# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 6 Discussion  
The analysis of the results shown in Figure 5 clearly shows that the proposed hybrid approach achieves the best results compared to the stand-alone methods. The hybrid method achieves a precision of a perfect score of 1, which is the same as the new Split method (1), and both outperform the new ANN method (0.77). In the case of recall, the new ANN method achieves a better result (0.86) than the new Split method (0.74), and the hybrid method significantly improves it to 0.97. The F1 score values for the new Split and new ANN methods are comparable (0.85 and 0.81, respectively), while the highest F1 score value of 0.98 belongs to the hybrid method. It is clear that the hybrid approach seamlessly combines the strengths of both stand-alone methods. The analysis of the results for all the methods considered in this article, presented in Figure 6, clearly shows that the hybrid method outperforms the previous approaches in all the quality measures. It should also be noted that the new Split and new ANN methods introduced significant improvements compared to their previous versions, which significantly improved all quality measures for these revised methods compared to their predecessors. The precision for the old Split method was very good (0.99), but the new Split method reached the optimal perfect rate (1). Also, the new ANN method has a precision measure rate of 0.77, and it is much better than the old ANN method, which has a precision rate of 0.65. Furthermore, both the new Split and new ANN-based methods have a recall measure rate of 0.74 and 0.86, respectively, which is also better than the old Split and ANN recall measure rates of 0.58 and 0.43. The F1 measure was very good for both the new Split and ANN methods (0.85 and 0.81) compared with the old Split and ANN methods, which have F1 measure rates of 0.73 and 0.52, respectively. If we compare the quality measures for our hybrid method with the results for other methods published in the available literature, these results are excellent, i.e., precision of 1.00, recall of 0.97, and F1 score of 0.98. In contrast, the Deep Belief Network (DBN) approach in [18], which incorporated student modelling and question features, achieved a precision of 0.83, a recall exceeding 0.83, and a maximum F1 score of 0.834. The Survey on Automated Short-Answer Grading with DeepLearning [35] reports F1 score values  
Figure 6: Measures (precision, recall and F1) comparison. from 0.72 to 0.97. The mean F1 score values of automated essay scoring models, such as feature-based models and transformer-based models described in [33], vary from 0.783 to 0.864, while F1 score values for automatic scoring using large language models and chain-of-thought in [39] span from 0.2375 to 0.9083. However, a comparison of methods based only on literature reports is not reliable because these methods have been used to solve problems of different difficulty and complexity classes and have been tested on different datasets. Our model has not been tested on large datasets like the other models described in the literature. It does not use semantic analysis and is not capable of making sense of students' answers. It is based on text similarity algorithms and simple feed-forward artificial neural networks. The advantage of our hybrid model is its low computational complexity, unlike the methods based on deep learning and large language models. The total time complexity of the hybrid method is a sum of the time complexities of both its components, the new Split algorithm and the new ANN algorithm, where the time complexity of the new ANN one is the dominant component. It should be noted, however, that artificial neural networks take time to train, but once trained, the network can be used many times, solving the problems for which it was designed very quickly. To reduce the computational complexity of the ANN method and to lower the requirements for the number of input data patterns, a set of small ANNs instead of one complex ANN has been used.