A learner can autonomously acquire knowledge by experiencing the world, without necessarily being explicitly taught. The contents and ways of this type of real-world learning are grounded on his/her surroundings and are self-determined by computing real-world information. However, conventional studies have not modeled, observed, or understood a learner's self-regulation mechanism of real-world learning. This study developed computational learning analytics to estimate how this mechanism works. Our analytics segmented a time series of real-world learning into units of a cognitively closed and semantically independent function by estimating the spatiotemporal clusters of a learner's concentrated stay behavior. We found that learners' intercluster moves functioned to determine whether they maintained or changed their contents and strategies of real-world learning. We also found that the spatiotemporal sizes of the estimated clusters were correlated with the activeness and diversity of strategy-based content examinations at each location. This study forms a basis for automatically estimating qualitative transitions of real-world learning and encouraging a learner to obtain a better understanding of the world.