In this paper, we introduce shinyReCoR: a new app that utilizes a cluster-based method for automatically coding open-ended text responses. Reliable coding of text responses from educational or psychological assessments requires substantial organizational and human effort. The coding of natural language in responses to tests depends on the texts’ complexity, corresponding coding guides, and the guides’ quality. Manual coding is thus not only expensive but also error-prone. With shinyReCoR, we provide a more efficient alternative. The use of natural language processing makes texts utilizable for statistical methods. shinyReCoR is a Shiny app deployed as an R-package that allows users with varying technical affinity to create automatic response classifiers through a graphical user interface based on annotated data. The present paper describes the underlying methodology, including machine learning, as well as peculiarities of the processing of language in the assessment context. The app guides users through the workflow with steps like text corpus compilation, semantic space building, preprocessing of the text data, and clustering. Users can adjust each step according to their needs. Finally, users are provided with an automatic response classifier, which can be evaluated and tested within the process.

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