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Session Type: Symposium
Automated feedback–feedback provided by computers regarding students’ writing—is shown to be effective for helping students develop their writing skills and supporting teachers in planning and delivering writing instruction. Yet, there are many issues surrounding the use of automated feedback, such as the quality of automated feedback, whether students trust and utilize automated feedback, and whether automated feedback reflects and perpetuates biases, among other issues. This session will present five papers that address these issues, highlighting the cutting edge of automated feedback on writing and how best to navigate maximize learning outcomes using automated feedback while minimizing any attendant risks inherent in this increasingly ubiquitous technology.
Automating Bias in Writing Evaluation: Sources, Barriers, and Recommendations - Maria Goldshtein, Arizona State University - Polytechnic; Rod D. Roscoe, Arizona State University - Polytechnic; Amin Alhashim
Advances in Automating Feedback for Argumentative Writing: Feedback Prize as a Case Study - Perpetual Baffour, The Learning Agency LAB; Scott Crossley, Vanderbilt University
Using ChatGPT for Large-Scale Writing Project Scoring - Kausalai K. Wijekumar, Texas A&M University; Shuai Zhang, University of Saint Joseph; Debra McKeown, Texas A&M University
Automated Writing Trait Analysis - Paul D. Deane, Educational Testing Service
Supporting Students’ Argumentative Writing via Formative Automated Writing and Revision Assessment - Richard J. Correnti, University of Pittsburgh; Lindsay Clare Matsumura, University of Pittsburgh; Diane Litman, University of Pittsburgh; Zhexiong Liu, University of Pittsburgh; Tianwen Li, University of Pittsburgh; Elaine Lin Wang, RAND Corporation