Computational Learning Analytics to Estimate Location-Based Self-Regulation Process of Real-World Experiences

Abstract

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 realworld 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' inter-cluster moves functioned to determine whether they maintained or changed their contents and strategies of realworld 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.