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Pre-Service Teachers’ Instructional Innovation Capabilities: A Many-Faceted Rasch Model Analysis

Author

Listed:
  • Jun Liu
  • Meng Sun
  • Zile Liu
  • Yanhua Xu

Abstract

Innovation capability has become a necessary requirement for qualified teachers in the context of informatization. However, the validity and objectivity of existing assessments are unclear. Therefore, this study selected nine researchers to evaluate the instructional innovation capabilities of 60 pre-service teachers from a Chinese normal university. The Many-Faceted Rasch Model (MFRM) was used to measure and analyze the rater severity, difficulty of evaluation criteria, and instructional innovation capability of pre-service teachers. Combined with qualitative analysis, the results showed that most participants had moderate instructional innovation capability, and only certain individuals demonstrated a high level. In addition, innovation related to teaching content and learning activities was easier than innovation related to instructional and evaluation strategies. These results suggest directions for fostering pre-service teachers’ instructional innovation capabilities.

Suggested Citation

  • Jun Liu & Meng Sun & Zile Liu & Yanhua Xu, 2023. "Pre-Service Teachers’ Instructional Innovation Capabilities: A Many-Faceted Rasch Model Analysis," SAGE Open, , vol. 13(4), pages 21582440231, December.
  • Handle: RePEc:sae:sagope:v:13:y:2023:i:4:p:21582440231218802
    DOI: 10.1177/21582440231218802
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    References listed on IDEAS

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    1. Esther Garzón Artacho & Tomás Sola Martínez & José Luís Ortega Martín & José Antonio Marín Marín & Gerardo Gómez García, 2020. "Teacher Training in Lifelong Learning—The Importance of Digital Competence in the Encouragement of Teaching Innovation," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    2. Margarida Romero & Jean-Nicolas Proulx, 2016. "Conceptions and Instructional Strategies of Pre-Service Teachers towards Digital Game based Learning Integration in the Primary Education Curriculum," International Journal of Digital Literacy and Digital Competence (IJDLDC), IGI Global, vol. 7(2), pages 11-22, April.
    3. Xian Jing & Rongxin Zhu & Jieqiong Lin & Baojun Yu & Mingming Lu, 2022. "Education Sustainability for Intelligent Manufacturing in the Context of the New Generation of Artificial Intelligence," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
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