Research Apértif: Across Domain Transfer

According to dictionary.com, an apértif is a small drink of alcoholic liqueur taken to stimulate the appetite before a meal. This research apértif is likewise designed to stimulate your mind’s appetite.

If you enjoy the appetizer, click-through at the bottom of the page for the main course!

Background Research/Lit Review

1. Productive Failure (PF) process: Exploration and Generation (activate prior knowledge), Consolidation and Knowledge Assembly
2. Learning about complex systems with computer models can help students learn complex systems principles and transfer their knowledge
3. Far across domain transfer can be encouraged by allowing two scenarios to be seen as embodying the same principal (lab only, so far)

Study

1. Female 9th grade students at a high-achieving all girls school in Australia used computers to understand climate change
2. Study was conducted in 6 class periods of 80 minutes
3. One group used a single climate model and wrote down the “key ideas”
4. the other group used two models (one climate model and one non-climate model w/ similar deep structure) to compare/contrast

Findings

1. Both groups improved in declarative and explanatory knowledge
2. Students taught by an expert teacher w/ high content knowledge showed significantly higher complex systems knowledge
3. Students taught by an expert teacher showed higher performance for near within domain transfer
4. Performance for the one model group were more dependent on the quality of the teacher
5. Two model group showed better far transfer regardless of teacher expertise

Implications

1. Prior knowledge activation and differentiation may give students more chances to practice and encode critical info for the studied concept
2. Highly contrasting models may activate more prior knowledge (of structural and surface features) allowing for more connections between prior and new knowledge (creating a more integrated schema, making schema abstraction more likely)
3. It is most effective to use maximally contrasting models, w/ same deep structure along w/ explicit teach instruction about the shared deep structures of each model

Link to Article

Schema Abstraction WIth Productive Failure And Anological Comparison: Learning Desings For Far Across Domain Transfer (Free for ~50 days)

Citation

Jacobson, M. J., Goldwater, M., Markauskaite, L., Lai, P. K., Kapur, M., Roberts, G., & Hilton, C. (2020). Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer. Learning and Instruction,65, 101222. doi:10.1016/j.learninstruc.2019.101222