摘要
[目的/意义]考察情报学学生对大数据和人工智能的认识,能够为情报学教育体系调整与改革提供不一样的视角,也可为相关研究补充有价值的建议和意见。[方法/过程]文章以江苏省各高校所培养情报学学生为对象,面向学生自身的特点和需求,设计了包含50个问题的调查问卷,考察了情报学学生对情报学教育体系现状和未来发展趋势的认知。对发回的问卷结果围绕问卷填写者的基本情况、情报学教育课程设置、情报学人才培养目标以及专业技能培养四个方面进行分析。[结果/结论]情报学学生以学习知识和技能为第一要务,并以就业或升学为重要目标。情报学学生对于情报学的认知和理解将很大程度上影响学科未来的发展,其对于新兴技术及这些技术在情报学教育中地位的看法,更值得认真思考和对待。
[Purpose/significance]Investigating the knowledge of information science students on big data and artificial intelligence not only provide a different perspective for the adjustment and reform of the information science education system,but also provide valuable suggestions and opinions for related research.[Method/process]This paper takes information science students that trained in various universities in Jiangsu Province as the research object.We have designed a questionnaire that contains 50 questions facing the characteristics and needs of students,investigating the cognition of information science students on the current situation and future development trends of the information science education system.The results of the questionnaire sent back were analyzed in four aspects:the basic situation of the respondents,the curriculum of information science education,the training target of information science talents and the training of professional skills.[Result/conclusion]We found that information science students take learning knowledge and skills as the first priority and take employment or advancement as an important goal.Information science students’cognition and understanding of information science will greatly affect the future development of the discipline.Their views on emerging technologies and technologies’status in information science education are more worthy to consider and treat seriously.
出处
《情报理论与实践》
CSSCI
北大核心
2021年第2期16-25,共10页
Information Studies:Theory & Application
基金
国家社会科学基金项目“加快构建中国特色哲学社会科学学科体系、学术体系、话语体系”研究专项项目的成果,项目编号:19VXK09。
关键词
情报学
情报工作
情报学教育
情报学教学
情报人才培养
大数据
人工智能
学科建设
问卷调查
information studies
information work
information science education
information science teaching
information personnel training
big data
artificial intelligence
disciplinary construction
questionnaire survey
