SAGE Open (Dec 2023)

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

  • Jun Liu,
  • Meng Sun,
  • Zile Liu,
  • Yanhua Xu

DOI
https://doi.org/10.1177/21582440231218802
Journal volume & issue
Vol. 13

Abstract

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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.