Concept Mapping in Introduction to Statistics


Marjorie Bond, Monmouth College


Concepts maps were developed in the 1970's. They are based on the theory of knowledge and the theory of learning. Knowledge is built up from concepts and propositions. Propositions are the combination of two or more concepts in which the leaner has made a relationship between the concepts. By using concepts maps, the user has a physical graph which represents structured knowledge. This structured knowledge is a representation of how their brain stores information. In order to store knowledge or to learn, each person needs to construct for themselves relationships between concepts and propositions. This is often building on past knowledge. Meaningful learning requires a well-organized-relevant-knowledge structure and an emotional commitment to integrate the new with the existing knowledge.

Students in Introductory Statistics classes often miss the forest because all they see are the trees. How often do instructors lament that the students are missing the big picture? The instructors see the elegance of moving from the sampling distribution to test of significance or confidence intervals, but our students are simply lost. Can the use of concept maps help the students see the big picture and more importantly experience meaningful learning? To these questions, I do not have an answer, but I hope to investigate.

During our time together, you'll receive more information about concept mapping and a bit of information concerning software for mapping. Most importantly, we will create concept maps for different topics in an introductory statistics class and present the concept maps to others.


Marjorie Bond is an associate professor at Monmouth College, a small liberal arts college in Illinois. She received her Ph.D. from Kansas State University in 1996 and her M.A. and B.S. from University of New Mexico. Her main research interest is in Statistics Education.


Handout (PDF)