1, based on big data analysis: Grade 6 estimator builds up a huge data set by analyzing the data of Grade 6 exams in previous years, including information on exam difficulty, score distribution, etc. These data can provide information on the distribution of scores for different types of questions and different difficulty levels, as well as the scores of candidates on these questions. These data can provide the distribution of scores for topics of different types and difficulty levels, as well as the performance of candidates on these topics. By analyzing these data, the distribution of candidates in different score ranges can be obtained, providing a basis for subsequent score estimation.

2. Machine Learning Algorithms: Grade 6 Score Estimator uses machine learning algorithms, such as regression analysis, decision trees, and neural networks, to train and model previous years' data. By taking the candidates' scores in the practice tests as inputs and combining them with similar situations in the data of previous years for comparison and matching, the model can learn the features and patterns of different scoring situations. The model can predict the range of scores a candidate will obtain in the actual Grade 6 exam based on his/her scores in the practice test.