AIED 2013 : 16 th International Conference on Artificial Intelligence in Education

In this paper, I bring out the contrasts between two different approaches to student learning: that of computational learning scientists and socio-cultural anthropologists, and suggest some implications and directions for learning research in MOOCs. Computational learning scientists see learning as a matter of imbibing particular knowledge propositions, and therefore understand teaching as a way of configuring these knowledge propositions in a way that takes into account the learner's capacities. Cultural anthropologists see learning as a process of acculturation or socialization--the process of becoming a member of a community. They see school itself as a social institution and the process of learning at school as a special case of socialization into a certain kind of learning style (Lave 1988); being socialized into this learning style depends on the kinds of social and cultural resources that a student has access to. Rather than see these approaches as either right or wrong, I see them as productive leading to particular kinds of research: thus, while a computational model of learning leads to research that looks at particular paths through the course material that accomplish the learning of a concept, an anthropological approach would look at student-student and student-teacher forum dialog to see how students use language, cultural resources and the affordances of the forum itself to make meaning. I argue that a socialization approach to learning might be more useful for humanities courses where assignments tend to be essays or dialogue. Finally, I bring up the old historical controversy in Artificial Intelligence: between the Physical Symbol Systems hypothesis and situated action. I argue that some of the computational approaches taken up by the proponents of situated action may be useful exemplars to implement a computational model of learning as socialization.