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| 研究生招考中综合审核何以实现——来自美国的经验 |
| How to Carriy out Holistic Review in Postgraduate Admission: Experience from the United States |
| 投稿时间：2019-04-12 |
| DOI： |
| 中文关键词: 综合审核;研究生招考;美国 |
| 英文关键词: holistic review;postgraduate enrollment;the United States |
| 基金项目:国家自然科学基金2017年度面上项目"博士研究生知识生产的模式与机制研究"（71573010） |
| 作者 | 单位 | | 杨佳乐 | 清华大学 教育研究院, 北京 100084 | | 王传毅 | 清华大学 教育研究院, 北京 100084 |
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| 摘要点击次数: 1481 |
| 全文下载次数: 3351 |
| 中文摘要: |
| 美国在研究生招考中正逐步实施综合审核以全面考察申请者的过去经历、认知水平、非认知能力及发展潜力。其原因在于以学为标的考察方式单一且效果有限，唯分而论扼杀生源多样性且抑制创新力，而综合审核可提高培养效率并降低教育成本。美国高校推进综合审核的举措包括：制定观测维度、建立计分标准、创新对非认知能力的评价和加强对匹配度的考察。我国的研究生招考制度改革在国家层面需加强宏观引导与质量监督，探索"一考多次，申请考核；一考多类，替代选择"的方式；在院校层面试点综合审核，制定观测维度与计分标准，开发非认知能力评价工具，提供材料审核与数据分析的相关培训。 |
| 英文摘要: |
| The United States is stepping up efforts to implement the holistic review in graduate admission and comprehensively examine the applicant's past experience, cognitive level, non-cognitive competency and development potential. The reason lies in the fact that the score-oriented examination is single in existence and its effect is limited. Focusing on scores will only stifle the diversity of student source and suppress their innovation. But the holistic review can improve training efficiency and reduce educational cost. The measures taken by American universities to promote the holistic review include:establishing observation dimensions, building scoring standards, innovating the system for evaluating non-cognitive competency and strengthening the examination of program fit. At the national level, China's postgraduate admission reform needs to strengthen macro guidance and quality supervision and explore the way of "multiple examinations of the same kind and reviewing only when receiving admission applications, and one examination for several disciplines instead of choosing schools". The institutions of higher learning need to pilot the holistic review by establishing observation dimensions and score standard, developing non-cognitive competency evaluation tools, and providing training related to material review and data analysis. |
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