基于MATLAB的物理实验数据处理教学改革与实践

Teaching Reform and Practice of Physical Experiment Data Processing Based on MATLAB

摘要: 在人工智能和教育数字化背景下，高等教育对理工科学生的数智素养、数据处理能力与编程实践能力提出了更高要求。传统大学物理实验课程中，数据处理常依赖手工绘图与经验判断，效率低、精度差，难以满足新时代教学目标。文章以“静电场模拟实验”为案例，设计并实施了一种融合最小二乘法与MATLAB编程的实验数据处理教学新模式。通过引导学生利用MATLAB平台实现圆形等势线拟合、误差分析与可视化呈现，不仅有效提升了实验数据处理的科学性与准确性，也增强了学生在数学建模、程序实现和实验分析方面的综合能力。实验结果显示，最小二乘法在拟合精度方面显著优于传统均值法，拟合结果更接近理论值。该研究初步构建了“理论建模–数据采集–编程分析–图形展示”一体化的教学流程，为物理实验课程的数字化转型与工程能力培养提供了可行路径和实践参考。

Abstract: With the rapid development of artificial intelligence and educational digitalization, higher education places increasing emphasis on students’ digital literacy, data analysis, and programming skills. In traditional university physics laboratory courses, data processing often relies on manual plotting and empirical estimations, which are inefficient and inaccurate, failing to meet the goals of modern STEM education. This study takes the electrostatic field simulation experiment as a case and introduces an instructional model that integrates the least squares method with MATLAB programming. Students are guided to perform curve fitting, error analysis, and visualization of equipotential circles using MATLAB, enabling them to bridge mathematical modeling with practical data processing. Experimental results show that the least squares method significantly outperforms the traditional mean-value method in fitting accuracy and alignment with theoretical values. This study constructs a comprehensive teaching workflow, spanning theoretical modeling, data acquisition, numerical fitting, and graphical presentation, providing an effective pathway for reforming physics laboratory instruction and cultivating students’ engineering competence and digital intelligence in the context of emerging technologies.