Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework
Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework
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利用数据可视化在先进学习技术的自我调节学习过程中促进情绪调节：概念框架
DOI：
10.1145/3027385.3027440
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Megan J. Price
中科院分区：
文献类型：
--
作者：
R. Azevedo;Garrett C. Millar;M. Taub;Nicholas V. Mudrick;Amanda E. Bradbury;Megan J. Price
情绪在高级学习技术（ALT）的学习和解决问题过程中发挥着关键作用。尽管他们的重要性，相对较少的尝试已经了解学习者的情绪监控和调节，通过使用他们自己（和他人）的认知，情感，元认知，和动机（CAMM）自我调节学习（SRL）过程的数据可视化，以潜在地促进他们的情绪调节（ER）。我们提出了一个基于理论和经验驱动的概念框架，提出使用自己和他人的CAMM SRL多通道数据的可视化，以方便学习者的监测和调节情绪在学习过程中与ALT，解决ER。我们使用一个眼动追踪数据的例子来说明理论假设，ER策略和数据可视化类型之间的映射，可以提高学习者的ER，包括情绪灵活性，情绪适应性和情绪效能等关键过程。我们的结论与未来的方向，导致一个系统的跨学科的研究议程，解决悬而未决的ER相关的问题，通过整合模型，理论，方法和分析技术的认知，学习和情感科学;人机交互（HCI）;数据可视化;大数据;数据挖掘;和SRL。
Emotions play a critical role during learning and problem solving with advanced learning technologies (ALTs). Despite their importance, relatively few attempts have been made to understand learners' emotional monitoring and regulation by using data visualizations of their own (and others') cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation (ER). We present a theoretically based and empirically driven conceptual framework that addresses ER by proposing the use of visualizations of one's own and others' CAMM SRL multichannel data to facilitate learners' monitoring and regulation of emotions during learning with ALTs. We use an example with eye-tracking data to illustrate the mapping between theoretical assumptions, ER strategies, and the types of data visualizations that can enhance learners' ER, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions leading to a systematic interdisciplinary research agenda that addresses outstanding ER-related issues by integrating models, theories, methods, and analytical techniques for the cognitive, learning, and affective sciences; human- computer interaction (HCI); data visualization; big data; data mining; and SRL.
影响因子：
4.6
作者：
Doré BP;Silvers JA;Ochsner KN
通讯作者：
Ochsner KN