Optimizing learning from examples using animated pedagogical agents.

Abstract

This study attempted to optimize a computer-based learning environment designed to teach learners how to solve word problems by incorporating an animated pedagogical agent. The agent was programmed to deliver instructional explanations either textually or aurally, while simultaneously using gaze and gesture to direct the learners to focus their attention on the relevant part of the example. In Experiment 1, learners presented with an agent delivering explanations aurally (voice plus agent) outperformed their control peers on measures of transfer. In Experiment 2, learners in the voice-plus-agent condition outperformed their peers presented with textual explanations on a variety of measures, including far transfer. In sum, an animated agent programmed to deliver instructions aurally can help optimize learning from examples. A worked example is an instructional device that provides a model for solving a particular type of problem by presenting the solution in a step-by-step fashion. It is intended to provide the learner with an expert’s solution, which the learner can use as a model for his or her own problem solving. To date, in most experiments, worked examples have been visually fixed; that is, the examples simultaneously presented a problem and an expert’s