Aiding transfer in statistics: Examining the use of conceptually oriented equations and elaboration's during subgoal learning

Consistent with a subgoal-learning approach, the authors hypothesized that learners who studied statistics examples with conceptually oriented equations would transfer more successfully to novel problems compared with learners who studied examples using computationally oriented equations. The conceptually oriented equations were designed to capture the relationship between the concept and its computation, whereas the computationally oriented equations were designed to simplify calculations. This hypothesis was supported across 2 experiments. The authors also examined the implications of providing learners with elaborations of the procedures illustrated in the examples either before or after they studied them. The location of the elaborations had no apparent effect. Overall, these results demonstrate that solution procedures organized around appropriate conceptually oriented subgoals are easier to adapt for novel problems than procedures built around computationally friendly, but conceptually opaque, steps.