The Interference of Pre-Processing Software for the Numerical Simulation of Groundwater on the Cognition of Environmental Students: Model Mesh Construction as an Example

(1) Background: Software for the numerical simulation of groundwater plays an important role in studying environmental problems. However, it is still unclear whether the pre-processing software of the numerical simulation of groundwater has a negative effect on the cognition of undergraduates in the environmental field who only have basic groundwater flow and solute transport knowledge and software operation skills. (2) Methods: To explore this issue, we used software meshing as an example and selected undergraduates in the environmental field to conduct the questionnaire surveys. A total of 345 undergraduate answer sheets were received, and data analysis was carried out. The students were divided into two groups, one with and another without certain basic groundwater flow and solute transport knowledge or software operation skills. (3) Results: For undergraduate students with some basic knowledge or software operation ability, the proportion of students whose cognition was adversely interfered with by the pre-processing software was 64.3%, and the ratio of students not interfered with was 35.7%. For undergraduates without groundwater flow and solute transport knowledge and relevant software operation skills, the ratios were 63.2% and 36.8%, respectively. (4) Conclusions: Pre-processing software numerical simulation of groundwater could negatively interfere with students’ cognition. The basic groundwater flow and solute transport knowledge and software operation skills did not observably reduce the interference degree (p = 0.259) but had significant influences on the undergraduates’ thinking modes on the numerical simulation problems (p = 0.009). The interference was mainly caused by the significant difference between the level of knowledge possessed by the students and that represented by the pre-processing software. This paper provides basic scientific data for the optimization of students’ knowledge structures and the improvement of teaching methods.