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针对高校计算机类课程传统评价方法存在耗时、主观性强、反馈延迟、缺乏个性化等问题，分析人工智能技术在过程性评价中的应用潜力和途径，提出一种基于AI技术的过程性评价模型。在数字图像处理课程中运用计算机视觉、自然语言处理等人工智能技术，实时分析学生的在线活动、作业质量及互动数据，为每位学生提供即时反馈和个性化学习建议。实践表明，该评价模型促进了学生的主动参与和深度理解，显著提升了图像处理技能的掌握水平。
Abstract：In response to the problems of traditional evaluation methods for computer-related courses in colleges and universities, such as time-consuming, strong subjectivity, delayed feedback, and lack of personalization, this paper analyzes the application potential and approaches of artificial intelligence technology in process evaluation and proposes a process evaluation model based on AI. By applying computer vision and natural language processing and other artificial intelligence technologies in the Digital Image Processing course, it can analyze students' online activities, assignment quality, and interaction data in real time, providing each student with immediate feedback and personalized learning suggestions. Practice has shown that this evaluation model has promoted students' active participation and in-depth understanding, significantly improving their mastery of image processing skills.
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基本信息:
DOI：10.16512/j.cnki.jsjjy.2025.10.046
中图分类号:TP3-4;G642.0
引用信息:
[1]唐满,郭东恩,詹英.“AI+”背景下计算机类课程过程性评价改革[J].计算机教育,2025,No.370(10):133-138.DOI:10.16512/j.cnki.jsjjy.2025.10.046.
基金信息:
南阳理工学院教学改革项目“‘数字图像处理’过程性评价研究与实践”（NIT2023JY-164）;南阳理工学院重点教改项目“新工科背景下高水平软件技术专业集群建设”（NIT2020JY-003）;南阳理工学院教学改革项目“基于类ChatGPT大语言模型的差异化智慧学习与个性化指导模式研究与实践”（NIT2023JY-104）;南阳理工学院教学改革项目“‘互联网+’背景下二级学院教育教学管理模式创新与实践”（NIT2020JY-037）; 教育部产学合作协同育人项目“面向人工智能应用的‘数字图像处理’课程教学改革与实践”（230704144121940）
2025-10-10
2025-10-10
