基于大数据的建筑模型与工艺实验室管理与教学评价研究

Research on the Management and Teaching Evaluation of Architectural Modeling and Craftsmanship Laboratory Based on Big Data

摘要: 随着高校教育数字化改革的不断推进，实验教学的管理模式和评价体系正经历着深刻结构性重构。建筑模型与工艺课程作为环境设计专业的重要实践教学模块，对实验室多样化材料、精细化工具、复杂化流程的依赖尤为显著。然而传统实验室管理方式普遍存在信息割裂、数据缺乏、资源利用率不均、过程评价缺失等问题，已难以满足高质量实践教学的需求。基于此，本研究依托南通理工学院传媒与设计学院建筑模型与工艺实验室，以大数据技术为核心，从管理数字化、资源精细化配置、教学评价精准化等维度展开系统研究。文章通过文献调研、数据采集、跨院校走访、实验室应用试验等方法，构建了适用于多项目、多耗材、多设备、多作品特点的建筑模型实验室管理体系与教学评价体系，并在实验室实际运行中进行应用测试与反馈验证。研究结果表明，大数据不仅能显著提升实验室资源利用效率，还能全面捕捉学生与教师在模型工艺课程中的行为特征，为教学评价的客观化、及时化与多元化提供科学依据。本研究为设计类专业实验室数字化管理提供可参考的模式，对实验教学质量提升具有一定现实价值。

Abstract: With the continuous advancement of digital reform in higher education, the management model and evaluation system of experimental teaching are undergoing a profound structural transformation. The Architectural Modeling and Craftsmanship course, as a key practical teaching module within the Environmental Design major, relies heavily on laboratory resources, including diverse materials, refined tools, and complex workflows. However, traditional laboratory management methods often suffer from information fragmentation, a lack of data, uneven resource utilization, and the absence of process-oriented evaluation, making it difficult to meet the demands of high-quality practical teaching. To address these challenges, this study, based on the Architectural Modeling and Craftsmanship Laboratory of the School of Media and Design at Nantong Institute of Technology, adopts big data technology as the core and conducts systematic research from the perspectives of digital management, refined resource allocation, and precise teaching evaluation. Through methods such as literature review, data collection, cross-institutional visits, and laboratory application experiments, this study develops a laboratory management system and a teaching evaluation system tailored to the characteristics of multiple projects, consumables, equipment, and student works. The proposed systems are then tested and validated in actual laboratory operations. The results indicate that big data not only significantly improves the efficiency of laboratory resource utilization but also comprehensively captures the behavioral characteristics of both students and instructors in the modeling and craftsmanship course, thereby providing a scientific basis for objective, timely, and diversified teaching evaluation. This research offers a replicable model for the digital management of design-related laboratories and holds practical value for enhancing the quality of experimental teaching.