Optimizing Learning of Scientific Category Knowledge in the Classroom: The Case of Plant Identification

Abstract

The software program Visual Learning—Plant Identification offers a solution to problems in category learning, such as plant identification. It uses well-established learning principles, including development of perceptual expertise in an active-learning format, spacing of practice, interleaving of examples, and testing effects to train conceptual learning.

Abstract

Learning to identify organisms is extraordinarily difficult, yet trained field biologists can quickly and easily identify organisms at a glance. They do this without recourse to the use of traditional characters or identification devices. Achieving this type of recognition accuracy is a goal of many courses in plant systematics. Teaching plant identification is difficult because of variability in the plants’ appearance, the difficulty of bringing them into the classroom, and the difficulty of taking students into the field. To solve these problems, we developed and tested a cognitive psychology–based computer program to teach plant identification. The program incorporates presentation of plant images in a homework-based, active-learning format that was developed to stimulate expert-level visual recognition. A controlled experimental test using a within-subject design was performed against traditional study methods in the context of a college course in plant systematics. Use of the program resulted in an 8–25% statistically significant improvement in final exam scores, depending on the type of identification question used (living plants, photographs, written descriptions). The software demonstrates how the use of routines to train perceptual expertise, interleaved examples, spaced repetition, and retrieval practice can be used to train identification of complex and highly variable objects.