The ongoing digital transformation of education necessitates robust tools to measure its scope and impact. However, the field lacks a thoroughly validated instrument to assess digital change in schools, hindering empirical research and evidence-based policy. Addressing this gap, this working paper presents a novel methodological simulation that leverages AI-generated synthetic data to develop and refine an analytical protocol for the Digital Change in Schools Scale (DCSS). The original Digital Transformation Scale was adapted to the Chinese context via AI-assisted translation and expert review. Using the AITurk platform, iterative synthetic datasets were generated (n₁=200, n₂=400, n₃=600) to simulate the entire psychometric workflow. Exploratory Factor Analysis on the initial dataset refined the scale from 17 to 10 items, revealing a three-factor structure (digitization, digitalization, digital transformation). Subsequent Confirmatory Factor Analyses on independent samples confirmed the model's acceptable fit and internal consistency, although the digitization subscale showed reliability concerns. Crucially, this simulation does not constitute validation but serves as a rigorous proof-of-concept and feasibility study. It provides a pre-tested analytical framework and a hypothesized factor structure, offering researchers a efficient and cost-effective pipeline for future empirical validation with human subjects, thereby accelerating research into digital change in educational contexts。
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- Kwok Kuen Tsang (Autor:in), 2025, Leveraging AI-Generated Data for Factor Structure Simulation and Analytical Protocol Development of the Digital Change in Schools Scale (DCSS), München, GRIN Verlag, https://www.hausarbeiten.de/document/1617328
