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(Ed.)
Open-ended questions in mathematics are commonly used
by teachers to monitor and assess students’ deeper conceptual understanding of content. Student answers to these
types of questions often exhibit a combination of language,
drawn diagrams and tables, and mathematical formulas and
expressions that supply teachers with insight into the processes and strategies adopted by students in formulating
their responses. While these student responses help to inform teachers on their students’ progress and understanding, the amount of variation in these responses can make it
difficult and time-consuming for teachers to manually read,
assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task.
This work seeks to build upon this prior research by introducing a model that is designed to help automate the
assessment of student responses to open-ended questions
in mathematics through sentence-level semantic representations. We find that this model outperforms previouslypublished benchmarks across three different metrics. With
this model, we conduct an error analysis to examine characteristics of student responses that may be considered to
further improve the method.
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