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The presentation began with a brief review of literature summarizing what other researchers have done with sampling distributions. There is a lack of consistent measurement tools used to measure “understanding” or “reasoning” about sampling distributions – each researcher has traditionally used a course quiz or exam, idiosyncratic to the class and professor. There is an obvious need for statistics education research to pursue consistent, reliable, and valid ways to measure reasoning about sampling distributions. This led the group to two questions: (a) what are we calling statistical reasoning, and (b) what are we building towards? These lead us back to the question of “how do we assess statistical reasoning?” One discussion thread identified one of our goals as professors is to build procedural and process knowing with our students … what can they do with the knowledge they have about sampling distributions? Looking at the concept of “sampling distribution,” what is it we want them to know exactly? And if we look at the behavior or actions of our students for research data, how do we identify what knowledge is behind those behaviors and actions? Is this knowledge emerging with the task in which they are engaged? Or is this knowledge already in place in their minds, and they are simply accessing and using that knowledge? Methods of assessing statistical reasoning that we discussed included memory and recall tasks (can you tell me what this is) and image making (e.g., concept mapping). There are a variety of methods for assessing this type of relational knowledge (Jonassen, Beissner & Yacci, 1993; Olson & Biolsi, 1991; Schau & Mattern, 1997). Does each of these methods give us reliable and valid data? Are any of them transferable to classroom assessment practices? These are questions statistics education researchers need to address before we can come up with consistent and comparable results across age levels and throughout the world. By the end of the session, the group had not looked at specific examples of data as other groups and sessions did. What we had done instead was look one-step before collecting data to evaluate (a) what data should we collect? (b) How should we collect it?, and (c) what will this data tell us about what students know, how they know it, and how they came to know it? The discussion generated many more questions that we could have possibly answered, but I believe we have set up some interesting points to ponder as we move forward in our statistics education research efforts. REFERENCES Jonassen, D. H., Beissner, K. & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale, NJ: Erlbaum. Olson, J. R. & Biolsi, K. J. (1991). Techniques for representing expert knowledge. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: prospects and limits (pp.
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