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    新时代教育评价改革的数字正义何以可能

    How Is Digital Justice of Educational Evaluation Reform in the New Era Possible

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【作      者】:

曹渡帆, 朱德全


【关 键 词 】:

数字技术; 教育评价改革; 教育评价; 数字正义


【栏      目】:

理论探讨


【中文摘要】:

教育评价数字化转型使“数字正义”成为新时代教育评价改革的重要组成部分。数字正义是对教育评价数字化转型合法性的判断,意指教育评价在数字化转型过程中秉持了社会正义和道德关怀的立场,从而满足了人们对美好教育需求的一种理想状态,主要表现为分配正义、算法正义与数据正义三种形式。然而,新时代教育评价改革迈向数字正义时,必须警惕教育评价过度计量化、智能评价算法偏误与智能评价数据异化的价值风险,进而规避新时代教育评价改革可能引发的数字非正义现象。为保障新时代教育评价改革数字正义的实现,要建构相应的风险防控策略:一要明确教育评价改革的价值意义,树立“需求本位”的分配原则;二要保障智能评价算法的人机协同,确保“合目的性”的算法运行;三要把握教育评价数据的应用边界,彰显“以人为本”的数据价值。


【英文摘要】:

The Digital transformation of educational evaluation has made "digital justice" an important part of educational evaluation reform in the new era. Digital justice is a judgment on the legitimacy of the digital transformation of educational evaluation, which means that educational evaluation adheres to the position of social justice and moral concern in the process of digital transformation, so as to meet people's demand for a better education in an ideal state, which is mainly manifested in the form of distributive justice, algorithmic justice and data justice. However, when the reform of educational evaluation in the new era moves towards digital justice, it is necessary to be alert to the value risks of excessive quantification of educational evaluation, bias of intelligent evaluation algorithm and alienation of intelligent evaluation data, so as to avoid the digital injustice that may be caused by educational evaluation reform in the new era. In order to guarantee the realization of digital justice of educational evaluation reform in the new era, corresponding risk prevention and control strategies should be constructed. Firstly, the value of educational evaluation reform should be clarified, and the "demand-based" distribution principle should be established. Secondly, it is necessary to ensure the human-machine collaboration of intelligent evaluation algorithm and ensure the "purposeful" operation of the algorithm. Thirdly, the application boundary of educational evaluation data should be grasped to highlight the "people-oriented" value of data.

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