Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer

Although there has been considerable research into knowledge transfer for over a century, there remains a need for specific, validated techniques for teaching for transfer. This article reports on classroom-based research in which students learned about complex systems and climate change with agent-based computer models using two different instructional approaches based on productive failure (PF). In both PF approaches, students initially explored a problem space on their own and then received teacher-led instruction. One treatment group used climate computer models whereas the other group engaged in analogical comparisons between the same climate computer models and complexity computer models in different domains. The study found both groups demonstrated significant learning gains by posttest on assessments of declarative and explanatory knowledge and on within domain near transfer. However, students in the two models treatment group performed at a significantly higher level on an across domain far transfer problem solving task. Theoretical and practical implications are considered.