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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157035


    Title: 教師教學創新的跨國性比較：三種跨國性大數據分析方法的比較與介紹
    Cross-Country Comparison of Teacher Teaching Innovation: Comparison and Introduction of Three Big Data Analysis
    Authors: 曾明基
    Tseng, Ming-Chi
    Contributors: 教育與心理研究
    Keywords: TALIS 2018;貝氏近似測量恆等;貝氏隨機效果模型;校準;教師教學創新
    TALIS 2018;Bayesian approximate measurement invariance;Bayesian random effect model;calibration;teacher teaching innovation
    Date: 2025-03
    Issue Date: 2025-05-23 09:50:00 (UTC+8)
    Abstract: 本研究透過TALIS 2018國際資料庫進行15個國家在教師教學創新的跨國性比較，並透過貝氏隨機效果模型、貝氏近似測量恆等，以及校準三種不同大數據分析方法，比較教師教學創新的跨國差異。研究發現，過往的精確測量恆等性分析並不適合使用在教師教學創新的跨國比較。而貝氏隨機效果模型、貝氏近似測量恆等及校準方法，可有效估計教師教學創新的跨國差異，值得推廣使用。此外，經由排序比較發現，臺灣教師在教學創新上仍有許多可以進步之處。
    Research Motivation and Objective: The primary objective of this study is clear: To conduct a transnational comparison of teaching innovation among teachers across 15 countries, using the TALIS 2018 international database. This research aims to explore the differences in teaching innovation across these countries. To achieve this, the study employs three advanced big data analysis methods developed in recent years within the field of testing and assessment: Bayesian random effects models (Asparouhov & Muthén, 2016; De Jong et al., 2007; Fox, 2010; Verhagen & Fox, 2012), Bayesian approximate measurement invariance (Muthén & Asparouhov, 2012, 2018), and Calibration (Asparouhov & Muthén, 2014, 2022; Muthén & Asparouhov, 2014, 2018). This research makes a definitive comparison of teaching innovation across countries and introduces these three methodological approaches for empirical researchers to reference in conducting transnational comparisons.
    Relation: 教育與心理研究, 48(1), 37-63
    Data Type: article
    DOI 連結: https://doi.org/10.53106/102498852025034801002
    DOI: 10.53106/102498852025034801002
    Appears in Collections:[教育與心理研究 TSSCI] 期刊論文

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