Developing and Validating a Learning Map for Introductory Statistics

Angela Broaddus
Associate Professor
Benedictine College

In this workshop we will discuss processes for and preliminary results of the NSF funded StatLM Project, designed to build and validate a Learning Map (LM) for the content of introductory statistics: the StatLM. LMs provide a graphical representation of learning targets and connections to depict a theory of how knowledge develops in a domain. LMs can influence instruction, learning, and assessment development by modeling how prerequisites connect to learning outcomes. The first phase of the project focused on building the StatLM, modeling a subset of content typically taught in undergraduate statistics. The StatLM organizes learning outcomes related to single quantitative variables, including a proposed hierarchy of how the learning outcomes may be ordered or related. To examine the accuracy StatLM, we needed to compare its structure to students’ knowledge of same outcomes modeled in the StatLM. The research team created an assessment containing items focused on selected learning outcomes in the StatLM. For each selected learning outcome, four items were written with the same question but different contexts. All items were reviewed for content, language, and bias to ensure quality and accessibility of the items in a manner consistent with assessment best practices. The items were piloted at multiple stages of development, with appropriate edits and adjustments implemented at each stage. The StatLM assessment was administered to a national sample of introductory statistics.