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<p>Pre-service science teachers often struggle to conduct critical, practice-oriented reflection due to limited experience and a lack of objective evidence. This study employed a design-based research (DBR) approach involving three iterative cycles of design, implementation, and evaluation to develop and refine a collective reflection model. The model integrated classroom video recordings with intelligent multimodal analysis reports (IMAR) generated by an AI-powered platform. Across successive iterations, the design was enhanced through structured facilitation protocols, comparative analysis of reports, and the involvement of in-service teachers and experts. These refinements progressively transformed the nature of collective reflection: participants’ discourse shifted from subjective impressions and superficial comments to evidence-based, critical dialogues. Analyses of teaching videos and self-efficacy surveys further indicated measurable improvements in instructional practices. The study concludes by presenting a set of design principles for leveraging AI analytics to foster critical, organized, and practice-oriented collective reflection. These principles offer a scalable and practical framework for integrating intelligent teaching analytics into teacher education programs.</p>

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From Video to Analytics: A Design-based Study on Fostering Critical Collective Reflection for Pre-service Science Teachers

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Jimei Li,
  • Dongchen Pan,
  • Ya Zhao,
  • Taotao Long

摘要

Pre-service science teachers often struggle to conduct critical, practice-oriented reflection due to limited experience and a lack of objective evidence. This study employed a design-based research (DBR) approach involving three iterative cycles of design, implementation, and evaluation to develop and refine a collective reflection model. The model integrated classroom video recordings with intelligent multimodal analysis reports (IMAR) generated by an AI-powered platform. Across successive iterations, the design was enhanced through structured facilitation protocols, comparative analysis of reports, and the involvement of in-service teachers and experts. These refinements progressively transformed the nature of collective reflection: participants’ discourse shifted from subjective impressions and superficial comments to evidence-based, critical dialogues. Analyses of teaching videos and self-efficacy surveys further indicated measurable improvements in instructional practices. The study concludes by presenting a set of design principles for leveraging AI analytics to foster critical, organized, and practice-oriented collective reflection. These principles offer a scalable and practical framework for integrating intelligent teaching analytics into teacher education programs.