Teaching and Evaluating Data Communication At Scale

Description

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Communication of statistical findings, in addition to the computational procedures used to reveal them, is an important practice we want to teach our students. However, evaluating the quality of student work and providing feedback on the kind of open-ended assignments and projects that can build communication skills can be time consuming for an instructor. How can we evaluate communication products fairly and give feedback more efficiently? How can we learn from other writing pedagogies to inform our efforts? This workshop aims to cross-pollinate ideas from teaching writing across multiple disciplines.

Organizers

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Y J
Yue Jiang Duke University, Statistical Science
S S
Sara Stoudt Bucknell University, Mathematics

Speakers

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J A
Jonathan Auerbach George Mason University, Statistics
D B
David Brown Carnegie Mellon University, English
M Ç
Mine Çetinkaya-Rundel Duke University, Statistical Science
A D
Amber Dood University of Michigan, Sweetland Center for Writing
S E
Shannon Ellis University of California, San Diego, Cognitive Science
S F
Solaire Finkenstaedt-Quinn University of Michigan, Sweetland Center for Writing
J F
Jennifer Fletcher California State University, Monterey Bay, English
S I
Suguru Ishizaki Carnegie Mellon University, English
Y J
Yue Jiang Duke University, Statistical Science
M L
Michael Laudenbach Carnegie Mellon University, English
A M
Amelia McNamara University of St. Thomas, Computer and Information Sciences
R N
Ramona Naddaff University of Californiaa, Berkeley, Rhetoric
S R
Suraj Rampure University of California, San Diego, Halıcıoğlu Data Science Institute
K S
Kelly Spoon San Diego Mesa College, Mathematics
M T
Maria Tackett Duke University, Statistical Science
A T
Allison Theobold California Polytechnic State University, San Luis Obispo, Statistics

Registration

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