国内物流学科深度学习发展趋势研究  

Research on Topics and Trends of Deep Learning

作　　者：高翔 吴刚[1,2] 李爽爽 谢正忠 GAO Xiang;WU Gang;LI Shuangshuang;XIE Zhengzhong(School of Management,Shanghai University of Engineering Science,Shanghai 201620,China;Institute of Higher Education,Shanghai University of Engineering Science,Shanghai 200336,China)

机构地区：[1]上海工程技术大学管理学院,上海201620 [2]上海工程技术大学继续教育学院,上海200336

出　　处：《物流科技》2022年第2期32-35,共4页Logistics Sci-Tech

基　　金：2020年度上海工程技术大学研究生科研创新项目“大数据环境下在线学习行为分析及学习成绩预测研究”(20KY0312)。

摘　　要：随着物流行业发展越来越信息化、智能化,使得物流学科需要改善人才培养模式以适应其发展。在深化面向以学生核心素养发展的课程教学改革背景下,深度学习成为教育领域的研究热点,为了更好地了解物流学科深度学习的研究现状及发展趋势,研究以CNKI数据库在2010~2020年收录的中文核心期刊和CSSCI来源期刊为研究对象,综合利用CiteSpace软件、Bicomb软件和SPSS软件,对作者、关键词等内容进行分析。研究表明,国内深度学习研究主题主要集中在深度学习相关理论研究、信息环境研究、学习者能力研究以及教学策略研究四个方面。未来物流学科深度学习的发展趋势需结合不同学科深度学习现状并逐步向提升核心素养、发展智慧教育、实现跨学科融合以及实践性教学等方面发展。With the development of the logistics industry becoming more and more informational and intelligent,the logistics discipline needs to improve the talent training model to adapt to its development.In the context of deepening the curriculum and teaching reform for the development of students'core literacy,deep learning has become a research hotspot in the field of education.In order to better understand the research status and development trend of deep learning in logistics,the research is based on the CNKI database in 2010~2020.Chinese core journals and CSSCI source journals are the research objects,using CiteSpace software,Bicomb software and SPSS software to analyze the author,keywords and other content.Research shows that domestic deep learning research topics mainly focus on four aspects:deep learning related theoretical research,information environment research,learner ability research,and teaching strategy research.The future development trend of deep learning in logistics disciplines needs to combine the status quo of deep learning in different disciplines and gradually develop in terms of improving core literacy,developing wisdom education,achieving interdisciplinary integration,and practical teaching.

关 键 词：物流学科 深度学习 研究主题 发展趋势 

分 类 号：G642[文化科学—高等教育学]