How peer feedback with regulation scripts contributes to the development of critical thinking in dialogues: strengthening cognitive and affective feedback content

ABSTRACTAlthough peer feedback has been proposed as an instructional strategy for cultivating critical thinking, high-quality peer feedback is difficult to obtain. Regulation scripts are a promising scaffold for this activity. Besides, few previous studies have explored the dynamic relationship between feedback content and critical thinking. Therefore, this study investigated the effects of peer feedback with regulation scripts on students' critical thinking, cognitive and affective feedback content, and how feedback content is related to critical thinking. Thirty-four students were randomly divided into nine groups and assigned to one of two conditions: regulation scripts (experimental groups, EGs) and general scripts (control groups, CGs). EGs' students were instructed to argue about the feedback content with prompting questions, and CGs' students completed the tasks with task-related hints. The results revealed that peer feedback with regulation scripts is effective in promoting critical thinking, suggestive comments on cognitive content and positive comments on affective content. Moreover, the epistemic network analysis results indicated that the EGs showed a stronger connection between suggestive/informative comments and creating for cognitive content, while regarding affective content, they showed stronger connections between various affective comments and higher-order critical thinking. Overall, the study yields new insights into the relationship between feedback content and critical thinking.KEYWORDS: dialogic peer feedbackfeedback contentcritical thinkingregulation scriptsepistemic network analysis AcknowledgmentsWe thank Geoff Whyte, MBA, from Liwen Bianji (Edanz) (www.liwenbianji.cn/), for editing the English text of a draft of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China (grant number 62277006) and Beijing Natural Science Foundation (grant number 9222019).Notes on contributorsJingjing ShaoJingjing Shao School of Educational Technology/Research Center for Knowledge Engineering, Beijing Normal University. Research interests are peer assessment, collaborative argumentation, and regulated learning.Lu ChengLu Cheng Beijing No.4 High School. Research interests are peer assessment.Yansu WangYansu Wang School of Foreign Language Studies, Wenzhou Medical University. Research interests are peer assessment and foreign language teaching.Keru LiKeru Li School of Educational Technology/Research Center for Knowledge Engineering, Beijing Normal University. Research interests are peer assessment.Yanyan LiYanyan Li School of Educational Technology/Research Center for Knowledge Engineering, Beijing Normal University. Her main research interests are learning analytics, computer-supported collaborative learning (CSCL), STEM education, and applications of artificial intelligence in education.