Lobczowski Nikki G
Learning, Research, and Development Center, University of Pittsburgh, Pittsburgh, PA, United States.
Front Psychol. 2022 Apr 25;13:846811. doi: 10.3389/fpsyg.2022.846811. eCollection 2022.
Despite recent increases in research on emotions and regulation in collaborative learning, measuring both constructs remains challenging and often lacks structure. Researchers need a systematic method to measure both the formation of emotions and subsequent regulation in collaborative learning environments. Drawing from the Formation and Regulation of Emotions in Collaborative Learning (FRECL) model, I introduce a new observational coding procedure that provides comprehensive guidelines for coding these phenomena. The FRECL coding procedure has been implemented successfully in other studies and is described here in detail. Specifically, I detail the ideal situations for using the procedure, discuss background information and present a codebook and empirical examples for each stage of the FRECL model, and provide additional considerations that allow researchers flexibility based on their own experiences and preferences. This procedure extends past research by providing an accessible observational protocol that is both systematic and comprehensive. The FRECL coding procedure can benefit future research by providing more organized consistency to the measurement of collaborative emotions and regulation.
尽管最近关于协作学习中情绪与调节的研究有所增加，但对这两个概念的测量仍然具有挑战性，而且往往缺乏系统性。研究人员需要一种系统的方法来测量协作学习环境中情绪的形成以及随后的调节。借鉴协作学习中情绪的形成与调节（FRECL）模型，我引入了一种新的观察编码程序，该程序为编码这些现象提供了全面的指导方针。FRECL编码程序已在其他研究中成功实施，本文将详细介绍。具体而言，我详细说明了使用该程序的理想情况，讨论了背景信息，并为FRECL模型的每个阶段提供了编码手册和实证示例，还提供了其他考虑因素，使研究人员能够根据自己的经验和偏好灵活运用。该程序通过提供一个既系统又全面的可访问观察协议，扩展了以往的研究。FRECL编码程序可以为协作情绪和调节的测量提供更有组织的一致性，从而使未来的研究受益。
