This paper reports a study that examined two types of exploratory computer learning methods: self-discovery vs. co-discovery, the latter of which involves two users working together to learn a system. An experiment was conducted to compare these two methods and the results were interpreted within a mental model framework. Co-discovery subjects were better than self-discovery subjects at making inferences about the capability and extended functions of the system. Furthermore, while working by themselves after an initial period of learning, they performed better in a similar, though more complex task than the one they encountered at the learning phase. Process tracing analysis showed that self-discovery subjects focused more on surface structures, such as detailed physical actions, for implementing the task. On the other hand, co-discovery groups focused more on relating lower level actions to higher level goals. Therefore, co-discovery subjects had a better understanding of the relationships between the physical actions and goals, and hence formed mental models with higher inference potential than self-discovery subjects.