Most learning models assume players are adaptive (i.e., they respond only to their own previous experience and ignore others' payoff information) and that behavior is not sensitive to the way in which players are matched. Empirical evidence suggests otherwise. In this paper, we extend our adaptive experience-weighted attraction (EWA) learning model to capture sophisticated learning and strategic teaching in repeated games. The generalized model assumes that there is a mixture of adaptive learners and sophisticated players. Like before, an adaptive learner adjusts his behavior the EWA way. A sophisticated player however does not learn and rationally best-responds to her forecasts of all other behaviors. A sophisticated player can be either myopic or foresighted. A foresighted player develops multiple-period rather than single-period forecasts of others' behaviors and chooses to 'teach' the other players by choosing a strategy scenario that gives her the highest discounted net present value. Consequently a foresighted player can develop a reputation for herself by strategic teaching if she is matched with an adaptive player repeatedly. We estimate the model using data fromp-beauty contests and repeated entry-deterrence (chain-store) games. Overall, the results show that the generalized model is better than the adaptive EWA model in describing and predicting behavior. Including teaching also allows an empirical learning-based approach to reputation formation which is at least as plausible as the now-standard type-based approach, and is superior in predictive performance in four different ways.