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

Jun Liu, Meng Sun, Zile Liu and Yanhua Xu

SAGE Open, 2023, vol. 13, issue 4, 21582440231218802

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.

Keywords: instructional innovation capability; Many-Faceted Rasch model; pre-service teachers; teaching; normal university students (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/21582440231218802 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:4:p:21582440231218802

DOI: 10.1177/21582440231218802

Access Statistics for this article

More articles in SAGE Open
Bibliographic data for series maintained by SAGE Publications ().