Word Embedding for Response-To-Text Assessment of Evidence

TL;DR

This paper introduces a word embedding-based method to automatically score evidence in student responses for the Response to Text Assessment, aiming to reduce manual grading effort and provide formative feedback.

Contribution

It develops interpretable features using word embeddings to improve evidence scoring in RTA, a novel approach for educational assessment automation.

Findings

Effective evidence scoring on student responses

Improved scoring accuracy with word embeddings

Potential for formative feedback applications

Abstract

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.