A Model for Automated Business Writing Assessment - Daniil Dmitrievich Zafievsky; Nadezhda Stanislavona Lagutina; Oksana Andreyevna Melnikova; Anatoliy Yurievich Poletaev (Моделирование и анализ информационных систем, 2022)
This study is aimed at building an automated model for business writing assessment, based on 14 rubrics that integrate EFL teacher assessment frameworks and identify expected performance against various criteria (including language, task fulfillment, content knowledge, register, format, and cohesion). We developed algorithms for determining the corresponding numerical features using methods and tools for automatic text analysis. The algorithms are based on a syntactic analysis with the use of dictionaries. The model performance was subsequently evaluated on a corpus of 20 teacher-assessed business letters. Heat maps and UMAP results represent comparison between teachers’ and automated score reports. Results showed no significant discrepancies between teachers’ and automated score reports, yet detected bias in teachers’ reports. Findings suggest that the developed model has proved to be an efficient tool for natural language processing with high interpretability of the results, the roadmap for further improvement and a valid and unbiased alternative to teachers’ assessment. The results may lay the groundwork for developing an automatic students’ language profile. Although the model was specifically designed for business letter assessment, it can be easily adapted for assessing other writing tasks, e.g. by replacing dictionaries.