Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning.
Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning.
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DOI：
10.1080/00461520.2017.1281747
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发表时间：
2017
影响因子：
8.8
通讯作者：
Duckworth A
中科院分区：
文献类型：
--
作者：
D'Mello S;Dieterle E;Duckworth A
人们普遍认为，参与在学习中起着至关重要的作用。不幸的是，由于缺乏有效和有效的措施，对敬业度的研究一直受到阻碍。我们介绍了先进的、分析的和自动化的（AAA）方法来测量细粒度时间分辨率的参与度。AAA测量方法以认知和情感的具身理论为基础，主张思想和行动之间的紧密耦合。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（如面部表情、眼球追踪、点击流数据）以及环境背景的各个方面，自动推断与参与相关的心理状态（如兴趣、心流）。我们提出了15个案例研究，说明了AAA方法在数字学习环境中衡量参与度的潜力。我们讨论了AAA方法的优势和劣势，并得出结论，它在促进用户粘性研究方面具有重大前景。
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 present15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.
DOI：
10.1007/978-1-4419-9625-1_5
发表时间：
2011-01-01
期刊：
NEW PERSPECTIVES ON AFFECT AND LEARNING TECHNOLOGIES
影响因子：
--
作者：
Afzal, Shazia;Robinson, Peter
通讯作者：
Robinson, Peter
DOI：
10.1016/j.cub.2014.02.009
发表时间：
2014-03-31
期刊：
Current biology : CB
影响因子：
--
作者：
Bartlett MS;Littlewort GC;Frank MG;Lee K
通讯作者：
Lee K
影响因子：
8.8
作者：
Azevedo, Roger
通讯作者：
Azevedo, Roger