Mechanistic explanations in the learning sciences as common ground for a more cumulative knowledge production

Abstract

Human learning is studied at multiple levels and timescales, and different research communities within the interdisciplinary field of learning research have accumulated specialized knowledge about various facets of the learning phenomenon. However, productive dialogue between such communities is also important for a continuous maturation of the field, yet this dialogue is difficult to achieve because it is dependent upon sufficient common ground in terms of how the issue of explanation is approached epistemologically and methodologically. The aim of this article is to discuss how mechanisms and mechanistic explanations, rooted in new mechanistic philosophy, can constitute a sufficient common ground for a more cumulative knowledge production in the learning sciences and learning research. The argument is that a mechanistic stance, as a meta perspective, can improve the quality of the learning research because it allows for the development of novel explanatory models that can connect multiple levels and timescales in the study of learning, and thus strengthen connections between specialized research communities and their accumulated knowledge.