Automated, content-focused feedback for a writing-to-learn assignment in an undergraduate organic chemistry course

Abstract

Writing-to-learn (WTL) pedagogy supports the implementation of writing assignments in STEM courses to engage students in conceptual learning. Recent studies in the undergraduate STEM context demonstrate the value of implementing WTL, with findings that WTL can support meaningful learning and elicit students’ reasoning. However, the need for instructors to provide feedback on students’ writing poses a significant barrier to implementing WTL; this barrier is especially notable in the context of introductory organic chemistry courses at large universities, which often have large enrollments. This work describes one approach to overcome this barrier by presenting the development of an automated feedback tool for providing students with formative feedback on their responses to an organic chemistry WTL assignment. This approach leverages machine learning models to identify features of students’ mechanistic reasoning in response to WTL assignments in a second-semester, introductory organic chemistry laboratory course. The automated feedback tool development was guided by a framework for designing automated feedback, theories of self-regulated learning, and the components of effective WTL pedagogy. Herein, we describe the design of the automated feedback tool and report our initial evaluation of the tool through pilot interviews with organic chemistry students.