随着大数据技术的快速发展,海量、多源、实时的地学数据为地学教育提供了前所未有的教学资源,同时对传统的教学模式提出挑战。通过地学时空尺度特性分析,以DIKW学习模型为指导,以“地震全球分布教学”为典型案例,深入分析大数据驱动的地学实验教学模式。具体教学设计包括以下步骤和层次:数据采集或获取、信息提取、知识总结、规律预测。该过程重视全数据分析而非抽样调查,重视相关分析而非因果,有利于培养学生的批判性思维。在大数据背景下,这一教学模式要求提升教师大数据素养、加强教学资源建设、优化课程设计、重构评价体系等,从而构建一个更加开放、灵活、高效的地学实验教学模式,以适应大数据时代的发展需求。
With the rapid development of big data technology, the volume, multi-source, real-time geological data has provided unprecedented teaching resources for geological education, but also posed challenges to traditional teaching modes. By analyzing the spatial and temporal scale characteristics of geoscientific phenomena, and guided by the DIKW learning model, this paper conducts an in-depth analysis of the geological experimental teaching model driven by big data, taking the teaching of global earthquake distribution as a typical case. The specific teaching design includes the following steps and levels:data collection or acquisition, information extraction, knowledge summarization, and wisdom prediction. This process attaches great importance to full data analysis rather than a random sampling survey, and to correlation analysis rather than causation, which is conducive to cultivating students' critical thinking. This teaching model requires us to enhance the big data capacity of teachers, strengthen the construction of teaching resources, optimize course design, and restructure the evaluation system in the context of big data, to build a more open, flexible and efficient geoscientific experimental teaching model to meet the development pace of the big data era.
2025,46(2): 198-205 收稿日期:2025-1-3
DOI:10.3969/j.issn.1003-3246.2025.02.023
基金项目:中央高校研究生教育教学改革专项(项目编号:300103100130);长安大学中央高校基本科研业务费项目(项目编号:300102273206)
作者简介:张丽敏(1979—),女,助理研究员,现主要从事研究生教育教学管理工作。E-mail:54299884@qq.com
*通讯作者:徐玮(1982—),男,助理研究员,现主要从事研究生教育教学管理。E-mail:xw@chd.edu.cn
参考文献:
邓仲华,李志芳. 科学研究范式的演化——大数据时代的科学研究第四范式[J]. 情报资料工作,2013,(4):19-23.
丁晶,李丽,高也,等. FDSN服务鉴权的设计与实现[J]. 地震地磁观测与研究,2024,45(3):161-167.
顾功叙. 中国地震目录[M]. 北京:科学出版社,1983.
郭华东. 地球大数据科学工程[J]. 中国科学院院刊,2018,33(8):818-824.
侯建民,刘瑞丰,赵京轶,等. 基于WebGIS的地震目录数据发布系统研究[J]. 地震地磁观测与研究,2008,29(2):106-111.
黄炜,吴昀璟,余辉,等. 生成式人工智能技术在实验教学中的应用——以数据科学实验为例[J]. 实验室研究与探索,2024,43(9):122-128.
李德仁,张良培,夏桂松. 遥感大数据自动分析与数据挖掘[J]. 测绘学报,2014,43(12):1 211-1 216.
李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报,2016,45(4):379-384.
徐光宪. 物质结构的层次和尺度[J]. 科技导报,2002,(1):3-6.
翟明国,杨树锋,陈宁华,等. 大数据时代:地质学的挑战与机遇[J]. 中国科学院院刊,2018,33(8):825-831.
张旗,周永章. 大数据助地质腾飞:岩石学报2018第11期大数据专题“序”[J]. 岩石学报,2018,34(11):3 167-3 172.
Ackoff R L. From data to wisdom[J]. Journal of applied systems analysis, 1989, 16: 3-9.
Feng X, Ma J, Zhou Y, et al. Geomorphology and Paleoseismology of the Weinan Fault, Shaanxi, Central China, and the Source of the 1556 Huaxian Earthquake[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(12): e2019JB017848.
Gradstein F M. The geologic time scale 2012[M]. Amsterdam: Elsevier, 2012.
Meng X R, Bradley J, Yavuz B, et al. Mllib: Machine learning in apache spark[J]. Journal of Machine Learning Research, 2016, 17(1): 1 235-1 241.
Peters M A, Jandrić P, Green B J. The DIKW Model in the Age of Artificial Intelligence[J]. Postdigital Science and Education, 2024: 1-10.
Tolle K M, Tansley D S W, Hey A J G. The Fourth Paradigm: Data-Intensive Scientific Discovery[J]. Proceedings of the IEEE, 2011, 99(8): 1 334-1 337.
White T. Hadoop: The definitive guide[M]. Sebastopol: O’Reilly Media, Inc., 2009.
Wognin R, Henri F, Marino O. Data, information, knowledge, wisdom: A revised model for agents-based knowledge management systems[M]. Boston, MA: Springer, 2012: 181-189.