Hybrid ANN-Based and Text Similarity Method for Automatic Short Answer Grading in Polish

Computer-assisted grading plays an important role in an educational context, mainly by reducing the workload of teachers in manual scoring. While electronic choice surveys have long been used in many web applications, automatic scoring of open-ended responses remains an interesting research problem in natural language processing. In this article, we propose a new hybrid text-processing method for scoring students' responses based on word splitting and preprocessing, which will then combine textual algorithms with a set of artificial neural network classifiers and a set of heuristic decision rules. This concept has been implemented in the interactive e-test system operating in the local computer network of the Institute of Applied Computer Science at the Lodz University of Technology (TUL). The dataset is acquired as questions, reference answers and students' answers generated on the basis of exams conducted at our institute in the years 2015-2022 for more than a thousand students. This article extends our previous research [1, 2, 3] and includes comparative tests. The proposed method achieves excellent results and outperforms the previous approaches. The obtained precision is equal to 1, and the recall measure is 0.97 for the final results. The algorithm is designed for the Polish language but can be adapted to other inflected languages.