Modelling sociocognitive aspects of students’ learning

We present a computational model of sociocognitive aspects of learning. The model takes into account a student’s individual cognition and sociodynamics of learning. We describe cognitive aspects of learning as foraging for explanations in the epistemic landscape, the structure (set by instructional design) of which guides the cognitive development through success or failure in foraging. We describe sociodynamic aspects as an agent-based model, where agents (learners) compare and adjust their conceptions of their own proficiency (self-proficiency) and that of their peers (peer-proficiency) in using explanatory schemes of different levels. We apply the model here in a case involving a three-tiered system of explanatory schemes, which can serve as a generic description of some well-known cases studied in empirical research on learning. The cognitive dynamics lead to the formation of dynamically robust outcomes of learning, seen as a strong preference for a certain explanatory schemes. The effects of social learning, however, can account for half of one’s success in adopting higher-level schemes and greater proficiency. The model also predicts a correlation of dynamically emergent interaction patterns between agents and the learning outcomes.