# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
###### Abstract  
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. 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 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.  
open question test; automatic test assessment; natural language processing; split algorithm; similarity measures; machine learning; artificial neural network; hybrid text-processing method 2020 IEEEexample: accepted version 2.5.10.  
In our e-test application, one short-answer question object consists of the question phrase, one to four phrases proposed by the teacher as correct reference answers, and the answer given by the student. In our previous work, we presented a couple of text algorithms for short-answer scoring. The first algorithm initially tries to match the student's answer exactly to one of the reference answers and then uses the approximate match method [3]. In its revised version, scoring is proportional to the degree of fit [12]. Another alternative scoring method is based on Artificial Neural Networks (ANN) [13].  
The new hybrid scoring method presented in this article combines the text similarity algorithms with ANN-based evaluation and thus solves most of the shortcomings of the previously developed algorithms.  
The proposed algorithm combines the following previously used procedures:  
* Preprocessing (simplification, lemmatization);
* Using the Jaccard index to measure similarity;
* ANN of Multi-Layer Perceptron (MLP) type as a classifier.  
In the newly developed algorithm, the student's answer is pre-processed and then evaluated in parallel by the text similarity algorithm and revised ANN-based algorithm. The final grading is determined in the last step based on these two scoring results and a set of heuristic decision rules.  
The rest of this paper is structured as follows: Section 2 is state-of-art; Section 3 provides an overview of the most recent studies by the authors related to text similarity methods; Section 4 describes the developed artificial neural network-based methods; Section 5 shows the new hybrid ANN model joining the pre-processed split method and developed ANN-based methods and results of the newly developed method compared with the authors' previous experiments; Section 6 contains the discussion; and Section 7 contains the conclusions.