Deterministic and Probabilistic Theorizing in Structural Learning

In the previous chapters a variety of formal models of the teaching-learning process have been presented. With one major exception, however, all of the models have a largely stochastic foundation. The structural learning theory (SCANDURA, 1973a, forthcoming, also this volume) is based (partly) on the assumption that deterministic theorizing about teaching and learning might actually provide a more useful first step. Moreover, it was suggested that there are close relationships between deterministic and probabilistic theories which might increase both the generality and the usefulness of both types of theory. The present contribution is designed to put these insights and conjectures regarding deterministic and probabilistic theories on a firmer theoretical base. In particular, the goal is to identify and illustrate conditions that allow for probabilistic extensions of the deterministic theory of structural learning.