1. Conceptual modeling refers to transforming abstract mathematical concepts into practical models that are easy to understand through visualization. For example, when teaching students to know fractions, students can intuitively understand the concept of fractions by cutting cakes. Process modeling refers to transforming mathematical problems into concrete steps and operations, so that students can understand the problem-solving process through practice.

2. When teaching students to understand the area of a rectangle, guide students to measure the length and width of the rectangle, and then calculate the area with a formula. Symbolic modeling refers to expressing mathematical problems or concepts with mathematical symbols and establishing corresponding mathematical equations or expressions. When teaching students to understand the area of a triangle, guide them to represent the base and height of the triangle with symbols, and then calculate the area with formulas.

3. Equation modeling refers to transforming practical problems into mathematical equations and solving them. For example, when teaching students to understand the relationship between speed, time and distance, they can be guided to establish the equation between speed, time and distance, and solve the equation to get the answer.

Related knowledge of modeling

1. model construction: model construction refers to the process of using certain tools and materials to create models. In mathematical modeling, modeling usually refers to the use of mathematical tools to establish mathematical models. In other fields, such as physics and chemistry, different tools and materials may be needed to build models.

2. Model verification: Model verification refers to the process of checking and verifying the established model. Model verification usually includes evaluating the accuracy, reliability and stability of the model. In mathematical modeling, model verification usually refers to applying the model to actual data or problems, and checking and verifying it.

3. Model optimization: Model optimization refers to the process of improving and optimizing the established model. Model optimization usually includes optimizing the parameters, structure and algorithm of the model to improve the performance and effect of the model. In mathematical modeling, model optimization usually refers to adjusting the parameters and structure of the model to improve the accuracy and reliability of the model.