ReadME – Generating Personalized Feedback for Essay Writing Using the ReaderBench Framework

Writing quality is an important component in defining students’ capabilities. However, providing comprehensive feedback to students about their writing is a cumbersome and time-consuming task that can dramatically impact the learning outcomes and learners’ performance. The aim of this paper is to introduce a fully automated method of generating essay feedback in order to help improve learners’ writing proficiency. Using the TASA (Touchstone Applied Science Associates, Inc.) corpus and the textual complexity indices reported by the ReaderBench framework, more than 740 indices were reduced to five components using a Principal Component Analysis (PCA). These components may represent some of the basic linguistic constructs of writing. Feedback on student writing for these five components is generated using an extensible rule engine system, easily modifiable through a configuration file, which analyzes the input text and detects potential feedback at various levels of granularity: sentence, paragraph or document levels. Our prototype consists of a user-friendly web interface to easily visualize feedback based on a combination of text color highlighting and suggestions of improvement.