Deep Learning in Intermediate Microeconomics: Using Scaffolding Assignments to Teach Theory and Promote Transfer

Abstract

Intermediate microeconomics is typically viewed as a theory and tools course that relies on algorithmic problems to help students learn and apply economic theory. However, the authors’ assessment research suggests that algorithmic problems by themselves do not encourage students to think about where the theory comes from, why the theory is relevant, or under what conditions different theories and tools should be applied. In this article, the authors draw upon current learning theory to develop a sequence of scaffolding assignments that move students from well-structured algorithmic problems to ill-structured applied problems which encourage more elaborate and robust processing of course concepts. Their assessment data suggest that these assignments promote deep learning of economic theory as well as enhanced ability to transfer learning to later courses.

Keywords:

• deep learning
• intermediate microeconomics
• scaffolding
• transfer
• writing to learn

JEL code:

• A22

Acknowledgments

The authors thank the Economics faculty at Seattle University for their participation in reviewing the scaffolding assignments for assessment purposes and the Teagle Foundation for a grant supporting this research.