It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present 15 case studies that illustrate the potential of the AAA approach for measuring ensgagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.

中文翻译：

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学习过程中的参与度高级，分析性，自动化（AAA）。

众所周知，参与在学习中起着至关重要的作用。不幸的是，缺乏有效和有效的措施阻碍了对参与的研究。我们引入了先进的，分析性的和自动化（AAA）方法来以细粒度的时间分辨率测量参与度。AAA度量方法基于认知和情感的具体理论，这些理论主张思想与行为之间的紧密耦合。它使用机器学习的计算模型从机器可读的行为和生理信号（例如面部表情，眼睛跟踪，点击流数据）以及环境方面自动推断与参与（例如兴趣，流动）相关的心理状态语境。我们目前提供15个案例研究，这些案例说明了AAA方法在数字学习环境中测量参与度的潜力。我们讨论了AAA方法的优点和缺点，认为它有很大的前景来促进参与研究。