《气象统计方法》课程项目驱动式小班教学实践总结

Summary of Project-Based Small-Class Teaching Practice of “Meteorological Statistical Methods” Course

摘要: 本文总结了对大气科学学院2023级大气科学专业学生《气象统计方法》课程开展项目驱动式小班教学实践的初衷、具体实施措施、教学反馈及改进思考，并讨论了使用人工智能对课程教与学两方面的影响。此次小班教学引入了分组实践模块，在行课过程中学生需依次完成研究题目选定、文献调研、气象数据预处理、统计方法选取与建模、气象编程与可视化、结果分析、总结汇报等各项任务。通过此次项目式学习，学生在数据处理、统计建模、气象分析和团队协作等诸多方面均得到了锻炼，特别是令学生熟悉了基于气象统计方法进行科学研究的完整流程，与此同时，教师也在课程的内容更新和未来教学改进方向上获得了不少启发。此次教学实践体现出了小班教学的优势，总体上是一次收获颇多且值得推广的教改尝试。

Abstract: This paper presents a comprehensive evaluation of the project-based, small-class teaching initiative implemented in the “Meteorological Statistical Methods” course for the Grade 2023 Atmospheric Science majors at the School of Atmospheric Sciences. It outlines the original intention, instructional design, teaching feedback, and insights for future improvement. We also examined the transformative role of artificial intelligence in reshaping both teaching methodologies and student learning experiences within this course. A key innovation was the integration of the group-based research practice into the course: students sequentially undertook scientific tasks—including topic selection, literature review, meteorological data preprocessing, statistical method selection and model construction, meteorological programming and visualization, interpretation of results, and synthesis into reports. This project-based learning framework cultivated core competencies in data analysis, statistical modeling, scientific interpretation, and team collaborative. In particular, it enabled students to become familiar with the entire process of conducting scientific research based on meteorological statistics. Concurrently, instructors derived valuable insights for curriculum renewal and pedagogical refinement. Overall, this initiative exemplifies the distinctive advantages of small-class, project-based instruction and presents a rigorous, replicable, and pedagogically impactful reform effort.