Adaptive e-Learning systems are able to adjust to a user's learning needs, usually by user modeling or tracking progress. Such learner-adaptive behavior has rapidly become a hot topic for e-Learning, furthered in part by the recent rapid increase in the use of MOOCs (Massive Open Online Courses). A lack of general, individual, and situational data about student populations currently hampers the infusion of effective adaptive behavior into existing e-Learning platforms. This contribution presents original research on using differences in individual learning styles. Factors related to performance, motivation, satisfaction, and previous knowledge were targeted and used to assess the effectiveness of the approach. We discuss alternative bases for adaptation (e.g. cognitive styles), style distributions in student populations, and conclude with repercussions for adaptive behavior in HCI in general. [For the full proceedings, see ED562095.]