Automated writing evaluation, Intrapersonal factors, Machine learning, Motivation, Natural language processing, Utility value
The integration of subject matter learning with reading and writing skills takes place in multiple ways. Students learn to read, interpret, and write texts in the discipline-relevant genres. However, writing can be used not only for the purposes of practice in professional communication, but also as an opportunity to reflect on the learned material. In this paper, we address a writing intervention – Utility Value (UV) intervention – that has been shown to be effective for promoting interest and retention in STEM subjects in laboratory studies and field experiments. We conduct a detailed investigation into the potential of natural language processing technology to support evaluation of such writing at scale: We devise a set of features that characterize UV writing across different genres, present common themes, and evaluate UV scoring models using essays on known and new biology topics. The automated UV scoring results are, we believe, promising, especially for the personal essay genre.