基于过程性数据的大学物理实验课程考核改革

Reform of University Physics Experiment Course Assessment Based on Process Data

摘要: 针对传统大学物理实验报告评阅方式存在的主观性强、反馈滞后、忽视过程性学习表现等突出问题，本研究以经典的杨氏弹性模量测量实验为切入点，设计了一种基于多种过程性数据的实验考核智能评阅与反馈系统，致力于利用人工智能技术实现对学生实验能力的全面、客观与形成性评价。该系统不仅能自动完成数据准确性、图表规范性等基础项目的批阅，更能深度分析学生对物理概念的理解、实验操作、数据处理等多维度的能力评价，为学生生成个性化反馈报告，同时提供具体、可操作的改进建议，有效促进了学生的反思与更深度的学习。

Abstract: To address the prominent issues in the traditional evaluation method of college physics laboratory reports—such as strong subjectivity, delayed feedback, and neglect of process-oriented learning performance—this study takes the classic experiment of measuring Young’s modulus as a starting point and designs an intelligent report evaluation and feedback system based on multiple types of process data. The system is committed to leveraging artificial intelligence technology to achieve comprehensive, objective, and formative assessment of students’ experimental competencies. It not only automatically completes the grading of fundamental items such as data accuracy and chart standardization, but also performs in-depth analysis of students’ multidimensional abilities, including their understanding of physical concepts, experimental operations, and data processing. The system generates personalized feedback reports for each student, provides specific and actionable suggestions for improvement, and effectively promotes students’ reflection and deeper learning.