Revising in response to formative feedback is a cornerstone of effective writing instruction. Providing substantive comments on stacks of student essays, however, is highly time-consuming for busy teachers. As a result, teachers can be reluctant to assign many essays that require students to produce multiple drafts or provide extensive comments on early drafts of students’ writing.

Automated writing evaluation (AWE) systems offer a promising approach to relieving the burden of giving formative feedback to students on their writing. These systems employ natural language processing technologies to provide automated feedback messages to students to guide their revisions.

Despite the potential of AWE systems to support writing instruction, they have not been widely adopted by schools or teachers. Computer scientists and literacy researchers have been the main architects of AWE systems. Teachers, however, are the experts on managing the daily tasks of instruction, and their voices have largely been left out of the development of AWE systems. What do teachers want to see in AWE systems so that such technology can be integrated in their teaching, rather than ‘one more thing’ added to their classroom schedules?

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We asked teachers this question in our research developing an AWE system called eRevise. eRevise provides automated feedback to improve fifth and sixth grade students’ use of text evidence in their argument writing essays.

About the Author:

Lindsay Clare Matsumura is a Professor in the University of Pittsburgh’s School of Education and Senior Scientist at the Learning Research and Development Center (LRDC). She designs and studies technology-based interventions to increase the quality of literacy instruction and learning.

Elaine Lin Wang is a Policy Researcher at the RAND Corporation. Her work focuses on the factors that facilitate or pose challenges for implementation of education policies, programs, and technologies in classrooms.

Richard Correnti is an Associate Professor in the University of Pittsburgh’s School of Education and Research Scientist at the LRDC. He studies how policy and educational reform
initiatives can improve instruction and student learning ‘at scale.’

Diane Litman is a Professor of Computer Science at the University of Pittsburgh and a Senior Scientist at the LRDC. Her research is in areas including artificial intelligence, computational linguistics, knowledge representation and reasoning. Her most recent research has been in speech and natural language technology for educational applications.

This work was supported by the Institute of Education Sciences, Award #R305A160245.


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