Summary Street: Interactive Computer Support for Writing

TLDR

Summary Street is an educational software that uses latent semantic analysis to represent text content. The study demonstrates how LSA can support education by giving automatic feedback on students’ summaries. Summary Street delivers feedback through a graphic display that lets students revise multiple times independently before teacher evaluation, and the study discusses classroom implementation and additional instructional uses. Classroom trials with 6th graders showed that Summary Street improved summary quality, increased engagement time, produced more balanced coverage, and yielded greater content score gains especially on difficult texts, all without adding teacher workload.

Abstract

Summary Street is educational software based on latent semantic analysis (LSA), a computer method for representing the content of texts. The classroom trial described here demonstrates the power of LSA to support an educational goal by providing automatic feedback on the content of students' summaries. Summary Street provides this feedback in an easy-to-grasp, graphic display that helps students to improve their writing across multiple cycles of writing and revision on their own before receiving a teacher's final evaluation. The software thus has the potential to provide students with extensive writing practice without increasing the teacher's workload. In classroom trials 6th-grade students not only wrote better summaries when receiving content-based feedback from Summary Street, but also spent more than twice as long engaged in the writing task. Specifically, their summaries were characterized by a more balanced coverage of the content than summaries composed without this feedback. Greater improvement in content scores was observed with texts that were difficult to summarize. Classroom implementation of Summary Street is discussed, including suggestions for instructional activities beyond summary writing.