Word Embedding for Response-To-Text Assessment of Evidence
This work addresses the problem of efficient grading for educators and students, but it is incremental as it builds on existing word embedding techniques for a specific domain.
The paper tackles the labor-intensive manual grading of Response-to-Text Assessments (RTA) by developing an automatic scoring method using word embeddings to improve evidence rubric scoring, evaluated on corpora from upper elementary students.
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.