Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach

Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A low- and a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a low- and a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples. In complex cognitive domains such as mathematics, phys-ics, or computer programming, problem solutions can often be characterized by a hierarchical goal structure. The goal of these solutions can be attained only by successfully attaining all subgoals. Learning and performance of complex cogni-tive tasks are typically constrained by limited processing ca