Identification and Cultivation on Outstanding PhD Students Based on Portrait Model of Excellent Thesis Authors
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.
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