whose virtual learning courses the datasets had their origin.