Abstract Number:
3417
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Maria Cruciani (1), Justin Post (2), Jennifer Green (1), Sunghwan Byun (2)
Institutions:
(1) Michigan State University, N/A, (2) North Carolina State University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Inquiry-based activities allow students to explore questions they find interesting and applicable, motivating deeper engagement in a given task. Authentic data situates inquiry-based statistical explorations in meaningful contexts and advances the development of students' data acumen. We have designed an inquiry-based activity that authentically explores the distributions of student loan debt from postsecondary institutions. It is the first activity in a series of three that offers students rich experiences exploring questions about authentic data through R Shiny applets. The activity's goal is to strengthen students' data exploration skills while furthering their understanding of the normal distribution. The learning objectives of calculating, interpreting, and drawing conclusions from z-scores, percentiles, and proportions in addition to standardizing and comparing normally distributed data are met. Students will learn to standardize and compare distributions in a context that is relevant to their lives, will gain experience taking the lead on statistically investigating a question that is interesting to them, and will practice communicating their results to others.
Keywords:
Inquiry-based |Authentic Data|Undergraduate Introductory Statistics|Statistics Education|The Normal Distrubtion|R Shiny Applets
Sponsors:
Section on Statistics and Data Science Education
Tracks:
Miscellaneous
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