This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map three variables in a three‐dimensional framework: construct, functionality, and automaticity. The 12 characteristics used to construct a profile for ML‐based science assessments for each article were further analyzed by a two‐step cluster analysis. The clusters identified for each variable were summarized into four levels to illustrate the evolution of each. We further conducted cluster analysis to identify four classes of assessment across the three variables. Based on the analysis, we conclude that ML has transformed—but not yet redefined—conventional science assessment practice in terms of fundamental purpose, the nature of the science assessment, and the relevant assessment challenges. Along with the three‐dimensional framework, we propose five anticipated trends for incorporating ML in science assessment practice for future studies: addressing developmental cognition, changing the process of educational decision making, personalized science learning, borrowing 'good' to advance 'good', and integrating knowledge from other disciplines into science assessment.

中文翻译：

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从替代到重新定义：基于机器学习的科学评估框架

这项研究开发了一个框架，用于在科学评估中概念化机器学习（ML）的使用和演变。我们系统地审查了将ML应用到科学评估中的47项研究，并将其分为五类：（a）构造的反应，（b）论文，（c）模拟，（d）教育游戏和（e）跨学科。我们比较了基于ML的评估和常规科学评估，并提取了12个关键特征以在三维框架中映射三个变量：结构，功能和自动化。通过两步聚类分析进一步分析了用于构建每篇文章的基于M​​L的科学评估概况的12个特征。为每个变量确定的聚类归纳为四个级别，以说明每个变量的演变。我们进一步进行了聚类分析，确定了三个变量中的四类评估。根据分析，我们得出结论，机器学习已经转变，但尚未转变重新定义-在基本目的，科学评估的性质以及相关评估挑战方面进行常规科学评估实践。连同三维框架，我们提出了将ML纳入科学评估实践以供未来研究的五个预期趋势：解决发展认知，改变教育决策过程，个性化科学学习，借用“好”来促进“好”，并将其他学科的知识整合到科学评估中。