# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 2 Related Work
A hybrid model has been developed by joining the lexicon-based approach with machine learning [44]. Moreover, another hybrid model has been proposed and focused on the issue of sharing knowledge in the Question Answer Community (QAC) based on the content and non-content models, predicting the best answer to the user and reducing the time consumed during the asking process [45]. Hybrid models in various applications significantly improved results compared with traditional approaches [44; 45; 46; 47]. Summarising the current state of knowledge, the following conclusions can be drawn: AI-based methods provide higher accuracy in evaluating open-ended responses than classical classifiers [13; 18; 24], and Deep Neural Networks (DNN) outperform traditional ANNs in the case of complex classification tasks [18; 25; 33; 48; 49]. However, the data-based models, such as ANN and DNN, show difficulty in effectively catching the semantics of short answers for consistent comparison with reference answers. To compensate for this, an ensemble of classifiers, stacked models, and, especially, hybrid models that combine feature engineering with deep representation learning has been developed [35]. The most advanced transformer-based systems use large language models for natural language processing tasks that require understanding context in order to evaluate answers to questions [28; 33; 35; 39; 41]. It should be emphasised that, although these complex systems offer high computational efficiency, scalability, and universality, which enables their use in various complex tasks, their disadvantages are the requirement for a large number of training data and high memory requirements. The attention mechanism has a complexity of O(T\({}^{2}\)), which makes it difficult to process very long sequences. The choice of an appropriate method for evaluating open-ended responses should, therefore, be a reasonable compromise, using tools appropriate to the level of complexity of the problem. This work presents a smart hybrid method for short-answer grading. A key innovation of our approach is the integration of text similarity algorithms, MLP classifiers, and heuristic decision rules. Unlike methods that rely solely on neural networks or predefined rules, our hybrid approach seamlessly combines the strengths of both. The innovative split text similarity algorithms ensure accurate alignment with reference answers through exact and approximate matching and word embedding, capturing nuanced patterns and relationships in short-answer grading. The parallel leveraging of MLP classifiers allows for more robust performance by the feature abstraction capabilities of ANN, while heuristic decision rules provide clear and explainable evaluation criteria for information fusion from both methods. Together, these components provide a balanced framework that achieves very good results in short-answer evaluation tasks. Although many different hybrid models have been used for automated short-answer grading, the authors did not find a hybrid model similar to the one presented in this paper in the literature available to them.