Train-to-Code: an adaptive expert system for training systematic observation and coding skills

Problems in training behavioral observers to a high degree of interindividual accuracy and intraindividual stability are fundamental concerns in descriptive research, as well as in provisions of behavioral intervention services. This article presents design characteristics of and results from three formative evaluations of an adaptive computerized expert system that shapes observation and recording skills and maximizes both individual coding accuracy and stability. The system, called Train-to-Code, allows instructors or trainers to import their own video source files and to code those videos using any appropriate descriptive behavioral-coding scheme. This generates customized expert reference data that automate subsequent training on the basis of an operant response-shaping instructional design model. Successful training relies on transitions through alternative levels of prompting and feedback designed to optimize ongoing performance until stable expert-equivalent levels of interobserver accuracy are maintained without prompting or feedback.