The classroom activity described here is a structured problem series developed for students to discover concepts themselves. Among psychology students, introductory statistics is a course which often is less appealing than other courses. As a result, one of the major challenges in teaching it to undergraduates is making the material both interesting and relevant to the student's personal experience. This is particularly true in relation to other courses in the major, where the self-referential nature of the content insures at least some degree of relevance. During the past three years, I have taught introductory statistics courses to classes which included not only psychology majors but also education and biology students. The students of these courses and feedback from students has convinced me that a few key features of the course structure and manner of presentation of the material are primarily responsible for making the courses effective and enjoyable. These features all relate the material to the direct experience of the students. This approach has a strong justification of both educational theory (e.g., Dewey, 1938) and from psychological research (e.g., Craik & Lockhart, 1972); material made meaningful in this way is more likely to be assimilated and retained. In particular, the aspect of individual experience to which the statistical material is conceptually related is the manner in which knowledge is gained. This will be elaborated later in the article; the justification of this approach can be made in terms of the nature of the discipline as well as pedagogically. Statistical inference is directly concerned with specifying principles by which scientific knowledge is gained; be relating the content of statistics to one's own experience of gaining knowledge, one sees more clearly the core of the discipline. This paper first describes the classroom activities which have been features of this approach; it then reviews the manner in which statistical principles have been conceptually related to the students' experience of gaining knowledge.
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