## 3 Related Work  
Though the automated scoring systems that are already in place have worked well in the past, they all have the same major flaw. None of these methods made an effort to weight students' responses based on their importance. These methods instead combined NLP-based algorithms, statistics, probabilities, and other data sources such as corpora to arrive at a more accurate approximation of a student's score. Semantic similarity research was widely implemented in essay grading [27; 28; 29]. However, they require a massive annotation effort in terms of cost, time, and expertise. In addition, semantic parsing is not fully automated [30]. In [31], the K-Nearest Neighbors (KNN) was used as a categorization method for grading essays. This algorithm uses a dictionary that understands both Thai and English to break down text into individual responses to numerous keywords and phrases. Next, they combined the matching and word similarity methods with the KNN algorithm. KNN performs well when there are few input variables, but it becomes increasingly difficult for the algorithm to anticipate the outcome of a new data point as the number of input variables increases. When the idea of ontology became widely available, it was put to use in grading essays [32; 33; 34]. In [35], the authors evaluate essays using a system that combines semantic matching, ontology creation, information extraction, and the Unified Modeling Language. The method takes advantage of WordNet to calculate the depth of the Least Common Sub-Summer, a metric that gauges the semantic relatedness between material taken from the ungraded essay and some other graded essays. The use of a large collection of already-scored essays helps keep the system's initial training costs low. In addition, the system's ontology-based structure and the use of layers (a presentation layer, a business layer, and a data layer) facilitate the provision of insightful input to students. The primary issue with the model is that it cannot assess essays whose subjects deviate from those picked, as the taxonomy only includes knowledge about those topics. Deep learning, defined as multi-layer neural networks that autonomously acquire valuable characteristics from data, was brought to the area of essay grading by the authors in [36]. In this instance, the lower layers learn the receptors for more concrete characteristics, while the upper layers learn more abstract features at a higher level. Using neural network design, the system discovers a dispersed illustration for each word in a collection according to its local context. The automated feature extraction in this system makes it more cost-effective to teach the evaluation system on a large collection of already-graded essays. However, it struggles to isolate the textual features that the network has determined to be most informative. In addition, it does not engage with semantics but rather tests for correct grammar and syntax. Latent semantic analysis (LSA) has been widely adopted in the area of essay grading in recent years. It was used sometimes with integration with fuzzy logic and sometimes with clustering techniques. When it is integrated with clustering, it achieves some success as it does not need pre-specified classes or categories, so it does not need pre-training. The fuzzy function assigns LSA a fair amount of weight. Even so, it does have some disadvantages. As it stands, LSA is unable to determine whether repeated words are useful. LSA only cares about precise word similarities and ignores how those words are used in context [37]. Another work [38] integrated LSA with the K-nearest neighbor algorithm in essay scoring. The only advantage of this approach was that the correlation degree checked similarity between paragraphs and within paragraphs, so it checked cohesion and coherence. Yet, distance between neighbors is dominated by irrelevant features, and the weights of some words, such as modal verbs, are small because they are distributed evenly among the classes, which can affect the result.