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

• Published in: Jiao yu yu xin li yan jiu Vol. 48; no. 1; pp. 037 - 063
• Main Author: 曾明基(Ming-Chi Tseng)
• Format: Journal Article
• Language: Chinese
• Published: 台灣 國立政治大學教育學院 01.03.2025
• Subjects: Bayesian approximate measurement invariance; Bayesian random effect model; calibration; TALIS 2018; teacher teaching innovation; TSSCI; 教師教學創新; 校準; 貝氏近似測量恆等; 貝氏隨機效果模型; 貝氏近似測量恆等; 教師教學創新; 校準; Bayesian random effect model; TALIS 2018; 貝氏隨機效果模型; Bayesian approximate measurement invariance; teacher teaching innovation; TSSCI; calibration
• ISSN: 1024-9885

本研究透過TALIS2018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 ＜br＞ 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