Computer learners often develop explanations of events they observe during training. Recent work on generalization suggests that explanations may be valuable in permitting learners to develop generalizations from one or a few examples. This study explores the idea by describing four generalization paradigms in which explanations play a part: explanation-based generalization (EBG), structure mapping analogical generalization (SMAG), modificational analogical generalization (MAG) and synthetic generalization (SG). It describes a model, the EXPL system, which is capable of applying MAG or SG to the generalization of simple procedures in human-computer interaction. It presents evidence that EXPL's analysis procedure, which constructs explanations as needed by MAG or SG, embodies heuristic principles used by human learners, and that MAG provides a good account of some human generalization, when retention of examples is not a problem. (Author/JAZ)