Harnessing what you know

Harnessing what you know: The role of analogy in robust learning

Robert Hausmann and Timothy J. Nokes

Abstract

Knowledge transfer is a core assumption built into the pedagogy of most educational programs from K-12 to college. It is assumed that the material learned in the fourth week of the course is retained and transfers to material taught in the eighth week of the course. This is particularly true for highly structured courses such as physics; however, the empirical literature on learning suggests that far transfer is much more difficult than traditional pedagogy assumes (for reviews, see Bransford, Brown, & Cocking, 2000; Bransford & Schwartz, 1999; Gick & Holyoak, 1983). The goal of the present project is twofold. First, we will use educational data-mining models to identify knowledge components from translational kinematics that fail to transfer to rotational kinematics. Second, we will design an intervention, based upon cognitive principles from self-explanation and analogical comparison, to support knowledge components that fail to transfer.

Background and Significance

Traditional pedagogy assumes knowledge transfers between problems, units, and even courses; however, the learning literature suggests transfer is rarely observed (Detterman, 1993). Is there transfer between units in a complex science course, such as physics? If so, to what extent?

Research Objectives

Phase 1. Revise the initial knowledge-component model from the Andes physics tutor for both the translational and rotational kinematics units.

Phase 2. Develop educational data-mining models to detect the success and failure of the transfer of knowledge components. Student profiles will be defined in an effort to aggregate over individual differences in tutored help-seeking and problem-solving strategies, while still being sensitive to them.

Phase 3. Design an instructional intervention, based on cognitive science principles, to facilitate transfer between units. The format of the intervention will be designed around the literature on analogical comparison and self-explanation. The content of the intervention will be based on the revised knowledge-component model, the identification of failed knowledge-component transfer, and student profiles.

Hypotheses

H1: The learning curves from translational kinematics knowledge components can predict the error rates for rotational kinematics.

H2: Educational interventions that draw upon prior knowledge, such as analogical comparison and self-explanation, can support knowledge components that fail to transfer between translational and rotational kinematics.