In this workshop, we invite speakers from a variety of approaches to broadly inform our understanding of active learning, including cognitive development, education, and computational modeling. We examine what “active” means in active learning, and present talks on the cognitive mechanisms that might support active learning, including attention, hypothesis-generation, explanation, pretend play, and question asking. We also explore how efficient learners are when planning and executing actions in the service of learning, and whether there are developmental or socioeconomic differences in active learning. We integrate the problem of active learning with teaching to investigate the similarities and differences involved in selecting evidence for oneself and others. Throughout we ask how we can capture these processes with computational models that spell out the underlying assumptions and potential algorithms.