EFL methodology has always recognized the importance of giving student learners of foreign languages regular and quick feedback on student production, both written and oral. The presented paper describes the decisions taken during the development of an application to measure text complexity, and shows how the results achieved with this application were translated into feedback related to the author’s language proficiency. Along with some standard text complexity features, this tool takes into account those that are significant for Russian learners of English. The application provides students with the statistics of the relevant linguistic features of the text in comparison with texts of the learner essays that were considered the top and the bottom levels in the learner corpus. The paper also points out what text features are especially relevant for the assessment of the essays written in English by Russian students. The choice was made possible after the analysis of 3440 texts from Russian Error-Annotated English Learner Corpus, and after applying methods of machine learning and statistical analysis to predict the grade that could be received for the essay.

EFL方法学一直认识到为学生提供定期和快速反馈的书面和口头成绩对外语学习者的重要性。提出的论文描述了在开发应用程序时用来衡量文本复杂度的决策，并展示了如何将使用此应用程序获得的结果转换为与作者的语言水平相关的反馈。除了一些标准的文本复杂性功能外，该工具还考虑了对俄语英语学习者至关重要的功能。该应用程序为学生提供了与该语言相关语言特征的统计信息，以及与被视为学习者语料库中最高层和最低层的学习论文的文本相比较的统计信息。本文还指出了哪些文本特征与俄罗斯学生用英语撰写的论文评估特别相关。在分析了来自俄罗斯带错误注释的英语学习者语料库的3440篇课文之后，并应用了机器学习和统计分析方法来预测论文可以收到的成绩之后，才做出了选择。