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作 者:赵以霞 金昆[4] 郑晓欢[4] 孔丽华[3] Zhao Yixia;Jin Kun;Zheng Xiaohuan;Kong Lihua(National Science Library,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100190,China;Computer Network Information Center,Chinese Academy of Sciences,Beijing 100190,China;Chinese Academy of Sciences,Beijing 100864,China)
机构地区:[1]中国科学院文献情报中心,北京100190 [2]中国科学院大学,北京100190 [3]中国科学院计算机网络信息中心,北京100190 [4]中国科学院,北京100864
出 处:《中国科学数据(中英文网络版)》2019年第3期158-166,共9页China Scientific Data
基 金:中国科学院“十三五”信息化建设子课题“服务人才高地建设的智慧终身学习平台”(2017-2018)
摘 要:为更好地面向科研人员提供学习服务,促进科技创新发展,中国科学院继续教育网为科研人员提供在线学习服务,科研人员自主选学课程。2018年1月1日至2018年12月31日,共积累219472条学习数据,学习时长为212382.74小时。初步数据分析结果显示,10–12时和14–18时是学习行为产生的高峰期,但晚上18–24时也有相当数量的学习行为,不可忽视。每次学习时长与性别年龄无显著相关,科研人员最感兴趣的学习内容更多聚焦在人工智能和大数据方面。通过数据集,可以分析不同专业技术职称、不同年龄、不同性别、不同工作年限科研人员的在线学习时间点倾向、学习内容倾向和学习时长倾向,为精准服务科研人员在线学习奠定基础。In order to promote scientific and technological innovation and development,the Continuing Education Network of the Chinese Academy of Sciences(CASMOOC)provides online learning services where researchers choose courses independently.This study examines a time period from January 1,2018 to December 31,2018,through which 219,472 data entries were accumulated,amounting to a total learning time of 21,282.74 hours.It shows that online learning behavior arrives at its peak at 10am to 12am and 14pm to 18pm while we should not ignore that considerable learning behaviors occur at 18pm to 24pm.There is no significant correlation exists among learning duration, gender and age, and artificial intelligence and bigdata are on the highest demand. The data set provides a locus for analyzing the tendencies of researchers’online learning time, content and duration across a variety of professional and technical titles, ages, gendersand working years, which lays a precise foundation for online curriculum design of scientific researchers.
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