Instructional strategies to help learners build relevant mental models in inferential statistics


Authors: 
Hong, E., & O'Neil, H. F., Jr.
Category: 
Volume: 
84(2)
Pages: 
150-159
Year: 
1992
Publisher: 
Journal of Educational Psychology
Abstract: 

This study attempts to identify the relevant mental model for hypothesis testing. Analysis of textbooks provided the identification of the declarative and procedural knowledge that constitute the relevant mental models in hypothesis testing. A cognitive task analysis of intermediates' and experts' mental models was conducted in order to identify the relevant mental models for teaching novices. Of interest were the steps taken to arrive at the solution and the representations that were used in the problem solving process. Results indicate that experts and intermediates differ in their conceptual understanding. In addition, diagrammatic problem representation was useful in for all particularly for the intermediates. On this basis, the intermediate models were deemed relevant for instructing novices. Two instructional strategies were investigated: presentation sequence (concepts and procedures taught separately or together) and presentation mode (diagrammatic vs. descriptive). Based on their findings, the researchers conclude that meaningful learning occurs when conceptual instruction is provided prior to the procedures, that is, when they are taught separately rather than concurrently, and when a diagrammatic strategy was utilized rather than a descriptive method. This facilitates development of representational ability for understanding hypothesis testing. In short, using separate and diagrammatic representation strategies are effective for teaching novices in the area of hypothesis testing. The researchers conclude that by developing relevant mental models through this type of instruction, the learner's knowledge can be more accessible (awareness of knowledge), functional (predict or explain), and improvable.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education