The last decade has witnessed considerable interest in the investigation of the affective dimensions of learning and in the development of advanced learning technologies that automatically detect and respond to student affect. Identifying the affective states that students experience in technology-enhanced learning contexts is a fundamental question in this area. This article provides an initial attempt to answer this question with a selective meta-analysis of 24 studies that utilized a mixture of methodologies (online self-reports, online observations, emote-aloud protocols, cued recall) and affect judges (students themselves, untrained peers, trained judges) for fine-grained monitoring of 14 discrete affective states of 1,740 middle school, high school, college, and adult students in 5 countries. Affective states occurred over the course of interactions with a range of learning technologies, including intelligent tutoring systems, serious games, simulation environments, and simple computer interfaces. Standardized effect sizes of relative frequency, computed by comparing the proportional occurrence of each affective state to the other states in each study, were modeled with random-effects models. Engagement/flow was consistently found to be relatively frequent (d[subscript +] = 2.5), and contempt, anger, disgust, sadness, anxiety, delight, fear, and surprise were consistently infrequent, with d[subscript +] ranging from -6.5 to -0.78. Effects for boredom (d[subscript +] = 0.19), confusion (d[subscript +] = 0.12), curiosity (d+ = -0.10), happiness (d[subscript +] = -0.13), and frustration (d[subscript +] = -2.5) varied substantially across studies. Mixed-effects models indicated that the source of the affect judgments (self vs. observers) and the authenticity of the learning contexts (classroom vs. laboratory) accounted for greater heterogeneity than the use of advanced learning technologies and training time. Theoretical and applied implications of the findings are discussed.