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多模态测量增强了对即时反馈的情绪反应的洞察。

Multimodal measurements enhance insights into emotional responses to immediate feedback.

作者信息

Horvers Anne, Molenaar Inge, Van Der West Heleen, Bosse Tibor, Lazonder Ard W

机构信息

Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.

出版信息

Front Psychol. 2024 Feb 1;14:1294386. doi: 10.3389/fpsyg.2023.1294386. eCollection 2023.

Abstract

Adaptive learning technologies often provide students with immediate feedback on task performance. This feedback can elicit various emotional responses, which, in turn, influence learning. Most recent studies capture these emotions by single data streams, contradicting the multi-componential nature of emotion. Therefore, this study investigated 32 university students solving mathematical problems using an adaptive learning technology. Students received immediate feedback on every step in the solution process, after which their physiological, experiential and behavioral responses to this feedback were recorded. Physiological arousal was measured by electrodermal activity, valence was measured by self-reports (experiential), and emotion types were measured by observations of facial expressions (behavioral). Results showed more peaks in electrodermal activity after feedback than was expected based on chance. These responses were comparable in strength after feedback on failure and success. Students' experiential responses conveyed mostly positive valence after feedback on success and mostly negative valence after feedback on failure. Behavioral observations showed more negative than positive emotion types after feedback on failure and more positive than negative emotion types after feedback on success. These results show that physiological arousal is a valuable objective indicator of emotional responses after immediate feedback but should be accompanied by other data streams in order to understand students' emotional responses. Both valence and emotion types can be used for this purpose. These outcomes pave the way for designing adaptive learning technologies that take students' emotions into account.

摘要

自适应学习技术通常会就任务表现向学生提供即时反馈。这种反馈能够引发各种情绪反应,进而影响学习。最近的大多数研究通过单一数据流来捕捉这些情绪,这与情绪的多成分性质相矛盾。因此,本研究调查了32名使用自适应学习技术解决数学问题的大学生。学生在解题过程的每一步都收到即时反馈,之后记录他们对该反馈的生理、体验和行为反应。生理唤醒通过皮肤电活动来测量,效价通过自我报告(体验)来测量,情绪类型通过对面部表情的观察(行为)来测量。结果显示,反馈后皮肤电活动的峰值比基于概率预期的更多。失败和成功反馈后的这些反应强度相当。学生的体验反应在成功反馈后大多传达出积极效价,在失败反馈后大多传达出消极效价。行为观察表明,失败反馈后负面情绪类型多于正面情绪类型,成功反馈后正面情绪类型多于负面情绪类型。这些结果表明,生理唤醒是即时反馈后情绪反应的一个有价值的客观指标,但为了理解学生的情绪反应,应该辅以其他数据流。效价和情绪类型均可用于此目的。这些结果为设计考虑学生情绪的自适应学习技术铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/189b/10867107/40bfbca0c91a/fpsyg-14-1294386-g001.jpg

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