By Alana Unfried (California State University, Monterey Bay) et al.
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How do you know if your teaching is effective in introductory statistics and data science courses? Is it grades? Is it anecdotes from students about how much they loved your class? We can do better! This poster will discuss the Motivational Attitudes in Statistics and Data Science Education (MASDER) NSF project, through which our team is developing a family of six instruments that allow statistics and data science educators and researchers to identify evidence-based best practices in our disciplines. The MASDER team has developed separate surveys assessing student attitudes toward statistics and data science, and instructor attitudes toward teaching statistics and data science. Additionally, we have created inventories for data collection on the classroom learning environment. Through triangulating these data sources and collecting data nationally, we can develop a robust picture of the current state of statistics and data science education in the US. Come learn more about the surveys, our website portal for getting access to the surveys, and preliminary findings from our first pre/post data collection from 20 institutions across the US.
Authors: Alana Unfried (California State University, Monterey Bay), Leyla Batakci (Elizabethtown College), Marjorie Bond (Penn State), April Kerby-Helm (Winona State University) Michael A. Posner (Villanova University), Douglas Whitaker (Mount Saint Vincent University)