Leveraging learning analytics to enhance immersive teacher simulations: Challenges and opportunities

2026-01-13

AI Summary

This study aims to enhance the support provided by immersive teacher simulation training for pre-service teachers’ professional decision-making and reflective practice. By integrating an extended reality (XR) platform with multimodal learning analytics—including verbal discourse, behavioral logs, and eye-tracking data—the research systematically employs multimodal data as a mediating tool to visualize the teaching reasoning process and uncover the cognitive distribution and sequential interaction patterns embedded in instructional discourse. The work advances beyond descriptive observation toward predictive modeling, identifying prototypical patterns of teacher–student interaction. These findings offer both theoretical grounding and empirical evidence for the development of scalable, data-driven next-generation teacher education environments.

Technology Category

• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

This chapter examines how data analytics can be leveraged to enhance immersive teacher simulations, situating this inquiry within the broader learning sciences discourse on embodied cognition, data-informed feedback, and teacher professional learning. It explores both conceptual foundations and empirical cases to illustrate how analytics serve as mediational tools that connect immersive experiences with reflective teaching practice. The chapter unfolds in multiple sections: (1) The Innovation Journey: An Overview of Immersive Teacher Simulations outlines the evolution from traditional simulations to XR-based environments, highlighting the need for professional decision-making under realistic constraints. (2) Innovation in Existing Research and Practice situates teacher analytics within the trajectory from descriptive observation to multimodal and predictive modeling. (3) Study Approach and Design details how multimodal data-discourse, behavior, and gaze-from the TeacherGen@i simulation were collected and organized to reveal cognitive distribution of pedagogical discourse and interaction patterns. (4) Findings present the cognitive distribution of preservice teachers'pedagogical discourse and the sequential interaction patterns that emerge in exchange, illustrating how multimodal analytics make pedagogical reasoning processes visible within immersive simulations. (5) Understanding Innovative Practices in Teacher Education examines teaching analytics to enhance immersive teacher simulation based on the findings of the study. (6) Key Takeaways of the Innovation Journey identifies research challenges and design implications for scalable, analytics-enhanced teacher education. Together, these sections position immersive teacher simulations as a pivotal testbed for aligning learning analytics, professional learning, and next-generation immersive learning environment design.

Problem

• learning analytics
• immersive teacher simulations
• teacher professional learning
• pedagogical reasoning
• multimodal data

Innovation

• learning analytics
• immersive teacher simulations
• multimodal data
• embodied cognition
• XR-based environments