Open-Ended Data Analysis Collaboration in the Introductory Statistics Course

Abstract

In today's data-centric society, being able to analyze data and communicate results is crucial and central to any introductory statistics course. In addition, the ability to collaborate with others successfully post-degree has only grown in importance and is now increasingly a focus in capstone-level courses and statistics & data science curricula. Ideally, an introductory course in statistics should include cooperative activities for real world data analyses that support student-driven inquiry instead of solely memorization of mathematical formulas. Teachers who wish to incorporate such activities into their introductory classes face the challenge of developing entry-level group data analysis activities in a framework that allows for assessment of these collaborations. In Carnegie Mellon's introductory statistics course, students have the opportunity to do open-ended data analysis activities via the Integrated Statistics Learning Environment (ISLE), which includes a data explorer toolbox that supports data import to visualization to hypothesis testing to reports/presentations. Collaboration features of the editor allow students to form groups and analyze data sets together on the same browser-based platform without coding, write reports together, or peer review each other's work (supported via integrated comments and chat functionality.) With a report history feature that allows the playback of each respective user's actions, instructors can analyze, for example, the sequence of steps the students have taken in the construction of a group report to provide both individualized and group feedback on decisions as well as writing style/syntax. This breakout session will present and discuss early results from collaborative introductory-level activities from the 2019-2020 academic year, including student feedback. While ISLE is the specific vehicle for our courses, the lessons learned about collaboration and related classroom insights will support next steps in the broad development of effective, scalable group activities. We will also discuss data analysis activities that could be supported on other software platforms or with pencil-and-paper. Session attendees will have the opportunity to brainstorm, design, and collaborate on data analysis activities via ISLE.

Visit: http://www.stat.cmu.edu/isle