This project is an idea of building an application(Web/Mobile) that classify the type of sentence/s based on the semantic nature of sentence and also evaluate the writing skills of a document. Technically speaking: Accessing the Writing skills of a document/author by classifiying the statements and sentences into different classes based on the sequential learning using Pre-trained models from BERT. Rating the document based on the score obtained from the classes for each statement/sentence.
Writing is a critical skill for success. However, less than a third of high school seniors are proficient writers, according to the National Assessment of Educational Progress. Unfortunately, low-income, Black, and Hispanic students fare even worse, with less than 15 percent demonstrating writing proficiency. One way to help students improve their writing is via automated feedback tools, which evaluate student writing and provide personalized feedback.
There are currently numerous automated writing feedback tools, but they all have limitations. Many often fail to identify writing structures, such as thesis statements and support for claims, in essays or do not do so thoroughly. Additionally, the majority of the available tools are proprietary, with algorithms and feature claims that cannot be independently backed up. More importantly, many of these writing tools are inaccessible to educators because of their cost. This problem is compounded for under-serviced schools which serve a disproportionate number of students of color and from low-income backgrounds. In short, the field of automated writing feedback is ripe for innovation that could help democratize education.
