The framework and procedures of generalizability theory are introduced and illustrated in this instructional module that uses a hypothetical scenario involving writing proficiency. Generalizability analyses are useful for understanding the relative importance of various sources of error and for designing efficient measurement procedures. (SLD)

Presents an introductory lesson on remote sensing and image processing to be used in cooperative groups. Students are asked to solve a problem by gathering information, making inferences, transforming data into other forms, and making and testing hypotheses. Includes four expansions of the lesson and a reproducible student worksheet. (MKR)