摘要
通过人工神经网络技术识别优秀博士学位论文作者的共同、典型特征构建群体画像模型,是甄选具有拔尖创新潜质人才、服务有效教与学的新手段。基于1500份毕业博士样本,本研究构建出理工农医和人文社科优秀博士学位论文作者的群体画像,党员身份、父母高学历、高学习投入、高能力素养、学科竞赛获奖或获得综合性奖励是两类群体画像的共有特征,体育锻炼习惯、前置学校双一流、实习实践的经历是人文社科类优秀学位论文作者画像的独有特征,较好的家庭经济水平、高课堂投入、高协作解决问题能力和学习能力则是理工农医类优秀学位论文作者画像的独有特征。基于画像模型的发现,选拔优秀博士生需要认识到党员身份、获奖的信号作用和家庭资本、高学习投入的支持作用,培养中要重视非认知能力与认知能力的统整融合,将课堂开设在广阔的大地上加强实践锻炼。
Constructing a portrait model of excellent doctoral thesis authors through artificial neural networks is a novel approach to identify potential outstanding talents and to serve effective teaching and learning.Based on 1500 samples of graduating PhD students,this study establishes portrait models of outstanding thesis authors in the fields of science,engineering,agriculture and medicine(SEAM),and fields of humanities and social sciences.The common features of the two portrait models are membership of CPC,highly educated parents,highly learning engagement,highly competencies,and winning or receiving comprehensive rewards.Meanwhile,the exclusive features of humanities and social sciences include habits of physical exercise,prior educated in word-class universities,and internship experiences.The exclusive features of SEAM include highly family economic status,highly classroom engagement,strongly collaborative problem-solving,learning skills.Based on the insights derived from the portrait models,to identify the potential outstanding PhD students requires recognizing the signaling effects of CPC membership and rewards,as well as the support effects of family capital and highly learning engagement.In the cultivation process,emphasis should be placed on the integration of non-cognitive and cognitive skills,and the classroom should be set up on broader stages.
作者
黄维海
李树岳
HUANG Weihai;LI Shuyue(College of Public Management,Nanjing Agricultural University,Nanjing 210095)
出处
《教育发展研究》
北大核心
2024年第3期38-45,共8页
Research in Educational Development
基金
国家社会科学基金教育学一般课题“基于新人力资本框架的研究生创新素养评价与培养路径研究”(BIA200173)的部分成果
