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Activity Number: 68 - Fostering Growth in Data Science and Analytics
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and Data Science Education
Abstract #323111
Title: Developing a Learning Map for Introductory Statistics
Author(s): Jennifer Kaplan and Angela Broaddus and Dionne Maxwell and Heidi Hulsizer*
Companies: Middle Tennessee State University and Benedictine College and Walton School District and Benedictine College
Keywords: Statistics Education; Introductory Statistics; Learning Maps; Learning Progressions

In this presentation we will describe the 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. Use of a LM can improve instruction and learning by providing diagnostic information to instructors about students in their courses, informing professional development for instructors lacking content and pedagogical knowledge, and modeling how critical prerequisites connect to learning outcomes. In the first phase of the project the research team hypothesized the StatLM, including a subset of content typically included in an undergraduate introduction to statistics course as well as pre-requisite knowledge. The team then created assessment items to validate sections of the StatLM, which were administered to a national sample of introductory statistics students. The presentation will provide a brief background on LMs and methods of validation, a description of the process used to create and validate the StatLM, and preliminary results from the validation.

Authors who are presenting talks have a * after their name.

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