As cutting-edge technologies, such as machine learning (ML), are increasingly involved in science assessments, it is essential to conceptualize how assessment practices are innovated by technologies. To partially meet this need, this article focuses on ML-based science assessments and elaborates on how ML innovates assessment practices in science education. The article starts with an articulation of the “practice” nature of assessment both of learning and for learning, identifying four essential assessment practices: identifying learning goals, eliciting performance, interpreting observations, and decision-making and action-taking. I then extend a three-dimensional framework for innovative assessments, including construct, functionality, and automaticity, and based on which to conceptualize innovative assessments in three levels: substitute, transform, and redefine. Using the framework, I elaborate on how the 10 articles included in this special issue, Applying Machine Learning in Science Assessment: Opportunity and Challenge, advanced our knowledge of the innovations that ML brought to science assessment practices. I contend that the 10 articles exemplify a great deal of effort to transform the four components of assessment practices: ML allows assessments to target complex, diverse, and structural constructs, and thus better approaching the three-dimensional science learning goals of the Next Generation Science Standards (NGSS Lead States, 2013); ML extends the approaches used to eliciting performance and collecting evidence; ML provides a means to better interpreting observations and using evidence; ML supports immediate and complex decision-making and action-taking. I conclude this article by pushing the field to consider the underlying educational theories that are needed for innovative assessment practices and the necessities of establishing a “romance” between assessment practices and the relevant educational theories, which I contend are the prominent challenges to forward innovative and ML-based assessment practices in science education.

随着机器学习（ML）等尖端技术越来越多地参与科学评估，因此必须概念化技术如何创新评估实践。为了部分满足此需求，本文重点介绍基于ML的科学评估，并阐述ML如何在科学教育中创新评估实践。本文首先阐述了学习评估和学习评估的“实践”性质，确定了四种基本的评估实践：确定学习目标，激发绩效，解释观察结果以及决策和采取行动。然后，我扩展了创新评估的三维框架，包括结构，功能和自动化，并在此基础上从三个层次上对创新评估进行概念化：转换，然后重新定义。使用该框架，我详细介绍了本期特刊中的10篇文章如何将机器学习应用于科学评估：机遇与挑战，提高了我们对ML带给科学评估实践的创新的认识。我认为，这10篇文章体现了转换评估实践的四个组成部分的大量努力：ML使评估针对复杂，多样和结构化的结构，从而更好地实现了下一代科学的三维科学学习目标标准（NGSS领先国家，2013年）；ML扩展了用于引发绩效和收集证据的方法；ML提供了一种更好地解释观察结果和使用证据的方法；ML支持立即而复杂的决策和采取行动。